With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. Keywords: Real-t. (shrink)

The rapid evolution of cyber threats has made traditional cybersecurity methods increasingly inadequate. Artificial Intelligence (AI) has emerged as a transformative technology in the field of cybersecurity, offering enhanced capabilities for detecting and responding to cyber threats in real time. This paper explores the role of AI in revolutionizing cybersecurity, focusing on its applications in threat detection, anomaly detection, and automated response systems. Through the use of machine learning algorithms, AI can analyze vast amounts of (...) data, identify patterns, and predict potential threats, making it an essential tool in modern cybersecurity strategies. This paper also highlights the challenges and ethical considerations associated with implementing AI in cybersecurity. (shrink)

The increasing sophistication of cyber threats necessitates innovative and proactive cybersecurity measures. This paper explores the latest trends in cybersecurity, focusing on the role of Artificial Intelligence (AI), Zero Trust security, and blockchain technology. A review of the literature highlights significant advancements and persistent challenges, including the security of Internet of Things (IoT) ecosystems and human-centric vulnerabilities. Experiments were conducted to evaluate the efficacy of machine learning-based intrusion detection systems and Zero Trust implementation in a simulated (...) environment. Results demonstrate improved detection rates and reduced insider threat risks. This paper concludes by emphasizing future directions, such as quantum-resistant cryptography and collaborative cybersecurity frameworks, to secure the digital future. (shrink)

The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus (...) on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy. (shrink)

The proliferation of Internet of Things (IoT) devices has introduced a range of opportunities for enhanced connectivity, automation, and efficiency. However, the vast array of interconnected devices has also raised concerns regarding cybersecurity, particularly due to the limited resources and diverse nature of IoT devices. Intrusion detection systems (IDS) have emerged as critical tools for identifying and mitigating security threats. This paper proposes a Multi-Layer Intrusion Detection Framework for IoT systems, leveraging Ensemble Machine Learning (EML) techniques to (...) improve the accuracy and effectiveness of detecting intrusions. The proposed framework integrates multiple layers of defense, including data preprocessing, feature extraction, classification, and post-processing, to provide a robust solution to IoT security challenges. This research explores the application of EML methods such as Random Forests, AdaBoost, and Gradient Boosting, combining them into a unified model to detect and mitigate various types of attacks in IoT environments. Experimental results demonstrate the superiority of the proposed framework in terms of detection accuracy, false positive rate, and computational efficiency. (shrink)

With the rapid evolution of information technology, malware has become an advanced cybersecurity threat, targeting computer systems, smart devices, and large-scale networks in real time. Traditional detection methods often fail to recognize emerging malware variants due to limitations in accuracy, adaptability, and response time. This paper presents a comprehensive review of machine learning algorithms for real-time malware detection, categorizing existing approaches based on their methodologies and effectiveness. The study examines recent advancements and evaluates the performance of various (...) machine learning techniques in detecting malware with minimal false positives and improved scalability. Additionally, key challenges, such as adversarial attacks, computational overhead, and real-time processing constraints, are discussed, along with potential solutions to enhance detection capabilities. An empirical evaluation is conducted to assess the effectiveness of different machine learning models, providing insights for future research in real-time malware detection. -/- . (shrink)

This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data (...) integrity, behavioral analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats. (shrink)

Artificial intelligence refers to the idea of programming computers to have human-like intelligence and the ability to imitate human behaviour. Machines show characteristics correlated with the human mind, such as learning and problem-solving. The current security systems are slow and insufficient. Artificial intelligence may aid in the improvement of these factors, as well as the detection rate of intrusion detection and prevention systems (IDPS).With the successful use of AI, the system would be more efficient and fast but this also (...) brings some risks and concerns. Therefore, it is equally important to manage both risks as well as concerns. Artificial intelligence improves the overall performance of security systems. Our research paper will help you know the various challenges, risks, and ways to resolve the issues and how new technologies can help fasten cybersecurity and make it more secure. It also informs about the current works going on in the world and what are different companies offering as help. (shrink)

The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong (...) desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three diferent aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments (...) with the goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Abstract: As the frequency and sophistication of cyberattacks continue to rise, organizations face increasing challenges in safeguarding their digital infrastructures. Traditional cybersecurity measures often struggle to keep pace with rapidly evolving threats, creating a pressing need for more adaptive and proactive solutions. Artificial Intelligence (AI) has emerged as a transformative force in this domain, offering enhanced capabilities for detecting, analyzing, and preventing cyberattacks in real- time. This paper explores the pivotal role of AI in strengthening cybersecurity defenses by (...) leveraging machine learning algorithms, predictive analytics, and automation to anticipate and mitigate potential threats before they manifest. Furthermore, it examines AI's ability to evolve with emerging attack vectors, providing a dynamic response to an ever-changing threat landscape. The paper also addresses the limitations and ethical considerations surrounding AI-driven cybersecurity, advocating for a balanced approach to its deployment. Through this exploration, the research underscores how AI is redefining the future of cyber defense by shifting the focus from reactive to proactive strategies. (shrink)

: In the digital age, cybersecurity and network engineering are two integral disciplines that must operate in unison to safeguard information and ensure the resilience of modern systems. While network engineering focuses on designing, implementing, and maintaining infrastructure, cybersecurity emphasizes protecting this infrastructure and its data from threats. This research explores the intricate relationship between these domains, advocating for a unified approach to achieve optimal protection in an increasingly interconnected world. The study highlights the evolving threat landscape characterized (...) by sophisticated cyberattacks, including ransomware, distributed denial-of-service (DDoS) attacks, and advanced persistent threats (APTs). Traditional security measures often fail to address the dynamic nature of these threats, necessitating a deeper integration of cybersecurity principles into network design. By embedding security at every layer of the network architecture, organizations can create systems that are not only efficient but also inherently resilient to attacks. Central to this paper is the concept of security-by-design, a methodology that integrates cybersecurity considerations into the initial stages of network engineering. This approach ensures that vulnerabilities are minimized from the outset rather than being addressed reactively. Techniques such as zero-trust architecture, micro-segmentation, and software-defined networking (SDN) are discussed as pivotal strategies for achieving a secure network environment. Additionally, the role of artificial intelligence (AI) and machine learning (ML) in detecting anomalies and predicting potential breaches is examined, underscoring the importance of innovation in maintaining robust cybersecurity defenses. The research also explores the human factor, emphasizing the need for cross-disciplinary training that equips network engineers with cybersecurity expertise and vice versa. Collaborative frameworks, such as DevSecOps, are presented as effective means of fostering communication and cooperation between teams. These frameworks not only streamline operations but also enhance the overall security posture of organizations. Case studies from sectors such as healthcare, finance, and critical infrastructure provide practical insights into the successful integration of cybersecurity and network engineering. These examples illustrate how proactive measures have mitigated risks, minimized downtime, and ensured business continuity. However, the study also identifies challenges, including the high cost of implementation, skill shortages, and resistance to change within organizations. This research concludes that bridging the gap between cybersecurity and network engineering is no longer optional but a necessity in an era of relentless cyber threats. By adopting a holistic approach that prioritizes collaboration, innovation, and adaptability, organizations can build networks that are both secure and efficient. Future work should focus on developing standardized protocols and frameworks that facilitate seamless integration and address emerging challenges. This researchcontributes to the broader discourse on cybersecurity and network engineering by providing actionable insights and advocating for a paradigm shift that aligns these disciplines for optimal protection. The findings serve as a call to action for stakeholders to embrace a collaborative mindset, ensuring that the digital infrastructure of tomorrow is resilient, adaptive, and secure. (shrink)

Phishing, a prevalent form of social engineering attack, continues to threaten enterprises by exploiting human vulnerabilities and targeting sensitive information. With the increasing sophistication of phishing schemes, traditional detection methods often fall short in identifying and mitigating these threats. As attackers employ advanced techniques, such as highly personalized spear-phishing emails and malicious links, enterprises require innovative solutions to safeguard their digital ecosystems. This research explores the application of artificial intelligence (AI) in enhancing phishing detection and response, with a specific focus (...) on addressing advanced social engineering tactics. AIpowered phishing detection systems utilize machine learning (ML) and natural language processing (NLP) to analyze communication patterns, detect anomalies, and identify deceptive content in real-time. Unlike conventional rule-based systems, AI approaches adapt dynamically to evolving attack vectors, offering a robust defense against both known and emerging threats. These systems can scrutinize email headers, content, URLs, and attachments to uncover subtle indicators of malicious intent. By leveraging historical datasets and predictive analytics, AI models identify patterns associated with phishing, such as unusual sender behavior, suspicious linguistic cues, and mismatched domain information. This study examines the critical components of AI-driven phishing detection, including feature extraction, algorithm selection, and system deployment within enterprise environments. It highlights the role of NLP in understanding the contextual and linguistic subtleties of phishing messages and how advanced ML models, such as decision trees, support vector machines, and deep neural networks, enhance detection accuracy. -/- Additionally, the research delves into the challenges of implementing AI in phishing detection, such as ensuring data privacy, mitigating adversarial attacks on AI models, and fostering trust through explainable AI. Real-world case studies are analyzed to demonstrate the practical effectiveness of AI-powered phishing detection systems. These examples showcase significant reductions in phishing incidents, improved detection times, and the ability to thwart highly targeted attacks, such as business email compromise (BEC) schemes. The study also emphasizes the importance of integrating AI solutions with existing cybersecurity frameworks and highlights the role of human oversight in complementing AI capabilities. While AI offers promising advancements, its success depends on robust training datasets, continuous model optimization, and alignment with enterprise security goals. The paper concludes by presenting best practices for deploying AI-based phishing detection, including regular model updates, employee awareness programs, and multi-layered security strategies. By leveraging AI, enterprises can proactively defend against advanced social engineering attacks, safeguarding their systems, data, and stakeholders from evolving cyber threats. This research contributes to the growing field of AI applications in cybersecurity, offering actionable insights for organizations seeking to enhance their resilience against sophisticated phishing campaigns. (shrink)

The human factor in information systems is a large vulnerability when implementing cybersecurity, and many approaches, including technical and policy driven solutions, seek to mitigate this vulnerability. Decisions to apply technical or policy solutions must consider how an individual’s values and moral stance influence their responses to these implementations. Our research aims to evaluate how individuals prioritise different ethical principles when making cybersecurity sensitive decisions and how much perceived choice they have when doing so. Further, we sought to (...) use participants’ responses to cybersecurity scenarios to create profiles that describe their values and individual factors including personality. Participants (n = 193) in our study responded to five different ethically sensitive cybersecurity scenarios in random order, selecting their action in that scenario and rating and ranking of the ethical principles (i.e., Beneficence, Non-Maleficence, Justice, Autonomy, Explicability) behind that action. Using participants’ demographics, personality, values, and cyber hygiene practices, we created profiles using machine learning to predict participants’ choices and the principle of most importance to them across scenarios. Further, we found that, generalising, for our participants Autonomy was the most important ethical principle in our scenarios, followed by Justice. Our study also suggests that participants felt they had some agency in their decision making and they were able to weigh up different ethical principles. (shrink)

The contribution of this paper is a comprehensive cybersecurity framework to secure cloud hosted Guidewire implementations by addressing critical security challenges such as threat detection, incident response, compliance, and system performance. Based on advanced technologies like machine learning, behavioral analytics and auto patching, the framework detects and mitigates known and unknown threats, incidentally zero-day exploit. The system does this through micro segmenting, behavioral anomaly detection, and automated patch orchestration in a way that does not render the system (...) unperforming. Key performance metrics of threat detection time (less than 3 seconds), incident response (patching within 5 minutes) and system availability (99.95% uptime) are all tested as the framework outperforms in all of these. Additionally, the framework ascertains industry standards such as GDPR and HIPAA via automated audit trails and ongoing monitoring. Compared to the currently deployed cloud security, the proposed approach, with a special focus on more secure the Guidewire application deployed in the cloud, offers a multi layered security approach to the modules specific to the Guidewire. Though powerful, the framework also suffers from complexity in its deployment, overhead in terms of performance and a reliance on cloud provider-specific features. The scalability will need to improve, and the product will need to support multiple clouds and integrate advanced technology such as quantum computing and AI-powered threat intelligence. This work sets a solid basis for the protection of cloud-based Guidewire systems against future cyber threats while maintaining operational continuity and system compliance. (shrink)

The philosophical debate around the impact of machine learning in science is often framed in terms of a choice between AI and classical methods as mutually exclusive alternatives involving difficult epistemological trade-offs. A common worry regarding machine learning methods specifically is that they lead to opaque models that make predictions but do not lead to explanation or understanding. Focusing on the field of molecular biology, I argue that in practice machine learning is often used (...) with explanatory aims. More specifically, I argue that machine learning can be tightly integrated with other, more traditional, research methods and in a clear sense can contribute to insight into the causal processes underlying phenomena of interest to biologists. One could even say that machine learning is not the end of theory in important areas of biology, as has been argued, but rather a new beginning. I support these claims with a detailed discussion of a case study involving gene regulation by microRNAs. (shrink)

The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and microsegmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (...) (AI), machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape. (shrink)

The increasing complexity and sophistication of cyber threats have rendered traditional perimeter-based security models insufficient for protecting modern digital infrastructures. Zero Trust Architecture (ZTA) has emerged as a transformative cybersecurity framework that operates on the principle of "never trust, always verify." Unlike conventional security models that rely on implicit trust, ZTA enforces strict identity verification, continuous monitoring, least-privilege access, and microsegmentation to mitigate risks associated with unauthorized access and lateral movement of threats. By integrating technologies such as artificial intelligence (...) (AI), machine learning (ML), and behavioral analytics, Zero Trust enhances threat detection, reduces attack surfaces, and ensures a proactive security posture across cloud, on-premises, and hybrid environments. This paper explores the core principles, implementation strategies, and benefits of Zero Trust Architecture, along with its challenges and future trends in cybersecurity. Furthermore, it highlights real-world applications and case studies that demonstrate the effectiveness of ZTA in protecting critical assets against advanced cyber threats. By adopting a Zero Trust approach, organizations can significantly improve their resilience to cyberattacks and ensure robust data protection in an evolving threat landscape. (shrink)

As technology rapidly advances, the landscape of cybersecurity faces increasingly complex and evolving threats. This paper explores the future of cybersecurity, focusing on emerging threats and the corresponding mitigation strategies. Key threats include AI-driven attacks, ransomware evolution, quantum computing vulnerabilities, IoT security risks, cloud security challenges, and supply chain attacks. These threats have the potential to disrupt organizations and compromise sensitive data on an unprecedented scale. To address these challenges, the paper outlines effective mitigation strategies such as leveraging (...) AI and machine learning for defense, implementing Zero Trust architecture, adopting quantum-resistant cryptography, strengthening IoT security, improving cloud security practices, and managing third-party risks. A proactive, multi-layered approach to cybersecurity is essential to safeguard against the growing sophistication of cyber threats. This paper highlights the importance of continuous innovation and collaboration across industries to ensure a secure digital future. (shrink)

Machine learning (ML) has emerged as a powerful tool for transforming data-driven decision-making across various industries. By leveraging large volumes of data and advanced algorithms, machine learning models can uncover insights, make predictions, and enable businesses to make more informed decisions. This paper explores how machine learning is revolutionizing decision-making processes, enhancing efficiency, accuracy, and predictive capabilities. It also examines the key challenges, opportunities, and future directions for the integration of machine learning (...) into decision-making frameworks. (shrink)

In the rapidly evolving digital landscape, cyber threats are becoming increasingly sophisticated, making traditional security measures inadequate. Advanced Threat Detection (ATD) leveraging Artificial Intelligence (AI)-driven anomaly detection systems offers a proactive approach to identifying and mitigating cyber threats in real time. This paper explores the integration of AI, particularly machine learning (ML) and deep learning (DL) techniques, in anomaly detection to enhance cybersecurity defenses. By analyzing vast amounts of network traffic, user behavior, and system logs, AI-driven (...) models can identify deviations from normal patterns, enabling early threat detection and prevention. These systems excel in detecting zero-day attacks, insider threats, and advanced persistent threats (APTs), which often bypass conventional rule-based security mechanisms. Additionally, we discuss the challenges of AI-based anomaly detection, including false positives, model interpretability, and adversarial attacks. The findings emphasize the need for continuous learning and adaptive security frameworks to ensure robust cyber threat detection. The study concludes that AIdriven anomaly detection significantly enhances threat intelligence and response capabilities, making it a vital component of modern cybersecurity strategies. (shrink)

Integrating machine learning (ML) into network management and orchestration has revolutionized edgebased networking paradigms, where real-time decision-making is critical. Traditional network management approaches often struggle with edge environments' dynamic and resource-constrained nature. By leveraging ML algorithms, networks at the edge can achieve enhanced efficiency, automation, and adaptability in areas such as traffic prediction, resource allocation, and anomaly detection (Wang et al., 2021). Supervised and unsupervised learning techniques facilitate proactive network optimization, reducing latency and improving quality of service (...) (QoS) (Li & Zhang, 2020). Furthermore, reinforcement learning (RL) models enable autonomous network orchestration, allowing edge devices to adapt intelligently to fluctuating workloads and environmental conditions (Patel et al., 2019). However, challenges such as data privacy, computational overhead, and model interpretability remain key concerns in deploying ML-driven network orchestration at the edge (Chen et al., 2022). This paper explores state-of-the-art advancements, key challenges, and future research directions in ML-based edge network management, highlighting its potential to drive the next generation of intelligent, self-optimizing networks. (shrink)

Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal (...) schema transformations. Experiments on supermarket sales datasets show substantial improvements in accuracy, scalability, and processing time compared to traditional approaches. The results emphasize the potential of incorporating ML into database management practices to boost operational efficiency and support better decision-making. (shrink)

In the evolving landscape of data security, traditional access control mechanisms often fall short in addressing dynamic and context-specific requirements. As data breaches become more sophisticated, organizations require more adaptive and intelligent access control strategies. This paper explores the integration of Artificial Intelligence (AI) into Just-in-Time (JIT) access control models to enhance database security. By leveraging AI, we aim to create adaptive, context-aware permission systems that grant access precisely when needed, reducing the attack surface and mitigating unauthorized access risks. We (...) begin by analyzing traditional access control methods such as Role-Based Access Control (RBAC), Mandatory Access Control (MAC), and Discretionary Access Control (DAC), highlighting their limitations in handling real-time access control scenarios. The paper then explores the benefits of JIT access control, emphasizing how it minimizes over-privileged accounts and reduces security vulnerabilities. Furthermore, we investigate the role of AI in cybersecurity, particularly in real-time monitoring, anomaly detection, and decision-making processes for access control. To achieve a more intelligent access control framework, we propose an AI-driven JIT access model that incorporates machine learning algorithms, user behavior analytics, and contextual evaluation to determine access permissions dynamically. The proposed model is evaluated through simulations and case studies to measure its effectiveness in preventing unauthorized access, reducing the attack surface, and enhancing overall database security. The results demonstrate that our AI-driven approach significantly improves access accuracy, minimizes false positives and negatives, and optimizes response times compared to traditional methods. Through comprehensive analysis, this research provides a roadmap for organizations looking to implement AIenhanced JIT access control mechanisms. By dynamically granting access based on real-time behavioral and contextual assessments, organizations can significantly improve database security, reduce administrative overhead, and mitigate insider threats. Future research directions include further refinement of AI models, integration with multi-factor authentication mechanisms, and testing across diverse real-world scenarios to enhance security effectiveness. (shrink)

Issues of reliability are claiming center-stage in the epistemology of machine learning. This paper unifies different branches in the literature and points to promising research directions, whilst also providing an accessible introduction to key concepts in statistics and machine learning – as far as they are concerned with reliability.

Autonomous systems, powered by machine learning (ML), have the potential to revolutionize various industries, including transportation, healthcare, and robotics. However, the integration of machine learning in autonomous systems raises significant challenges related to safety, ethics, and regulatory compliance. Ensuring the reliability and trustworthiness of these systems is crucial, especially when they operate in environments with high risks, such as self-driving cars or medical robots. This paper explores the intersection of machine learning and autonomous systems, (...) focusing on the challenges of ensuring safety, mitigating ethical concerns, and navigating evolving regulatory frameworks. We discuss key strategies for improving the transparency, fairness, and accountability of autonomous systems, as well as the role of machine learning in enabling safe decision-making. Additionally, we propose a roadmap for the future development of autonomous systems that incorporates robust safety measures, ethical guidelines, and regulatory compliance. (shrink)

: As organizations continue to adopt cloud computing for its numerous advantages—such as scalability, cost-efficiency, and flexibility—the need to secure digital assets within the cloud has never been more critical. Cloud security faces unique challenges due to the distributed and multi-tenant nature of cloud services. Cyber-attacks, data breaches, and misconfigurations are among the prominent risks that threaten the integrity, confidentiality, and availability of cloud-based data and applications. In this paper, we explore CloudGuard, an advanced security framework designed to protect organizations' (...) digital assets in the cloud. This fortress security approach focuses on implementing multiple layers of protection through cutting-edge technologies, such as encryption, identity and access management (IAM), threat intelligence, and security automation. We also highlight the integration of AI and Machine Learning to improve threat detection and response times, and blockchain's potential in ensuring data integrity and privacy. Through a comprehensive analysis, this paper outlines practical strategies for organizations to adopt CloudGuard and strengthen their security posture against evolving cyber threats in the digital world. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to (...) investigate the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

In the current digital environment, cyberattacks continue to pose a serious risk and difficulty. Internet of Things (IoT) devices are becoming more and more vulnerable due to security problems like ransomware, malware, poor encryption, and IoT botnets. These flaws may result in ransom demands, data tampering, illegal access, and system risks. Creating strong cybersecurity procedures for contemporary smart environments is essential to resolving these problems. This strategy uses proactive network traffic monitoring to spot any dangers in the Internet of (...) Things ecosystem. Our approach is to improve Smart Environments' security and awareness of potential threats. Two IoT gateways were used to examine the effectiveness and performance of a deep neural network (DNN) model. The results were promising: the model caused an average increase of less than 30 kb/s in network bandwidth and a mere 2% rise in CPU usage. Additionally, memory and power consumption were minimal, with 0.42 GB and 0.2 GB of memory usage for NVIDIA Jetson and Raspberry Pi devices, respectively, and an average 13.5% increase in power consumption per device. The machine learning models achieved nearly 93% detection accuracy and a 92% F1 score on the datasets used. Our framework demonstrates an effective and efficient method for detecting malware and attacks in Smart Environments. (shrink)

The increasing reliance on digital technologies within the power sector has introduced considerable cybersecurity risks, especially from malware and trojans. These threats can disrupt essential operations, manipulate grid functions, and compromise the integrity of energy systems, thereby endangering both economic stability and national security. This research aims to create a detection framework tailored to the specific challenges of the power sector. The proposed framework utilizes advanced methods such as behaviour based anomaly detection, machine learning algorithms, and both (...) static and dynamic analysis of software. By examining distinct patterns and signatures associated with malware and trojans targeting power sector software, this study seeks to enhance early detection capabilities and response strategies. Real-world case studies and simulations will be employed to evaluate the effectiveness of these detection techniques, highlighting the necessity of robust and adaptable security measures to protect critical energy infrastructure. (shrink)

As our epistemic ambitions grow, the common and scientific endeavours are becoming increasingly dependent on Machine Learning (ML). The field rests on a single experimental paradigm, which consists of splitting the available data into a training and testing set and using the latter to measure how well the trained ML model generalises to unseen samples. If the model reaches acceptable accuracy, an a posteriori contract comes into effect between humans and the model, supposedly allowing its deployment to target (...) environments. Yet the latter part of the contract depends on human inductive predictions or generalisations, which infer a uniformity between the trained ML model and the targets. The paper asks how we justify the contract between human and machine learning. It is argued that the justification becomes a pressing issue when we use ML to reach ‘elsewheres’ in space and time or deploy ML models in non-benign environments. The paper argues that the only viable version of the contract can be based on optimality (instead of on reliability which cannot be justified without circularity) and aligns this position with Schurz’s optimality justification. It is shown that when dealing with inaccessible/unstable ground-truths (‘elsewheres’ and non-benign targets), the optimality justification undergoes a slight change, which should reflect critically on our epistemic ambitions. Therefore, the study of ML robustness should involve not only heuristics that lead to acceptable accuracies on testing sets. The justification of human inductive predictions or generalisations about the uniformity between ML models and targets should be included as well. Without it, the assumptions about inductive risk minimisation in ML are not addressed in full. (shrink)

Prediction-based decisions, which are often made by utilizing the tools of machine learning, influence nearly all facets of modern life. Ethical concerns about this widespread practice have given rise to the field of fair machine learning and a number of fairness measures, mathematically precise definitions of fairness that purport to determine whether a given prediction-based decision system is fair. Following Reuben Binns (2017), we take ‘fairness’ in this context to be a placeholder for a variety of (...) normative egalitarian considerations. We explore a few fairness measures to suss out their egalitarian roots and evaluate them, both as formalizations of egalitarian ideas and as assertions of what fairness demands of predictive systems. We pay special attention to a recent and popular fairness measure, counterfactual fairness, which holds that a prediction about an individual is fair if it is the same in the actual world and any counterfactual world where the individual belongs to a different demographic group (cf. Kusner et al. (2018)). (shrink)

Integrating medical imaging with computing technologies, such as Artificial Intelligence (AI) and its subsets: Machine learning (ML) and Deep Learning (DL) has advanced into an essential facet of present-day medicine, signaling a pivotal role in diagnostic decision-making and treatment plans (Huang et al., 2023). The significance of medical imaging is escalated by its sustained growth within the realm of modern healthcare (Varoquaux and Cheplygina, 2022). Nevertheless, the ever-increasing volume of medical images compared to the availability of imaging (...) experts. Biomedical experts and radiologists have resulted in a widening disparity, causing an excess and overwhelming workload on these healthcare professionals (Chen et al., 2021). Several studies indicate that the present-day biomedical radiologist is now saddled with the daunting task of interpreting an image almost every 10 seconds to keep pace with the burgeoning clinical demands (McDonald et al., 2015; Hosny et al., 2018; Lantsman et al., 2022). This cognitive drain has invariably led to inevitable consequences such as delays in diagnosis and an amplified risk of diagnostic errors – thus, the biomedical imaging aspect is in dire need of methods that would aid accurate diagnostics and analytics for improved decision making. In this review, the importance of AI-related technologies such as ML and/or DL methods are reviewed in relation to the processing of medical or biomedical images along with their potentials, challenges, and possible suggestions for future studies in the health landscape. The focus will be on machine learning methods associated with the medical field of image classification. (shrink)

This study employs machine learning to predict diabetes using a Kaggle dataset with 13 features. Our three-layer model achieves an accuracy of 98.73% and an average error of 0.01%. Feature analysis identifies Age, Gender, Polyuria, Polydipsia, Visual blurring, sudden weight loss, partial paresis, delayed healing, irritability, Muscle stiffness, Alopecia, Genital thrush, Weakness, and Obesity as influential predictors. These findings have clinical significance for early diabetes risk assessment. While our research addresses gaps in the field, further work is needed (...) to enhance model generalizability. (shrink)

Current methods of operationalizing concepts of misinformation in machine learning are often problematic given idiosyncrasies in their success conditions compared to other models employed in the natural and social sciences. The intrinsic value-ladenness of misinformation and the dynamic relationship between citizens' and social scientists' concepts of misinformation jointly suggest that both the construct legitimacy and the construct validity of these models needs to be assessed via more democratic criteria than has previously been recognized.

The proliferation of the Internet of Things (IoT) has transformed various industries by enabling smart environments and improving operational efficiencies. However, this expansion has introduced numerous security vulnerabilities, making IoT systems prime targets for cyberattacks. This paper proposes a machine learning-based intrusion detection framework tailored to the unique characteristics of IoT environments. The framework leverages feature engineering, advanced machine learning algorithms, and real-time anomaly detection to identify and mitigate security threats effectively. Experimental results demonstrate the efficacy (...) of the proposed approach in detecting diverse IoT-specific attacks, including denial-of-service (DoS), man-in-the-middle (MITM), and malware-based attacks. The Internet of Things (IoT) has revolutionized modern living by interconnecting devices and enabling seamless communication. However, the increasing reliance on IoT systems has exposed significant vulnerabilities, making them a prime target for security attacks. This paper proposes a machine learning-based intrusion detection framework to detect and mitigate security attacks in IoT environments. The framework integrates diverse machine learning algorithms to identify abnormal behavior and potential threats. Through comprehensive experiments and evaluations, this research demonstrates the efficacy of the proposed framework in terms of accuracy, scalability, and robustness. (shrink)

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in (...) which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while enhancing efficiency, expand (...) the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

Electrocardiogram (ECG) signal analysis is a critical task in healthcare for diagnosing cardiovascular conditions such as arrhythmias, heart attacks, and other heart-related diseases. With the growth of Internet of Things (IoT) networks, real-time ECG monitoring has become possible through wearable devices and sensors, providing continuous patient health monitoring. However, real-time ECG signal analysis in IoT environments poses several challenges, including data latency, limited computational power of IoT devices, and energy constraints. This paper proposes a framework for Optimized Machine (...) class='Hi'>Learning Algorithms designed to analyze ECG signals in real time within IoT networks. The proposed system leverages lightweight machine learning models, including support vector machines (SVM) and convolutional neural networks (CNNs), optimized to run efficiently on low-power IoT devices while maintaining high accuracy. The system addresses the computational limitations of IoT devices by employing edge computing techniques that distribute the processing load between IoT devices and edge servers. Additionally, data compression and feature extraction techniques are applied to reduce the size of the data transmitted over the network, thereby minimizing latency and bandwidth usage. This paper reviews the current advancements in real-time ECG analysis, explores the challenges posed by IoT environments, and presents the optimized machine learning algorithms that enhance real-time monitoring of heart health. The system is evaluated for its performance in terms of accuracy, energy efficiency, and data transmission speed, showing promising results in improving real-time ECG signal analysis in resource-constrained IoT networks. (shrink)

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models (...) provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding. (shrink)

The rapid adoption of digital technologies in healthcare has revolutionized patient care, enabling seamless data sharing, remote consultations, and enhanced medical record management. However, this digital transformation has also introduced significant challenges to network security and data protection. Healthcare institutions face a dual challenge: ensuring uninterrupted connectivity for critical operations and safeguarding sensitive patient information from cyber threats. These challenges are exacerbated by the increased use of interconnected devices, electronic health records (EHRs), and cloud-based solutions, which, while enhancing efficiency, expand (...) the attack surface for malicious actors. This research focuses on addressing the pressing need for robust network security in healthcare institutions. It examines the unique vulnerabilities of healthcare networks, including the risks posed by outdated infrastructure, insufficient encryption protocols, and the lack of standardized security practices across systems. The paper highlights the critical role of advanced cybersecurity frameworks, such as zero-trust architectures and realtime threat detection systems, in mitigating risks. Additionally, it explores the integration of artificial intelligence (AI) and machine learning (ML) in enhancing the predictive capabilities of security tools, enabling institutions to proactively identify and neutralize potential threats. Connectivity remains a cornerstone of modern healthcare operations, facilitating collaboration among healthcare providers and ensuring timely access to patient data. However, maintaining connectivity without compromising security is a complex task. This research delves into strategies for achieving this balance, such as implementing secure Virtual Private Networks (VPNs), multi-factor authentication (MFA), and end-to-end encryption. It also emphasizes the importance of regular network assessments, employee training programs, and the adoption of compliance standards like HIPAA to ensure a secure and resilient network environment. The study includes case analyses of healthcare institutions that successfully navigated cybersecurity challenges, providing actionable insights into effective practices and lessons learned. These examples underscore the importance of integrating technological solutions with a culture of security awareness, emphasizing collaboration between IT professionals and medical staff. As cyber threats continue to evolve, healthcare institutions must remain vigilant and adaptive, embracing innovative solutions to safeguard their networks and protect patient data. This paper contributes to the broader discourse on cybersecurity in healthcare by proposing a comprehensive approach that addresses connectivity and data protection challenges while fostering operational efficiency. By prioritizing network security, healthcare institutions can build trust with patients, comply with regulatory requirements, and ensure the uninterrupted delivery of quality care. This research provides a roadmap for healthcare organizations seeking to strengthen their cybersecurity posture, highlighting the necessity of adopting a proactive, multi-layered approach to combat emerging threats. It calls for continuous investment in advanced technologies and emphasizes the role of collaboration among stakeholders to create a secure and connected healthcare ecosystem. (shrink)

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence helps mind researchers (...) to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software. (shrink)

: The integration of Machine Learning (ML) in Real-Time Biomedical Signal Processing has unlocked new possibilities in the field of telemedicine, especially when combined with the high-speed, low-latency capabilities of 5G networks. As telemedicine grows in importance, particularly in remote and underserved areas, real-time processing of biomedical signals such as ECG, EEG, and EMG is essential for accurate diagnosis and continuous monitoring of patients. Machine learning algorithms can be used to analyze large volumes of biomedical data, (...) enabling faster and more precise detection of anomalies. This paper proposes a novel system for machine learning-based real-time biomedical signal processing that leverages the capabilities of 5G networks to enhance the transmission, processing, and analysis of critical medical data in telemedicine applications. The system integrates convolutional neural networks (CNNs) for signal classification, anomaly detection, and predictive analysis, ensuring that patients receive timely and accurate medical feedback. Additionally, the 5G network’s low latency and high bandwidth provide seamless data transmission, improving remote diagnostics and enabling high-quality teleconsultations. This paper evaluates the current challenges in real-time biomedical signal processing in telemedicine, discusses the potential of machine learning and 5G networks, and presents an innovative solution for improving healthcare delivery through this integrated approach. (shrink)

In the contemporary world, there is lot of research going on in creating novel nano materials that are essential for many industries including electronic chips and storage devices in cloud to mention few. At the same time, there is emergence of usage of machine learning (ML) for solving problems in different industries such as manufacturing, physics and chemical engineering. ML has potential to solve many real world problems with its ability to learn in either supervised or unsupervised means. (...) It is inferred from the state of the art that that it is essential to use ML methods for analysing imagery of nano materials so as to ascertain facts further towards characterization and analysis of microstructure and spectral data of materials. Towards this end, in this paper, we proposed a ML based methodology for STEM image analysis and spectral data analysis from STEM image of a nano material. We proposed an algorithm named Machine Learning for STEM Image Analysis (ML-SIA) for analysing STEM image of a nano material. We proposed another algorithm named Machine Learning for STEM Image Spectral Data Analysis (ML-SISDA) for analysing spectral data of STEM image of a nano material. We developed a prototype ML application to implement the algorithms and evaluate the proposed methodology. Experimental results revealed that the ML based approaches are useful for characterization of nano materials. Thus this research helps in taking this forward by triggering further work in the area of material analysis with artificial intelligence. (shrink)

In an era of increasing cyber threats, ethical hacking has emerged as a pivotal practice in strengthening network security. Ethical hacking, also known as penetration testing, involves authorized attempts to breach a network or system to uncover vulnerabilities before malicious actors can exploit them. This research paper delves into the role of ethical hacking in assessing and mitigating network vulnerabilities to fortify defenses against cyberattacks. It emphasizes the strategic importance of ethical hacking in the context of evolving cybersecurity challenges (...) and explores its practical application in organizational security frameworks. The study begins by defining ethical hacking and its ethical and legal boundaries, distinguishing it from malicious hacking activities. It examines the methodologies and tools used by ethical hackers to identify vulnerabilities, simulate attacks, and provide actionable recommendations for remediation. By mimicking the tactics of cybercriminals, ethical hackers play a critical role in uncovering weaknesses in network configurations, software applications, and hardware systems. A core focus of the research is on the importance of integrating ethical hacking into a proactive cybersecurity strategy. The paper highlights how ethical hacking helps organizations stay ahead of potential threats by identifying vulnerabilities in real time and enabling swift mitigation. Case studies from various industries, such as healthcare, finance, and technology, are analyzed to demonstrate the tangible benefits of ethical hacking in preventing data breaches, minimizing downtime, and safeguarding sensitive information. The paper also explores the challenges associated with ethical hacking, including the need for skilled professionals, the potential for system disruptions during testing, and the importance of maintaining strict compliance with legal and regulatory standards. It discusses the ethical implications of penetration testing, particularly in balancing the need for security with respect for privacy and data protection. Advancements in tools and techniques used by ethical hackers are another significant aspect of this research. The paper investigates the use of artificial intelligence (AI) and machine learning (ML) to enhance vulnerability detection and streamline testing processes. It also addresses the rise of automated penetration testing tools and their role in increasing the efficiency and accuracy of ethical hacking efforts. The findings of this research underscore the necessity of ethical hacking as an essential component of modern cybersecurity frameworks. Organizations that adopt ethical hacking as part of their security strategy gain valuable insights into their vulnerabilities and can build resilient defenses against an everevolving threat landscape. By fostering a culture of continuous testing and improvement, ethical hacking contributes to the creation of a robust and secure network environment. This paper aims to serve as a comprehensive resource for cybersecurity professionals, IT managers, and policymakers, offering insights into the best practices, tools, and techniques of ethical hacking. By leveraging the principles of ethical hacking, organizations can effectively assess vulnerabilities, enhance their security posture, and protect critical assets in the face of mounting cyber threats. (shrink)

Objectives: Machine learning (ML) has the potential to facilitate “continual learning” in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such “adaptive” ML systems in medicine that have, thus far, been neglected in the literature. -/- Target audience: The target audiences for (...) this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. -/- Scope: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit—diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)—and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems. (shrink)

A novel advantage of the use of machine learning (ML) systems in medicine is their potential to continue learning from new data after implementation in clinical practice. To date, considerations of the ethical questions raised by the design and use of adaptive machine learning systems in medicine have, for the most part, been confined to discussion of the so-called “update problem,” which concerns how regulators should approach systems whose performance and parameters continue to change even (...) after they have received regulatory approval. In this paper, we draw attention to a prior ethical question: whether the continuous learning that will occur in such systems after their initial deployment should be classified, and regulated, as medical research? We argue that there is a strong prima facie case that the use of continuous learning in medical ML systems should be categorized, and regulated, as research and that individuals whose treatment involves such systems should be treated as research subjects. (shrink)

Results indicate significant accuracy improvements over traditional machine learning approaches, demonstrating the potential of deep learning in species identification. This project holds promise for applications in wildlife monitoring, ecological research, and educational tools, promoting awareness and conservation efforts. Future work may include integrating the system into mobile applications or deploying it for real-time bird species identification in field conditions.

In the modern enterprise environment, where cybersecurity threats continue to evolve in complexity and sophistication, network segmentation and micro-segmentation have emerged as critical strategies for mitigating risks and reducing attack surfaces. This research paper explores the principles, implementation, and benefits of network segmentation and micro-segmentation as essential components of a comprehensive cybersecurity framework. By dividing networks into smaller, isolated segments, these methodologies aim to limit unauthorized access, minimize lateral movement, and contain potential breaches, ensuring a more secure network (...) infrastructure. Network segmentation focuses on dividing large networks into smaller, more manageable subnetworks. This process enforces boundaries between different areas of a network, reducing exposure and protecting sensitive data. Meanwhile, microsegmentation extends this concept to the individual workload level, offering granular security controls that adapt to dynamic and cloud-based environments. These approaches are particularly relevant in today's context, where hybrid infrastructures and multi-cloud deployments are becoming the norm, posing significant security challenges. The paper examines the technical underpinnings of segmentation techniques, highlighting tools and frameworks that facilitate their deployment. It also addresses key challenges, such as the complexity of configuration, potential performance bottlenecks, and the necessity for alignment with broader organizational policies. Case studies from industries such as healthcare, finance, and government are analyzed to demonstrate the effectiveness of segmentation in reducing the scope and impact of cyberattacks. Additionally, this study delves into the evolving landscape of cyber threats, emphasizing the role of segmentation in countering advanced persistent threats (APTs), ransomware attacks, and insider threats. By adopting a zero-trust architecture that integrates micro-segmentation, organizations can ensure that every access request is verified and confined to the least privileged level necessary. This proactive approach to network defense aligns with industry best practices and regulatory standards, enhancing an organization's security posture. Furthermore, the research highlights the importance of continuous monitoring and automation in maintaining segmented networks. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are explored for their potential to optimize and simplify segmentation processes. These advancements enable organizations to dynamically adapt to evolving threats while maintaining operational efficiency. The findings emphasize that while network segmentation and micro-segmentation are not silver bullets, they represent indispensable layers of defense within a multi-faceted cybersecurity strategy. Organizations that successfully implement these strategies can significantly reduce the likelihood and impact of breaches, protect critical assets, and build resilience against future threats. This paper aims to provide a comprehensive guide for cybersecurity professionals, IT administrators, and policymakers to understand and adopt network segmentation and micro-segmentation. By integrating these strategies into their security frameworks, enterprises can fortify their defenses in the face of a constantly shifting threat landscape, safeguarding their infrastructure, data, and operations. (shrink)

Edge computing, combined with machine learning (ML), is emerging as a transformative paradigm for handling the data deluge generated by the Internet of Things (IoT) devices. Traditional cloud computing is often inadequate for the low-latency, high-throughput demands of IoT applications, especially in real-time analytics. By processing data locally at the edge of the network, edge computing reduces latency, enhances privacy, and alleviates the bandwidth burden on centralized cloud servers. The integration of ML algorithms into edge devices further augments (...) the decision-making process by enabling real-time data analytics, improving operational efficiency, and delivering immediate insights. This paper explores the synergy between edge computing, ML, and IoT, discussing how real-time data analytics at the edge can transform industries such as smart cities, healthcare, and industrial automation. We also present challenges, future directions, and practical applications of machine learning-based real-time analytics for edge computing in IoT ecosystems. (shrink)