Results for 'Malware'

32 found
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  1.  21
    Malware Detection using ML/DL.G. Prabhakar Raju - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (3):1-12.
    With the increasing reliance on digital technologies, cybersecurity threats, particularly malware, have become a major concern. Traditional malware detection methods, such as signature- based and heuristic approaches, struggle to detect sophisticated threats like zero-day attacks, polymorphic malware, and fileless malware. In response, this research explores the use of deep learning techniques to enhance malware detection accuracy, efficiency, and adaptability. Specifically, we investigate the effectiveness of convolutional neural networks (CNNs), autoencoders, and other architectures in classifying and (...)
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  2.  78
    Intelligent Malware Detection Empowered by Deep Learning for Cybersecurity Enhancement.M. Arulselvan - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):625-635.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)
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  3. Enhancing Malware Detection by Fusing Static and Dynamic Features Using Deep Neural Networks.Navas Garcia - manuscript
    Malware detection has been an ongoing challenge for cybersecurity experts due to the evolving nature of malicious software and the ability of malware to disguise itself. Traditional methods that rely solely on static features such as file signatures or dynamic analysis have had limitations in detecting new or obfuscated malware. This paper investigates the enhancement of malware detection by integrating both static and dynamic features and utilizing deep neural networks (DNNs) for more effective classification. By combining (...)
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  4.  61
    Comprehensive Detection of Malware and Trojans in Power Sector Software: Safeguarding Against Cyber Threats.A. Sai Lochan - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (11):1-14.
    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 (...)
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  5.  26
    Android Malware Detection.Thadukala Sai Kumar K. Kotteeswari, Amugadda Nitish Kumar, Nallagorla Ashok - 2025 - International Journal of Innovative Research in Science Engineering and Technology 14 (4):9318-9322.
    With the rapid expansion of Android-based mobile applications, ensuring user security and privacy has become a growing concern. Android's open-source nature and widespread adoption have made it a prime target for malware developers. Traditional malware detection approaches, such as signature-based and heuristic techniques, are increasingly insufficient against sophisticated and evolving threats. This project aims to develop an intelligent Android malware detection system using machine learning techniques to identify malicious applications based on behavioral and static features extracted from (...)
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  6.  97
    Real-Time Malware Detection Using Machine Learning Algorithms.Sharma Sidharth - 2017 - Journal of Artificial Intelligence and Cyber Security (Jaics) 1 (1):1-8.
    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 (...)
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  7.  80
    Revolutionizing Cybersecurity: Intelligent Malware Detection Through Deep Neural Networks.M. Sheik Dawood - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-666.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)
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  8. Empowering Cybersecurity with Intelligent Malware Detection Using Deep Learning Techniques.S. Yoheswari - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):655-665.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. (shrink)
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  9.  81
    MACHINE LEARNING ALGORITHMS FOR REALTIME MALWARE DETECTION.Sharma Sidharth - 2017 - Journal of Artificial Intelligence and Cyber Security (Jaics) 1 (1):12-16.
    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 (...)
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  10. Exploiting the In-Distribution Embedding Space with Deep Learning and Bayesian inference for Detection and Classification of an Out-of-Distribution Malware (Extended Abstract).Tosin Ige - forthcoming - Aaai Conference.
    Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow (...)
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  11. Impact of Variation in Vector Space on the performance of Machine and Deep Learning Models on an Out-of-Distribution malware attack Detection.Tosin Ige - forthcoming - Ieee Conference Proceeding.
    Several state-of-the-art machine and deep learning models in the mode of adversarial training, input transformation, self adaptive training, adversarial purification, zero-shot, one- shot, and few-shot meta learning had been proposed as a possible solution to an out-of-distribution problems by applying them to wide arrays of benchmark dataset across different research domains with varying degrees of performances, but investigating their performance on previously unseen out-of- distribution malware attack remains elusive. Having evaluated the poor performances of these state-of-the-art approaches in our (...)
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  12. Securing IoT Networks: Machine Learning-Based Malware Detection and Adaption.G. Ganesh - 2024 - International Journal of Engineering Innovations and Management Strategies 1 (5):1-16.
    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 (...)
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  13. Exploiting the In-Distribution Embedding Space with Deep Learning and Bayesian inference for Detection and Classification of an Out-of-Distribution Malware (Extended Abstract).Tosin ige, Christopher Kiekintveld & Aritran Piplai - forthcoming - Aaai Conferenece Proceeding.
    Current state-of-the-art out-of-distribution algorithm does not address the variation in dynamic and static behavior between malware variants from the same family as evidence in their poor performance against an out-of-distribution malware attack. We aims to address this limitation by: 1) exploitation of the in-dimensional embedding space between variants from the same malware family to account for all variations 2) exploitation of the inter-dimensional space between different malware family 3) building a deep learning-based model with a shallow (...)
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  14. A Novel Deep Learning-Based Framework for Intelligent Malware Detection in Cybersecurity.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):666-669.
    With the proliferation of sophisticated cyber threats, traditional malware detection techniques are becoming inadequate to ensure robust cybersecurity. This study explores the integration of deep learning (DL) techniques into malware detection systems to enhance their accuracy, scalability, and adaptability. By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid (...) datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis capabilities further improve response times, reducing the risk of potential damage. The study also incorporates visualization tools to provide interpretable insights into model decisions, enhancing transparency for cybersecurity practitioners. Concluding with a discussion on the challenges and future prospects, this research paves the way for scalable, AI-driven solutions to combat evolving cyber threats. (shrink)
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  15.  85
    Advanced Deep Learning Models for Proactive Malware Detection in Cybersecurity Systems.A. Manoj Prabharan - 2023 - Journal of Science Technology and Research (JSTAR) 5 (1):666-676.
    By leveraging convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, this research presents an intelligent malware detection framework capable of identifying both known and zero-day threats. The methodology involves feature extraction from static, dynamic, and hybrid malware datasets, followed by training DL models to classify malicious and benign software with high precision. A robust experimental setup evaluates the framework using benchmark malware datasets, yielding a 96% detection accuracy and demonstrating resilience against adversarial attacks. Real-time analysis (...)
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  16. A New Framework and Performance Assessment Method for Distributed Deep Neural NetworkBased Middleware for Cyberattack Detection in the Smart IoT Ecosystem.Tambi Varun Kumar - 2024 - International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering 11 (5):2283-2291.
    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 (...)
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  17. Handles for Pentesting Modern Secure Coding: bypassing mobile security.Mourad M. H. Henchiri - 2019 - International Journal of Engineering and Information Systems (IJEAIS) 3 (4):8-15.
    Abstract— Malware behavior was and still is a key solution, for top security appliances, to monitor algorithmic approaches when performing regular security tasks; scan, detection, cleaning and removal. And even for early actions; when building a security framework and securing all possible access points to all data sources. The first suspect in such scenario is the inner residents; appliances and system functions. Numerous are available at each operating system, and thus, the security is raised and set up frequently with (...)
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  18.  92
    Analysis and Identification of Malicious Applications.Mr Kamalakar - 2025 - International Journal of Engineering Innovations and Management Strategies 1 (10):1-12.
    The "Malware App Detection System" project enhances mobile security by detecting malicious apps through machine learning (ML) analysis of app behaviours. It examines behaviours like system calls (requests apps make to the operating system), permissions (levels of access apps have), and network connections (internet interactions). This focus on behaviour, rather than relying on specific malware signatures, enables the system to identify both known and new threats. The detection system is built using a dataset of both benign (harmless) and (...)
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  19. The unsustainable fragility of the digital, and what to do about it.Luciano Floridi - 2017 - Philosophy and Technology 30 (3):259-261.
    2017 saw the occurrence of two major IT disasters: the meltdown that plunged into chaos British Airways’ flights at Heathrow and Gatwick airports, and the WannaCry malware hit on Microsoft Windows systems. Incidents like these exemplify how fragile the digital is and how systemic the problems caused by digital failures can be. This paper explores some possible solutions to ensure that any damage caused by such failures is limited. These include redundancy, reflexivity and insurance, accountability, and collaboration.
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  20.  69
    Cybersecurity Frameworks in Guidewire Environments: Building Resilience in the Face of Evolving Threats.Ravi Teja Madhala Sateesh Reddy Adavelli - 2021 - International Journal of Innovative Research in Science, Engineering and Technology 10 (8):12040-12049.
    The digitization process has brought new opportunities in insurance industry operations and innovations but has also revealed major weaknesses. Since more and more actual insurers use Guidewire to handle claims, policies, and customer data, insurers become targets for cyber threats that target valuable information. The framework of Guidewire, along with cloud computing integrated API and third-party tools, is laden with numerous exposure points. These security gaps are utilized to execute phishing, spread malware and gain unauthorized access to customers and (...)
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  21. Attack Prevention in IoT through Hybrid Optimization Mechanism and Deep Learning Framework.Regonda Nagaraju, Jupeth Pentang, Shokhjakhon Abdufattokhov, Ricardo Fernando CosioBorda, N. Mageswari & G. Uganya - 2022 - Measurement: Sensors 24:100431.
    The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, (...)
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  22. Invasion of the Mind Snatchers. On memes and cultural parasites.Maarten Boudry - unknown
    In this commentary on Daniel Dennett's 'From Bacteria to Bach and Back', I make some suggestions to strengthen the meme concept, in particular the hypothesis of cultural parasitism. This is a notion that has both caused excitement among enthusiasts and raised the hackles of critics. Is the “meme” meme itself an annoying piece of malware, which has infected and corrupted the mind of an otherwise serious philosopher? Or is it an indispensable theoretical tool, as Dennett believes, which deserves to (...)
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  23. Analysis of Cyber Security In E-Governance Utilizing Blockchain Performance.Regonda Nagaraju, Selvanayaki Shanmugam, Sivaram Rajeyyagari, Jupeth Pentang, B. Kiran Bala, Arjun Subburaj & M. Z. M. Nomani - manuscript
    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 (...)
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  24. CareerBot: Advanced AI Mentorship for Students’ Career Aspirations and Planning.P. Selvaprasanth - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):610-620.
    The scope of the project encompasses the design, development, and implementation of AI-driven functionalities such as interest assessment, skill analysis, resume building, and personalized recommendations. The methodology involves data collection through user inputs, preprocessing of data for analysis, and the creation of a robust system architecture comprising frontend interfaces, backend servers, and database management. The implementation of the application involves a comprehensive technology stack, including Python for AI algorithms, TensorFlow for ML models, React.js for front end development, Flask for backend (...)
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  25.  76
    Enhancing Cybersecurity and Privacy using Artificial Intelligence: Trends and Future Directions of Research.V. Talati Dhruvitkumar - 2024 - International Journal of Innovative Research in Science, Engineering and Technology 13 (1):56-64.
    The speed with which cyber threats are evolving is calling for fresh approaches to enhance cybersecurity and protection of privacy. Artificial Intelligence (AI) is proving to be a revolutionary factor in enhancing cybersecurity, providing powerful capabilities for intrusion detection, malware detection, and privacy protection. This article outlines an in-depth review of the use of AI in cybersecurity with particular reference to future directions and trends in research. Applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) format, we (...)
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  26. Machine Learning-Based Intrusion Detection Framework for Detecting Security Attacks in Internet of Things.Jones Serena - manuscript
    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 (...)
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  27.  75
    Application Security in the OSI Model: Protecting the Application Layer from Emerging Threats.Bellamkonda Srikanth - 2023 - International Journal of Innovative Research in Science, Engineering and Technology 12 (5):8120-8126.
    The application layer of the Open Systems Interconnection (OSI) model plays a critical role in enabling end-user interaction with network services, making it a primary target for cybersecurity threats. This paper reviews contemporary research on securing the application layer, addressing emerging threats such as injection attacks, malware, phishing, and zero-day vulnerabilities. Key strategies, including secure development practices, application firewalls, cryptographic protocols, and user education, are explored to mitigate these risks. The challenges of an evolving threat landscape and resource constraints (...)
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  28.  91
    Enhancing Malicious Code Detection With Boosted N-Gram Analysis and Efficient Feature Selection.Nastooh Taheri Javan - 2024 - IEEE Access 12:147400-147421.
    A fundamental challenge in virology research lies in effectively detecting malicious code. N-gram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. (...)
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  29. CAREER GUIDANCE APPLICATION FOR STUDENTS – AI ASSISTED.K. Usharani - 2024 - Journal of Science Technology and Research (JSTAR) 5 (1):609-619.
    The rapid advancement of artificial intelligence (AI) technologies has revolutionized various industries, including the realm of education and career guidance. This project endeavors to harness the power of AI to develop a sophisticated career guidance application that offers personalized and effective recommendations to students and job seekers. The primary objective of this project is to address the limitations of traditional career guidance methods, which often lack customization and fail to adapt to individual preferences, skills, and aspirations. Through the integration of (...)
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  30.  96
    An investigation into the performances of the Current state-of-the-art Naive Bayes, Non-Bayesian and Deep Learning Based Classifier for Phishing Detection: A Survey. [REVIEW]Tosin Ige - manuscript
    Phishing is one of the most effective ways in which cybercriminals get sensitive details such as credentials for online banking, digital wallets, state secrets, and many more from potential victims. They do this by spamming users with malicious URLs with the sole purpose of tricking them into divulging sensitive information which is later used for various cybercrimes. In this research, we did a comprehensive review of current state-of-the-art machine learning and deep learning phishing detection techniques to expose their vulnerabilities and (...)
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  31. An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Survey. [REVIEW]Tosin Ige, Christopher Kiekintveld & Aritran Piplai - forthcoming - Proceedings of the IEEE:11.
    To secure computers and information systems from attackers taking advantage of vulnerabilities in the system to commit cybercrime, several methods have been proposed for real-time detection of vulnerabilities to improve security around information systems. Of all the proposed methods, machine learning had been the most effective method in securing a system with capabilities ranging from early detection of software vulnerabilities to real-time detection of ongoing compromise in a system. As there are different types of cyberattacks, each of the existing state-of-the-art (...)
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  32.  52
    ADB Debugging – Security Risks and Investigations.Dara Sai Ganesh R. Prathiba - 2025 - International Journal of Innovative Research in Science Engineering and Technology 14 (4):9343-9347.
    With the widespread adoption of mobile devices, Android Debug Bridge (ADB) has become a vital utility for developers and system administrators. However, its open nature presents significant security concerns when left unsecured. This project addresses the potential threats posed by improper ADB usage and introduces a custom-built forensic and security analysis tool designed for cyber security analysts and law enforcement professionals. Leveraging ADB commands, the tool extracts critical data such as logs, installed applications, and system information. On rooted devices, it (...)
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