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A Trustworthy and Robust Model for Mutated Malware Detection via Explainable AI and GANs

Raiju: Reinforcement Learning-Guided Post-Exploitation for Automating Security Assessment of Network Systems

To discover threats to a network system, investigating the behaviors of attackers after successful exploitation is an important phase, called post-exploitation. Although various efficient…

Defect-Scanner: A Comparative Empirical Study on Language Model and Deep Learning Approach for Software Vulnerability Detection

The complex and rapidly evolving nature of modern software landscapes introduces challenges such as increasingly sophisticated cyber threats, the diversity in programming languages and…

A study on adversarial sample resistance and defense mechanism for multimodal learning-based phishing website detection

Recent advancements in Artificial Intelligence (AI) have greatly impacted cybersecurity, particularly in detecting phishing websites. Traditional methods struggle to address evolving vulnerabilities, but research…

A multimodal Windows malware detection method based on hybrid analysis and graph representations

Towards Transparent Spam Detection: SentinelCall – A Distributed Ledger Solution for Call Filtering

The proliferation of connectivity through modern telecommunications has led to increased unwanted and disruptive calls. Such communications negatively impact user experience and trust in…

An empirical study on the impact of graph representations for code vulnerability detection using graph learning

A Coverage-guided Fuzzing Method for Automatic Software Vulnerability Detection using Reinforcement Learning-enabled Multi-Level Input Mutation

Fuzzing is a popular and effective software testing technique that automatically generates or modifies inputs to test the stability and vulnerabilities of a software…

A Study on Natural Language Processing-Based Method for Windows Malware Detection