Industrial networks, due to communication convergence, face a growing exposure to cyber
threats, necessitating the need to address a wider range of threats, alongside their …

Contemporary software systems encompass a multitude of interconnected entities, often
accessible via the Web, making them susceptible to potential malicious activities. Intrusion …

This study investigates the efficacy of machine learning models, specifically Random Forest,
in anomaly detection systems when trained on complete flow records and tested on partial …

To address the issues of insufficient robustness, unstable features, and data noise
interference in existing network attack detection and identification models, this paper …

Intrusion detection systems (IDS) based on public datasets often show promising results in
academic papers but fail to perform effectively in real-world scenarios due to flaws in dataset …

In the Tech Renaissance, spotting network traffic anomalies has become a game changer
for security. With the rapid growth of network traffic and the increasing frequency of …

Continual Learning (CL) is crucial in artificial intelligence for systems to maintain relevance
and effectiveness by adapting to new data while retaining previously acquired knowledge …