As cyber threats continue to evolve, ensuring network security has become increasingly
critical. Deep learning-based intrusion detection systems (IDS) are crucial for addressing …

Nowadays, most network traffic is encrypted, which protects user privacy but hides attack
traces, further hindering identifying attacks to inspect traffic packages. Machine Learning …

This paper studies the problem of detecting anomalous graphs using a machine learning
model trained on only normal graphs, which has many applications in molecule, biology …

The intrusion detection schemes (IDSs) based on the Gradient Boosting Decision Tree
(GBDT) face three problems: unbalanced training data distribution, large dimensionality of …

Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent
mechanisms for actively detecting fresh attacks over a complex network. Although review …

With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …

In this paper, we delve into the transformative role of pre-trained language models (PLMs) in
cybersecurity, offering a comprehensive examination of their deployment across a wide …

In parallel with the rapid adoption of transport layer security (TLS), malware has utilized the
encrypted communication channel provided by TLS to hinder detection from network traffic …

Digitalization is continuing facilitating our daily lives. The world is interconnected as never
before, bringing close people, businesses, or other organizations. However, hackers are …

With the rapid development of the Internet, network security issues have become
increasingly prominent, and the traditional rule-based and signature-based intrusion …