Malware detection has become a major concern due to the increasing number and
complexity of malware. Traditional detection methods based on signatures and heuristics …

Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …

The advantages of graph neural networks (GNNs) in leveraging the graph topology of
wireless networks have drawn increasing attentions. This paper studies the GNN-based …

Graph Neural Networks (GNNs) demonstrate their significance by effectively modeling
complex interrelationships within graph-structured data. To enhance the credibility and …

This paper introduces a new class of explanation structures, called robust counterfactual
witnesses (RCWs), to provide robust, both counterfactual and factual explanations for graph …

Network intrusion detection technology has always been an indispensable protection
mechanism for industrial network security. The rise of new forms of network attacks has …

In the dynamic and rapidly evolving world of social media, detecting anomalous users has
become a crucial task to address malicious activities such as misinformation and …

Industrial control systems (ICSs) are crucial in managing critical infrastructure, making their
security a paramount concern. In recent years, their widespread adoption, together with the …

The use of artificial intelligence, especially Graph Neural Network (GNN), in solving cyber
security issues brings challenges. We address three challenges of GNN for botnet detection …

As network sizes grow, attack schemes not only become more varied but also increase in
complexity. This diversification leads to a proliferation of attack variants, complicating the …