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7 articles for “Malware”
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A LITERATURE REVIEW OF INTELLIGENCE INTRUSION DETECTION PREVENTION TECHNIQUES FOR UNKNOWN MALWARE FINDING
Abstract: Intrusion detection system (IDS) has played a vital role as a device to guard our networks from unknown malware attacks. However, since it still suffers from detecting an unknown attack, the ultimate challenge in intrusion detection field is how we can precisely identify such an attack. For identifying known malware various tools are available but that does not detect Unknown malware exactly. It will vary according to connectivity and using …
Published in International Journal of Digital Communication and Analog Signals · Vol. 1, Issue 2, 2015 · pp. 1–8 Read article
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OODA Principles for Finding Unknown Malware
Abstract: This paper is planned to give a model to "OODA Principles for Finding Unknown Malware", it portrays the state's general necessities with respect to the securing and execution of intrusion prevention and detection systems with intelligence (IIPS/IIDS). This is intended to give a more profound comprehension of interruption aversion and recognition standards with knowledge may be in charge of getting, actualizing or checking such frameworks in comprehension the innovation and …
Published in International Journal of Microelectronics and Digital integrated circuits · Vol. 1, Issue 2, 2015 · pp. 1–12 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Decision Trees in Intrusion Detection: A Comparative Analysis of Machine Learning Techniques
Abstract: Recent malware sophistication poses immense challenges in intrusion detection systems, making it imperative to develop highly sophisticated methodologies for detection of fast-evolving cyber threats. Traditional IDS typically employs a signature-based detection approach and has limitations in identifying unknown and obfuscated attacks, particularly zero-day vulnerabilities. This paper discusses the existing limitations of IDS based on ML techniques to further improve the detection capability of such systems. Machine learning algorithms, particularly decision …
Published in International Journal of Telecommunications & Emerging Technologies · Vol. 11, Issue 1, 2025 Read article
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Hybrid Machine Learning Model for EfficientBotnet Attack Detection in IoT Environment
Abstract: The rapid advancement and widespread adoption of internet technologies have led to a surge in cyber-attacks, with botnet attacks emerging as particularly detrimental. Identifying botnet activities is increasingly challenging due to the diverse attack vectors and the evolving nature of malware. As the Internet of Things (IoT) continues to expand, network devices become more vulnerable to these sophisticated attacks, resulting in significant security breaches and financial losses. In order to …
Published in International Journal of Broadband Cellular Communication · Vol. 11, Issue 2, 2026 · pp. 31–38 Read article
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Cloud Security Using Machine Learning: A Study
Abstract: The rapid adoption of cloud computing has revolutionized IT infrastructure, offering unprecedented scalability, flexibility, and cost efficiency. However, this paradigm shift introduces a new spectrum of sophisticated security challenges that traditional, signature-based security mechanisms often struggle to address effectively given the dynamic, multi-tenant, and distributed nature of cloud environments. This paper explores the critical role of Machine Learning (ML) as a potent paradigm for strengthening cloud infrastructure. ML emerges as …
Published in International Journal of Distributed Computing and Technology · Vol. 11, Issue 2, 2025 · pp. 21–30 Read article
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Progresses in Network Safety Worldview: A Survey
Abstract: This survey paper examines the different cautious models and mechanisms utilized so far in network safety. Digital protection is an exceptionally delicate issue, where advances are coordinated step by step. To effectively manage sophisticated attackers, it is necessary to cultivate strengths in the form of a cautious approach to the rapidly evolving landscape of malware and other attack methods. Specifically, digitization and data framework started a fight for strength on …
Published in International Journal of Software Computing and Testing · Vol. 8, Issue 2, 2022 · pp. 31–38 Read article