The philosophical literature on personal relationships is focused on dyads: close relationships between just two people. This paper aims to characterise the value of looser and larger relational networks, particularly from the perspective of liberal political theory. We focus on relational networks' value vis-a-vis the important good of felt security. We begin by characterising felt security and analysing how felt security is produced within dyads. We highlight the ambivalent nature of dyadic relationships as a source of felt (...) security. We then theorise relational networks, explaining how they too can provide felt security. We explore three unappreciated benefits that arise when felt security is garnered from a network, rather than from a dyad. These benefits take liberal political theory as their starting point. We close by considering the implications - for both individual action and institutional design - of using relational networks as a source of felt security. (shrink)

Although Internet of Things (IoT) devices are being rapidly embraced worldwide, there are still several security concerns. Due to their limited resources, they are susceptible to malware assaults such as Gafgyt and Mirai, which have the ability to interrupt networks and infect devices. This work looks into methods based on machine learning to identify and categorize malware in IoT network activity. A dataset comprising both malware and benign traffic is used to assess different classification techniques, such as Random (...) Forest, XGBoost, Logistic Regression, etc., for multi-class malware detection. After a great deal of empirical testing, XGBoost comes out on top, providing 99.9% recall and accuracy. Both known and unknown malware can be detected by the trained model with remarkable precision. The use of transfer learning, where the XGBoost model is used as a basis for the rapid construction of new malware detection models, is one of the major innovations put forth. This makes it possible to quickly adjust to new dangers. More information about how real-time network traffic can be monitored with the help of the created model to find irregularities and sound the alarm. An intelligent and proactive security solution for IoT environments is offered by the machine learning technique that is being discussed. This is an efficient defense against malware because of its high accuracy, low false positive rate, real-time detection capability, and adaptability to new malware varieties changing risks associated with IoT. The suggested methods will assist in securing susceptible IoT devices and networks from obstructive malware assaults. (shrink)

Abstract: Wireless security is the avoidance of unlawful access or impairment to computers using wireless networks. Securing wireless network has been a research in the past two decades without coming up with prior solution to which security method should be employed to prevent unlawful access of data. The aim of this study was to review some literatures on wireless security in the areas of attacks, threats, vulnerabilities and some solutions to deal with those problems. It was (...) found that attackers (hackers) have different mechanisms to attack the networks through bypassing the security trap developed by organizations and they may use one weak pint to attack the whole network of an organization. However the author suggested using firewall in each wireless access point as the counter measure to protect data of the whole organization not to be attacked. (shrink)

In the current era dominated by cloud computing, networking infrastructure serves as the backbone of digital operations. Among the top cloud service providers, Microsoft Azure offers a robust suite of networking solutions tailored to meet the evolving needs of modern businesses. This document aims to provide a comprehensive overview of Azure networking, examining its key components, deployment models, best practices, and practical applications. By exploring Azure Virtual Network, Load Balancer, VPN Gateway, ExpressRoute, Firewall, and other services in detail, the (...) goal is to equip organizations with the necessary knowledge and insights to effectively leverage Azure networking for seamless cloud connectivity and enhanced security. (shrink)

This research presents a novel and efficient public key cryptosystem known as the Enhanced Schmidt Samoa (ESS) cryptosystem, proposed to safeguard the data of a single owner in cloud computing environments. Data storage is a one-time process in the cloud, while data retrieval is a frequent operation. Experimental results demonstrate that the ESS cryptosystem offers robust data confidentiality in the cloud, surpassing the security provided by traditional cryptosystems. The research also introduces a secure cloud framework designed to accommodate both (...) individuals and organizations accessing applications and data in the cloud. While individual users may generate and share data, organizations often involve multiple users in data sharing to support their business processes. In these scenarios, multi-user data ownership and access management become critical, requiring secure sharing of cryptographic keys among the authorized users. To address these issues and ensure data confidentiality in multi-user cloud environments, the Improved Secure Cloud Data Storage Framework (ISCDSF) is introduced. This research not only enhances data security but also provides a comprehensive framework for secure data sharing in the cloud, catering to the needs of both individual users and organizations. (shrink)

The increasing complexity and demands of next-generation networks necessitate the integration of Artificial Intelligence (AI) to enhance their efficiency and security. This article explores advancements in AI-driven communication systems, focusing on optimizing network performance, ensuring robust security measures, and addressing the challenges of scalability and real-time adaptability. By analyzing case studies, emerging technologies, and recent research, this study highlights AI's transformative potential in redefining communication systems for future applications.

The Internet Protocol (IP) is the source of Internet transmission but an inadequate authentication technique paves a way for IP spoofing. Since, IP spoofing initiates more attacks in the network, a secure authentication technique is required to defend against spoofing attacks. In order to achieve this, in this paper, we p ropose a (SVT) for defending IP spoofing attacks. Our technique authenticates IP address of each Autonomous System (AS) using Neighbour Authentication (NA) algorithm. The algorithm works by validating NA (...) table NA constructed by each node. The NA table is transmitted securely using RC-6 encryption algorithm. In addition to encryption, our technique use arithmetic coding to compress and decompress the NA table. The performance of our technique is proved through simulation results. Our technique incurs low overhead and significantly improves the performance of network. (shrink)

Cyber espionage campaigns pose significant challenges to global security, exploiting vulnerabilities in network infrastructures. This research paper explores advanced network traffic analysis models tailored for detecting sophisticated cyber espionage operations. The study focuses on leveraging machine learning algorithms, anomaly detection systems, and hybrid threat detection frameworks to identify subtle yet malicious activities within network traffic. Through a review of research, this paper synthesizes key findings and outlines practical applications, offering a roadmap for enhancing cybersecurity frameworks. Findings (...) highlight the efficacy of deep learning and unsupervised models in mitigating espionage-related risks, paving the way for more robust detection systems. (shrink)

E-Government refers to the administration of Information and Communication Technologies (ICT) to the procedures and functions of the government with the objective of enhancing the transparency, efficiency and participation of the citizens. E-Government is tough systems that require distribution, protection of privacy and security and collapse of these could result in social and economic costs on a large scale. Many of the available e-government systems like electronic identity system of management (eIDs), websites are established at duplicated databases and servers. (...) An established validation and management system could face a single failure point and the system is prone to Distributed Denial of Service Attacks (DDoS), denial of service attacks (DoS), malware and other cyber attacks. The execution of a privacy preserving and a secure decentralized system is enabled by the block chain technology. Here any third-party organizations do not have any control over the transactions of the Government. With the help of block chain technology, new and existing data are encapsulated within ledger or blocks, which are evenly distributed through the network in an enduring and sustainable way. The privacy and security of information are improved with the help of block chain technology, where distribution and encryption of data are performed through the total network. This analytical paper maps out the analysis of the security in the e-government system, utilizing the block chain technology that provides privacy and security of information and thereby enhancing the trust among the public sector. Qualitative and theoretical analysis is made for the proposed topic and implications of privacy and security of the proposed system is made. (shrink)

As the Internet of Things (IoT) continues to expand, ensuring secure and efficient data storage and retrieval becomes a critical challenge. IoT devices, often constrained by limited computational resources, require lightweight encryption protocols that balance security and performance. This paper presents an optimized encryption protocol designed specifically for lightweight, searchable data in IoT environments. The proposed protocol utilizes advanced optimization techniques to enhance the efficiency and security of searchable encryption, enabling rapid data retrieval without compromising the integrity and (...) confidentiality of the data. The protocol leverages a hybrid cryptographic approach, combining symmetric and asymmetric encryption, to ensure robust protection against unauthorized access while maintaining low computational overhead. Furthermore, the implementation of an optimized keyword indexing mechanism facilitates fast and accurate search operations, making the protocol well-suited for real-time IoT applications. Extensive experiments were conducted using various IoT datasets to evaluate the performance of the proposed protocol in terms of encryption speed, search efficiency, and overall security. The results demonstrate that the optimized encryption protocol significantly outperforms existing methods, offering a scalable solution that meets the stringent requirements of IoT systems. This research contributes to the field by providing a practical and secure encryption solution that addresses the unique challenges of IoT environments, paving the way for more secure and efficient IoT networks. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis (...) for better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

With the growing sophistication and frequency of cyberattacks, there is a critical need for effective systems that can detect and prevent breaches in real time. The AI/ML-based Network Intrusion Detection System (NIDS) addresses this need by analyzing traffic patterns to identify security breaches in firewalls, routers, and network infrastructures. By integrating machine learning algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest—the system is able to detect both known cyber threats and previously unseen attack vectors. Unlike (...) traditional methods that rely heavily on predefined indicators of compromise (IoCs), this system utilizes anomaly detection techniques, allowing it to identify new and emerging threats. As cyberattacks evolve, organizations must adopt adaptive methods to secure their networks. This system achieves high accuracy in classifying network traffic, with real-time alerts that provide early warnings of suspicious activities. It also includes intuitive visualizations, helping network administrators gain insights into the nature and scope of attacks. With the rise of increasingly complex and frequent cyberattacks, this NIDS offers a robust solution for enhancing network security and response capabilities. (shrink)

Abstract: Networking is one of the most important areas currently used for data transfer and enterprise management. It also includes the security aspect that enables us to protect our network to prevent hackers from accessing the organization's data. In this paper, we would like to learn what the network is and how it works. And what are the basics of the network since its emergence and know the mechanism of action components. After reading this paper - (...) even if you do not have a general background on networking - you will be able to manage your own network and be able to distribute and control your ip. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and Convolutional Neural (...) Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

given the increasing threats to data confidentiality and integrity. The project titled "Anonymization and Revocation of User Identity in CPABE-Based Ring Networks" addresses these concerns by integrating CiphertextPolicy Attribute-Based Encryption (CPABE) into a ring network environment. This novel approach effectively hides user identities during communications, mitigating risks of unauthorized data access and preventing potential misuse of sensitive information. A key feature of this project is the implementation of a secure revocation mechanism that ensures once a user's permissions are revoked, (...) they no longer have access to protected data, thereby strengthening the overall security framework. By combining CPABE’s robust access control with the anonymity provided by ring signatures, the project establishes a solution that guarantees both privacy preservation and secure data access control in networked environments. The successful integration of anonymization and secure revocation into CPABE-based systems represents a significant contribution to the field of secure communication networks, offering a reliable method for safeguarding user identities and maintaining data integrity. This approach can greatly enhance the security and privacy aspects of digital communications in a world where privacy breaches are increasingly prevalent. (shrink)

This study aimed to identify the extent of the application of Cyber Security at the Ministry of Interior and National Security from the point of view of workers in the computer and information technology units. 70 employees, and the study tool (questionnaire) was distributed, and the comprehensive survey method was used, as (61) questionnaires were retrieved at a rate of (87.1%), and they were unloaded and analyzed using the SPSS statistical package. The study reached several results, including: There (...) was a high degree of agreement on the degree of Cyber Security application, which amounted to (74.8%). With a relative weight of (74.8%), and The Dimension efforts to monitor threats, it received a high degree of approval and a relative weight of (73.4%). Privacy has a high degree of approval with a relative weight of (78.4%).The study presented a set of recommendations, the most important of which are: the need to pay attention to providing the Ministry of Interior and National Security with a budget in order to activate and improve the Cyber Security system, and the need for the Ministry of Interior and National Security to use a protection network capable of detecting all threats, and the cooperation of the Ministry of Interior and National Security with external institutions to limit penetration of network systems, In addition to the need to continuously strengthen and develop the laws and legislations of the Ministry of Interior in line with the development of Cyber Security, and to enhance the capabilities of the public beneficiaries of the ministry's electronic services to reduce security gaps in the field of Cyber Security. (shrink)

—Industrial networks are increasingly based on open protocols and platforms that are also employed in the IT industry and Internet background. Most of the industries use wireless networks for communicating information and data, due to high cable cost. Since, the wireless networks are insecure, it is essential to secure the critical information and data during transmission. The data that transmitted is intercepted by eavesdropper can be predicted by secrecy capacity. The secrecy capacity is the difference between channel capacity of main (...) link and wiretap link of wireless transmission. When the secrecy capacity gets fades, then it is known that transmitted data is intercepted. In the event, of applying optimal sensor scheduling scheme a sensor with highest secrecy capacity is chosen and data is transmitted. It is followed with cooperative localization algorithm to enhance nodes, predict and maintain power of each sensor nodes. Through localization algorithm best sensor nodes are get predicted for transmission. Moreover, RC4 encryption and decryption algorithm is enhanced for data while transmission. This will enable protection to data to an extent. Also, an asymptotic intercept probability analysis is performed to provide an imminent into the impact of the sensor scheduling on the wireless security. (shrink)

Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world applications. The aim of this study is to implement and compare the performances of each variant of the Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant’s assumption and their performance. Our investigation showed that each variant of the Bayesian algorithm blindly follows (...) its assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while Gaussian has an accuracy of 81.69% test (82.84% train). Going deeper, we investigated and found that each Naïve Bayes variants performances and accuracy is largely due to each classifier assumption, Gaussian classifier performed best on anomaly detection due to its assumption that features follow normal distributions which are continuous, while multinomial classifier have a dismal performance as it simply assumes discreet and multinomial distribution. (shrink)

The rapid proliferation of interconnected devices and the increasing complexity of digital networks in the modern era have resulted in a surge of diverse and voluminous network traffic. This growth poses considerable challenges in effectively distinguishing between normal and malicious data flows. As cyber threats continue to evolve, traditional traffic classification methods struggle to keep pace with the dynamic and multifaceted security challenges of contemporary networks. In this context, ensuring robust network security has become an imperative. (...) This research aims to address these challenges by developing a machine learning-based approach to accurately classify and segregate malicious traffic from legitimate traffic, thereby strengthening overall network defence mechanisms. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) and Convolutional (...) Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is (...) a combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay. (shrink)

Nowadays Fog Computing has become a vast research area in the domain of cloud computing. Due to its ability of extending the cloud services towards the edge of the network, reduced service latency and improved Quality of Services, which provides better user experience. However, the qualities of Fog Computing emerge new security and protection challenges. The Current security and protection estimations for cloud computing cannot be straightforwardly applied to the fog computing because of its portability and heterogeneity. (...) So these issues in fog computing arises new research challenges and opportunities. This survey features about existing security concerns for fog computing and new proposed system to tackle some of the issues in fog computing related to security and privacy, thereby enhancing the cloud security. (shrink)

Mobile and wireless networking infrastructures are facing unprecedented loads due to increasing apps and services on mobiles. Hence, 5G systems have been developed to maximise mobile user experiences as they can accommodate large volumes of traffics with extractions of fine-grained data while offering flexible network resource controls. Potential solutions for managing networks and their security using network traffic are based on UAA (User Activity Analysis). DLTs (Deep Learning Techniques) have been recently used in network traffic analysis (...) for better performances. These previously suggested techniques for network traffic analysis typically need voluminous information on network usages. Hence, this work proposes OFedeMWOUAA (optimal federated learning-based UAA technique with Meadow Wolf Optimisation) and DNN (deep Neuron Networks) for minimizing risks of data leakages in MWNs (Mobile Wireless Networks). In the proposed OFedeMWOUAA, the need to submit data to cloud servers does not arise because it trains DLTs locally and only uploads model gradients or knowledge weights. The OFedeMWOUAA approach effectively decreases dangers to data privacies with very minor performance losses in simulations. (shrink)

Cloud computing has transformed data management by providing scalable and on-demand services, but its open and shared infrastructure makes it highly vulnerable to sophisticated cyber threats. Traditional Intrusion Detection Systems (IDS) struggle with dynamic and large-scale cloud environments due to high false positives, limited adaptability, and computational overhead. To address these challenges, this paper proposes an AI-driven Intrusion Detection System (AI-IDS) that leverages deep learning models, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks, to analyze network (...) traffic, detect anomalies, and identify advanced cyber threats with high accuracy. Unlike rule-based IDS, which rely on static signatures, our model continuously learns from real-time data, improving detection rates while reducing false alarms. The proposed AI-IDS is optimized for cloud infrastructure, ensuring low-latency threat detection, minimal computational burden, and real-time adaptability to emerging attack patterns. Experimental validation on benchmark datasets demonstrates significant improvements in accuracy, precision, and scalability, making AI-powered IDS a crucial innovation in cloud security. (shrink)

This paper extensively develops and thoroughly creates a highly centralized and extremely efficient log collection system utilizing and leveraging the robust syslog server and comprehensively collects and gathers log data from the numerous hosts residing within the network, meticulously analyses and scrutinizes the detailed log files and promptly sends and transmits a critical alert message to the central host through secure and reliable SMS. This paper thoroughly and comprehensively addresses and tackles the increasingly growing and rapidly expanding need for (...) extremely robust and highly reliable cybersecurity measures and solutions in the vulnerable and sensitive power sector. (shrink)

Closed-Circuit Television (CCTV) systems are essential in modern security setups because they provide continuous surveillance, acting as both a deterrent and a critical tool for monitoring and evidence collection. Unlike human guards who can be limited by fatigue and blind spots, CCTV cameras offer consistent, 24/7 coverage of key areas. They fill gaps in the current security system by enabling real-time monitoring and recording incidents for later review, ensuring that potential security breaches are detected and addressed more (...) effectively. This enhances overall security effectiveness and reduces the reliance on human intervention. The integration of Artificial Intelligence and Machine Learning (AIML) techniques with existing CCTV networks presents a promising approach to address critical challenges in urban environments. This project examines how AIML can be leveraged for crowd management, crime prevention, and work monitoring using CCTV infrastructure. For crowd management, AIML enables automated crowd counting and density estimation, facilitating efficient allocation of resources during events and emergencies. In crime prevention, AIML algorithms analyze video feeds in real-time to detect suspicious activities and identify anomalies, aiding law enforcement in proactive interventions. Additionally, AIML enhances workplace monitoring by tracking productivity metrics, ensuring compliance with safety protocols, and optimizing operational workflows. Through these applications, AIML empowers cities and organizations to improve public safety, enhance operational efficiency, and make data-driven decisions based on insights derived from CCTV footage. The integration of AIML with existing CCTV networks represents a transformative advancement in urban surveillance and management practices, offering scalable solutions for diverse urban challenges. (shrink)

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To tackle (...) these obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high (...) accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

Since Vehicular ad hoc networks (VANETs) are vulnerable to various kinds of attacks, there is a need to fulfill the security requirements like message privacy, integrity, and authentication. The authentication technique is said to be efficient if it detects compromised nodes accurately with less complexity, reduced authentication delay, and keying overhead. In this paper, a trust-based authentication scheme for cluster-based VANETs is proposed. The vehicles are clustered, and the trust degree of each node is estimated. The trust degree is (...) a combination of direct trust degree and indirect trust degree. Based on this estimated trust degree, cluster heads are selected. Then, each vehicle is monitored by a set of verifiers, and the messages are digitally signed by the sender and encrypted using a public/ private key as distributed by a trusted authority and decrypted by the destination. This verifies the identity of sender as well as receiver thus providing authentication to the scheme. By simulation results, we prove that the proposed technique provides high security with less overhead and delay. (shrink)

Web page phishing is a type of phishing attack that targets websites. In a web page phishing attack, the attacker creates a fake website that looks like a legitimate website, such as a bank or credit card company website. The attacker then sends a fraudulent message to the victim, which contains a link to the fake website. When the victim clicks on the link, they are taken to the fake website and tricked into entering their personal information.Web page phishing attacks (...) are a serious threat to online security. They can be very effective, as they often look very convincing. To protect yourself from web page phishing attacks, you should be suspicious of any emails or messages that ask for your personal information. Do not click on links in emails or messages from unknown senders. If you are unsure whether a website is legitimate, do not enter your personal information. Therefore, in this study, we present a novel approach to detect whether a web page is phishing or legitimate using a neural network model. (shrink)

With the increasing popularity of social networking websites, the problem of fake profiles has become a significant concern. Fake profiles, often created by malicious actors for fraudulent purposes, pose threats to user privacy, security, and trustworthiness of online platforms. This project proposes a machine learning-based approach to detect fake profiles on social networking websites. By analyzing various features such as user activity patterns, profile attributes, and network connections, the model identifies potential fake profiles with high accuracy. The system (...) employs a variety of machine learning algorithms, including decision trees, support vector machines (SVM), and random forests, to classify profiles as either genuine or fake. Data preprocessing techniques such as feature extraction, normalization, and outlier detection are applied to enhance the model's performance. The proposed approach is evaluated on a dataset of social network profiles, and its effectiveness is compared to existing methods in terms of precision, recall, and F1-score. The results demonstrate the ability of the machine learning model to detect fake profiles accurately, providing a valuable tool for social networking platforms to protect users from potential threats and improve the overall user experience. This solution can also help in the automated detection of fraudsters and reduce the manual effort required for profile validation. (shrink)

The modern computer environment has moved past the local data center with a single entry and exit point to a global network comprising many data centers and hundreds of entry and exit points, commonly referred as Cloud Computing, used by all possible devices with numerous entry and exit point for transactions, online processing, request and responses traveling across the network, making the ever complex networks even more complex, making traversing, monitoring and detecting threats over such an environment a (...) big challenge for Network forensic and investigation for cybercrimes. It has demanded in depth analysis using network tools and techniques to determine how best information can be extracted pertinent to an investigation. Data mining technique providing great aid in finding relevant clusters for predicting unusual activities, pattern matching and fraud detection in an environment, capable to deal with huge amount of data. The concept of network forensics in cloud computing requires a new mindset where some data will not be available, some data will be suspect, and some data will be court ready and can fit into the traditional network forensics model. From a network security viewpoint, all data traversing the cloud network backplane is visible and accessible by the cloud service provider. It is not possible to think now that one physical device will only have one operating system that needs to be taken down for investigation. Without the network forensics investigator, understanding the architecture of the cloud environment systems and possible compromises will be overlooked or missed. In this paper, we focus on the role of Network Forensic in a cloud environment, its mapping few of the available tools and contribution of Data Mining in making analysis, and also to bring out the challenges in this field. (shrink)

Abstract: The study observes the core of both trafficking in persons and security offering a preliminary understanding the interconnection between the two concepts which is indeed a precondition of the more thorough contemplation of this security problem. Noteworthy is also the further elaboration of the risk that link between violence and modern-day slavery represents having in mind society and the individual. This informal economy violates the principle of morality and is understood to be one of the most offensive (...) crimes. Its elementary features are psychological and/ or physical torture, coercing into engaging in the violation of the law and established social norms as well as transporting and harboring. The consequence of all of this is the material gain, whereas the victim appears as Machiavellian means the gain will justify. Trafficking emerges in different forms, the most discernible of which are forced labor and sexual exploitation that is among the hardest forms of desecrating human soul. Unfortunately, it is a common phenomenon that many people (some independent estimates mention a figure of several million) around the world, women and children, in particular, every year become victims of this violence. Tough living conditions, as well as uncertain economic future, are among fundamental causes infecting healthy society. If a well-organised criminal network or a ‘hidden economy’ succeeds to impose the rule of conduct, then human trafficking finds fertile soil in which to grow. Having been lured the victims later realize where they are, but many never return to their families, and that terrible fate should make society do what can be done and help them. -/- Key words: Human, state, trafficking, security, smuggling. (shrink)

With the advent of the “Clean India” campaign in India, a renewed focus on cleanliness has started, with a special focus on sanitation. There have been efforts in the past to provide sanitation related services. However, there were several challenges in provisioning. Provision of sanitation is a public health imperative given increased instances of antimicrobial resistance in India. This paper focuses on sanitation provisioning in the city of Mumbai, especially in the slums of Mumbai. The paper compares and contrasts different (...) models of sanitation provision including supply-led provisioning of sanitation by the Indian government to demand-led provisioning of sanitation through a World Bank funded “Slum Sanitation Program” (SSP). The paper also outlines a comparative perspective on the implementation and usage of toilet blocks. The author presents the theory of social networks and positive peer pressure and argues that these will amplify the effect of other incentives. With the help of an illustration, this paper concludes that the sustainable sanitation policy should look at facilitating and creating the infrastructure as a network and not strictly at building toilet blocks. (shrink)

This study aimed to identify the digital transformation and its impact on the application of Cyber Security in the Palestinian Ministry of Interior and National Security. The study used the analytical descriptive approach. The study tool (questionnaire), and the comprehensive survey method was used, where (61) questionnaires were retrieved (87.1%), and they were unloaded and analyzed using the SPSS statistical package. The study found several results, including that there is a statistically significant correlation between all dimensions of Digital (...) transformation and the application of Cyber Security in the Ministry of Interior and National Security. And that there is an impact of Digital transformation on the application of security in the Ministry of Interior and National Security in the governorates of Gaza, and the impact coefficient was (0.897). It was found that the dimensions affecting the dependent variable of Cyber Security are dimensions (organizational structure, job description, data privacy and security, technical infrastructure required for digital transformation), as these dimensions affect (89.7%) of the variation in the application of Cyber Security. The study presented a set of recommendations, the most important of which are: The need for the Ministry's administration to provide a special budget to develop the quality of its electronic services as a lever for digital transformation. And the need for the number of workers in computer and information technology departments and units to be commensurate with the volume and quality of work to bridge the gap between the required performance and the actual performance. The ministry should set a clear methodology for exchanging data and information between the components of the ministry. And the need for the Ministry of Interior and National Security to use a protection network capable of detecting all threats. (shrink)

To evaluate the effectiveness of our approach to enhancing cloud computing network forensics by integrating deep learning techniques with cybersecurity policies. With the increasing complexity and volume of cyber threats targeting cloud environments, traditional forensic methods are becoming inadequate. Deep learning techniques offer promising solutions for analyzing vast amounts of network data and detecting anomalies indicative of security breaches. By integrating deep learning models with cybersecurity policies, organizations can achieve enhanced threat detection, rapid response times, and improved (...) overall security posture. This paper discusses the key steps involved in integrating deep learning models into network forensics, including data collection, model selection, real-time monitoring, and adaptive learning. Additionally, it highlights the importance of collaboration between cybersecurity experts and the cloud. Through case studies and experimental evaluations, we demonstrate the effectiveness and practicality of the proposed approach in enhancing cloud computing network forensics. Leveraging deep learning techniques offers promising solutions for detecting anomalies and identifying malicious activities within cloud networks. (shrink)

‘Torture porn’ films centre on themes of abduction, imprisonment and suffering. Within the subgenre, protagonists are typically placed under relentless surveillance by their captors. CCTV features in more than 45 contemporary torture-themed films (including Captivity, Hunger, and Torture Room). Security cameras signify a bridging point between the captors’ ability to observe and to control their prey. Founded on power-imbalance, torture porn’s prison-spaces are panoptical. Despite failing to encapsulate contemporary surveillance’s complexities (see Haggerty, 2011), the panopticon remains a dominant paradigm (...) within surveillance studies because it captures essential truths about the psychologies of self-governance and interdependency. This chapter will use torture porn’s panoptical spaces and captor-captive relationships as a springboard into examining those broader philosophical issues regarding selfhood. In the torture-space, cameras signify the control to which captives must submit. Since they are threatened with death, the surveillance dynamic appears to entirely subjugate these prisoners. However, the captive must undertake some agency in the oppression. Much of the captor’s implied threat is enacted by the captives, who brutalise one another to save themselves. The captor’s apparent omniscience is translated into omnipotence only because the captives forsake self-control – opting to engage in violent, contra-social behaviours – out of fear. Thus, it is implied that self-ownership is the bedrock of stable, interdependent sociality. To inspire horror, the opposite is depicted: fractured groups comprised of paranoid, self-invested individuals. By submitting to external pressure, these “weak” individuals empower their tormentor. Captives are not only encouraged to enact their own suppression, but also to internalise culpability for the suffering they undergo. Despite being threatened with erasure, torture porn’s protagonists are spotlighted in these films. Abductees dominate the screen-time, and their suffering drives the narrative forward. Torturers are often motivated solely by their victims’ agony. In many cases, torture is designed specifically for each hyper-individualised captive. These forms of emphasis imply that captives are the stimulus for their own victimisation. The captor’s exaggerated interest in the prisoners is perversely flattering: captives are implied to be worthy of the captor’s maniacal attention, which is reified by the CCTV cameras. In torture porn’s scenarios, it is not immediately clear who has greater control over the individual: the captor or the captive themselves. By dissecting how self-preservation, self-governance, and self-centredness manifest in torture porn, this chapter seeks to examine the dialectical qualities of liberty, interdependency and autonomy. (shrink)

The purpose of this article is to highlight the clientelistic strategies and informal practices that the ruling political parties in Albania use during the elections to ensure an unfair advantage in their favour over the opposition challengers. One of the main characteristics of the political developments of the transition period in Albania since 1991 has been the flourishing of informal practices and clientelist networks of political parties within state structures, which has produced an extreme politicization of these institutions. These strategies (...) that in general terms we label as “clientelistic” and that have been used to a large extent by the incumbent political parties have had a direct negative impact on the conduct of free and fair elections in Albania by distorting their main goal: to reflect the will of the people. This is because such clientelist strategies, together with the informal practices/mechanisms that accompany them, have influenced the creation of an unlevelled playing field, and have produced a hyperincumbency advantage in the electoral contests between political parties in Albania. The case of the recent elections held in Albania on 25 April 2021 will be the empirical case of this study, in which are evidenced the electoral containment strategies and practices that the ruling political party used to provide an unfair advantage in its favour and to secure its grip on power. (shrink)

Internet of Things (IoT) devices have transformed various industries, enabling advanced functionalities across domains such as healthcare, smart cities, and industrial automation. However, the increasing number of connected devices has raised significant concerns regarding their security. IoT networks are highly vulnerable to a wide range of cyber threats, making Intrusion Detection Systems (IDS) critical for identifying and mitigating malicious activities. This paper proposes a hybrid approach for intrusion detection in IoT networks by combining Machine Learning (ML) techniques with Signature-Based (...) Methods. The hybrid model leverages the strengths of both methodologies to achieve high detection accuracy, reduced false positives, and the ability to identify both known and unknown threats. We explore the integration of ML classifiers such as Random Forest, Support Vector Machines, and k-Nearest Neighbors with traditional signature-based techniques to create a robust IDS solution. The effectiveness of the proposed approach is evaluated using a publicly available IoT dataset, demonstrating its capability to detect a wide variety of attacks with high precision and recall. (shrink)

Criminal offences and regulatory breaches in using social networking evidence in personal injury litigation Pages 2-7 Current Criminal Law ISSN 1758-8405 Volume 2 Issue 3 March 2010 Author SALLY RAMAGE WIPO 900614 UK TM 2401827 USA TM 3,440.910 Orchid ID 0000-0002-8854-4293 Sally Ramage, BA (Hons), MBA, LLM, MPhil, MCIJ, MCMI, DA., ASLS, BAWP. Publisher & Managing Editor, Criminal Lawyer series [1980-2022](ISSN 2049-8047); Current Criminal Law series [2008-2022] (ISSN 1758-8405) and Criminal Law News series [2008-2022] (ISSN 1758-8421). Sweet & Maxwell (Thomson (...) Reuters) (Licensed Annotator of UK Statutes) in Current Law Statutes Annotated, (2006, 2009, 2010 editions) (sole and separate S&M contracts) for 7 UK Criminal Justice Statutes: UK Safeguarding Vulnerable Persons Act 2006; UK International Development Act 2006; UK Fraud Act 2006; UK Policing and Crime Act 2009, UK Local Democracy Act 2009; UK Bribery Act 2010; and UK Crime and Security Act 2010. Trade Mark SALLY RAMAGE ® 2401827 (2005-2025) in the UK, WIPO Trade Mark SALLY RAMAGE® 900164; USA-(Trade Mark 3,440,910) (2008-2028). WARNING: The doing of an unauthorized act in relation to a copyright work may result in both a civil claim for damages and criminal prosecution. Essentially, Social Network information may not always be admissible in a court of law but may be the door to lead to other, non-contentious evidence. In fact, even illegally obtained evidence may sometimes be admissible evidence, though this is not absolute. Most of the information available on social networking sites will be ‘personal data’. The data controller is the network operator, without whose consent, collecting such information could be an offence, unless it was necessary for the purpose of preventing or detecting crime. (shrink)

Create a novel network model for mobile ad hoc network (MANET) nodes and actors in wireless sensor networks to collaborate on event processing. There are two stages in the development of distributed algorithms: setup and negotiation. The first uses weighted proportional max-min fairness to initially allocate MANET nodes across event zones, whereas the latter uses a market-based method to re-distribute the number of MANET nodes based on existing and new events. A detection technique for malicious packet dropping attacks (...) in MANETs. The mechanism of the suggested approach is Collaborative Convolutional Neural Network (CCNN), which is based on the reputation value computed for that node by its neighbours. A node's reputation value is determined by its network packet forwarding behaviour. The reputation information is collected, saved, and transferred between nodes before being calculated under various scenarios. A network simulator was used to test the proposed protocol. The simulation results demonstrate the effectiveness of its performance. Even in the presence of cryptographic procedures, our method incurs negligible network bandwidth and latency costs. Moreover, we demonstrate that the protection is still effective in the presence of misbehaving nodes and routing changes caused by mobility. While further research is needed to thoroughly evaluate our method, we feel that the concept of collaborative security in MANETs is a potential future area. (shrink)

Home management and controlling have seen a great introduction to network that enabled digital technology, especially in recent decades. For the purpose of home automation, this technique offers an exciting capability to enhance the connectivity of equipment within the home. Also, with the rapid expansion of the Internet, there are potentials that added to the remote control and monitoring of such network-enabled devices. In this paper, we had been designed and implemented a fully manageable and secure smart home (...) automation system based on a cloud computing system with an ESP Arduino system. The security of home had been improved by adding a complete camera system with a GSM communication technique to connect the Arduino output data to an external specified number if there is no internet provider. We used three sensors for temperature, gas, and motion measurements. The ESP8226 Wi-Fi device programmed the sensors to maintain the sensors measurements and transfer them to the cloud server database which is programmed to the web server via Appatshy and Mysql formats. The system implemented with high time response so that all readings updated and appeared spontaneously. The designed system should be effective, a secure, and rapid response real-time smart home system should be achieved. (shrink)

There are contradictory results regarding how students' learning outcomes can be predicted by various family and community inputs among previous studies, creating an evidence gap. Furthermore, previous studies have mostly concentrated on the cognitive aspect of students' learning outcomes, ignoring the affective and psychomotor dimensions, creating key knowledge gaps. Bridging these gaps, this predictive correlational study was conducted to understand how cultural capital, parental involvement (family inputs), support for schools, security network and school reforms (community inputs) jointly and (...) partially predict students' overall, cognitive, affective and psychomotor learning outcomes in the context of Calabar Education Zone, Nigeria. A random sample of principals (?? = 78) and students (?? = 915) recruited through a multistage approach, participated in the study. Data were collected through the physical administration of three sets of questionnaires designed by the researchers. The psychometric properties of the questionnaires (such as validity, dimensionality, reliability and goodness of fit) were all analysed and found acceptable based on pilot data. Data analysis was performed using SPSS version 27 and AMOS version 26 software. Results from the main study proved, among others, that family inputs (family social capital and parental involvement) jointly and individually had a significant contribution to students' overall, cognitive, affective and psychomotor learning outcomes. Similarly, community inputs (support for school, security network and school reforms) have significant composite and partial contributions to students' overall, cognitive, affective and psychomotor learning outcomes in public secondary schools. This result implies that parents and host community leaders must strengthen their partnerships with secondary schools and contribute their quota to institutions' curricular and co-curricular activities. (shrink)

The main purpose of the study is to determine the key aspects of the mechanisms of state management of the exchange of information about cyberattacks, cyber incidents, and information security incidents. The methodology includes a set of theoretical methods. Modern government, on the one hand, must take into account the emergence of such a new weapon as cyber, which can break various information systems, can be used in hybrid wars, influence political events, pose a threat to the national (...) class='Hi'>security of any state. As a result of the study, key elements of the mechanisms of state management of the exchange of information about cyberattacks, cyber incidents, and information security incidents were identified. (shrink)

A large amount of data is maintained in every Social networking sites.The total data constantly gathered on these sites make it difficult for methods like use of field agents, clipping services and ad-hoc research to maintain social media data. This paper discusses the previous research on sentiment analysis.

issues in real-time, allowing authorities to address concerns swiftly. Additionally, the integration of IoT-based monitoring, utilizing ESP32/ARDUINO devices, will enable the collection of real-time data, providing alerts on water supply status and system malfunctions. The technology stack comprises JavaScript, PHP, and PostgreSQL with PostGIS for spatial data management, emphasizing secure, scalable data handling. The platform is designed for continuous improvement through user feedback, ensuring it evolves to meet the dynamic needs of the Jal Jeevan Mission. Through innovative technology and a (...) user-centric approach, this project aims to support equitable access to safe water across rural areas. (shrink)

Generative Adversarial Networks (GANs) have demonstrated significant potential in generating synthetic data for various applications, including those involving sensitive information like healthcare and finance. However, two major issues arise when GANs are applied to sensitive datasets: (i) the model may memorize training samples, compromising the privacy of individuals, especially when the data includes personally identifiable information (PII), and (ii) there is a lack of control over the specificity of the generated samples, which limits their utility for tailored use-cases. To address (...) these challenges, we propose a novel framework that integrates differential privacy with latent representation learning to ensure privacy while providing control over the specificity of generated data. Our approach ensures that the synthetic data does not reveal individual data points, and by learning effective latent codes, it allows for the generation of specific and meaningful data. We evaluate our method using the MNIST dataset, showing that it preserves privacy and demonstrates a privacy-utility trade-off, where increased privacy leads to decreased classification accuracy. Additionally, we highlight the computational challenges, as the training process incurs a tenfold increase in time compared to standard GAN models. Finally, we extend our approach to the CelebA dataset, demonstrating how privacy and specificity can be controlled to generate high-quality, private synthetic data. (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)

Security is a major concern on the internet, and Distributed Denial of Service (DDoS) attacks are a significant threat. These attacks overwhelm network resources and use up bandwidth, making it difficult for legitimate users to access services. One challenge in dealing with DDoS attacks is telling the difference between a sudden increase in traffic from real users, known as a "flash crowd," and actual attack traffic. This paper looks at different existing solutions for detecting DDoS attacks and explains (...) how these methods help reduce their impact. As more businesses move to cloud-based services, the risk of DDoS attacks has increased. These attacks can seriously disrupt organizations by overloading their network resources and making important services unavailable. Traditional defense methods, like firewalls, are no longer enough to handle the scale and complexity of modern DDoS attacks. To address this issue, We propose a new approach that utilizes the implementation of deep learning models to detect DDoS attacks in cloud environments. Our method uses various features from network traffic to train machine learning algorithms, aiming to effectively identify DDoS attacks in real-time with high accuracy. (shrink)

A number of the men who would become the 9/11 hijackers were stopped for minor traffic violations. They were pulled over by police officers for speeding or caught by random inspection without a driver’s license. For United States government commissions and the press, these brushes with the law were missed opportunities. For some police officers though, they were of personal and professional significance. These officers replayed the incidents of contact with the 19 men, which lay bare the uncertainty of every (...) encounter, whether a traffic stop, or with someone taking photos of a landmark. Representatives from law enforcement began to design policies to include local police in national intelligence, with the idea of capitalizing on what patrol officers already do in dealing with the general public. Several initiatives were launched, among these, the Suspicious Activity Reporting Initiative. Routine reporting of suspicious activity was developed into steps for gathering, assessing and sharing terrorism-related information with a larger law enforcement and intelligence network. Through empirical analysis of counterterrorism efforts and recent scholarship on it, this chapter discusses prevention, preemption, and anticipation as three technologies of security, focusing on how each deals with uncertainty. The Suspicious Activity Reporting Initiative, this analysis suggests, is an anticipatory technology which constitutes police officers and intelligence analysts as subjects who work in a mode of uncertainty. (shrink)