1 năm trước
Malware threatens cybersecurity by enabling data theft, unauthorized access, and extortion. Traditional malware detection systems (MDS) struggle with the increasing volume and complexity of malware. While machine learning (ML) and deep learning (DL) offer promising solutions, they remain vulnerable to adversarial attacks that evade detection. Recent research focuses on developing…
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The advancement of software vulnerability detection tools has accelerated in recent years, yet the prevalence and severity of vulnerabilities continue to escalate, posing significant threats to computer security and information safety. To address this, numerous detection methodologies have been proposed, with machine learning-based approaches demonstrating notable promise. In this paper,…
Đọc tiếp >>>2 năm trước
Detecting malware on Android remains a major challenge because malicious apps use sophisticated evasion techniques. This study presents RAX-ClaMal, a novel approach leveraging dynamic analysis of RAX (Register a Extended) register values for Android malware detection. By extracting and examining the RAX register in the data sections from Dalvik Executable…
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The cybersecurity landscape is witnessing an increasing prevalence of threats and malicious programs, posing formidable challenges to conventional detection techniques. Although machine learning (ML) and deep learning (DL) have demonstrated effectiveness in malware detection, their susceptibility to adversarial attacks has led to a growing research trend. This study aims to…
Đọc tiếp >>>2 năm trước
The application of machine learning and deep learning to intrusion detection systems (IDSs) enhances their ability to detect and respond to sophisticated cyber threats efficiently and effectively, providing a robust defense mechanism in the ever-evolving landscape of cybersecurity. However, many environments where IDSs are deployed, such as IoT devices or…
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