We study the containment of diffusion in a network immunization perspective, whose
solution also plays fundamental roles in scenarios such as the inference of rumor sources …

Fine-grained attack detection is an important network security task. A large number of
machine learning/deep learning (ML/DL) based algorithms have been proposed. However …

In recent years, there has been increasing interest in studying the adversarial attack, which
poses potential risks to deep learning applications and has stimulated numerous …

Deep learning (DL) technologies bring new threats to network security. Website
fingerprinting attacks (WFA) using DL models can distinguish victim's browsing activities …

Web server outages caused by a Distributed Denial of Service (DDoS) attacks have
increased considerably over the years. Intrusion Detection Systems (IDS) are not sufficient to …

Recent advancements in cloud computing have underscored the critical need for robust
security mechanisms to counter evolving cyber-threats. Traditional security solutions such as …

In this paper, we focus on the dynamics of the spread of malicious software (malware) in
multi-layer networks of various types, eg, cyber-physical systems. Recurring malware has …

Recent advancements in deep learning (DL) have introduced new security challenges in the
form of side-channel attacks. A prime example is the website fingerprinting attack (WFA) …

The threat hunting lifecycle is a complex atmosphere that requires special attention from
professionals to maintain security. This paper is a collection of recent work that gives a …

Learning discriminative features with adversarial behaviors can be extremely challenging to
build a robust learning model. This is partly evidenced by the difficulties in training robust …