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FI-IDS: A Federated Incremental Learning Approach for Intrusion Detection System

A Robust and Trustworthy Intrusion Detection System Using Adversarial Machine Learning and XAI

ADV-Sword: A Framework of Explainable AI-Guided Adversarial Samples Generation for Benchmarking ML-Based Intrusion Detection Systems

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…