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

With the rapidly evolving technological landscape, the huge development of the Internet of
Things, and the embracing of digital transformation, the world is witnessing an explosion in …

Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …

Concept drift is one of the most frustrating challenges for learning-based security
applications built on the closeworld assumption of identical distribution between training and …

Lateral movement is a key stage of system compromise used by advanced persistent
threats. Detecting it is no simple task. When network host logs are abstracted into discrete …

In the rapidly changing cybersecurity landscape, threat hunting has become a critical
proactive defense against sophisticated cyber threats. While traditional security measures …

Unsupervised Deep Learning (DL) techniques have been widely used in various security-
related anomaly detection applications, owing to the great promise of being able to detect …

Host-based threats such as Program Attack, Malware Implantation, and Advanced Persistent
Threats (APT), are commonly adopted by modern attackers. Recent studies propose …

Machine Learning (ML) has become a valuable asset to solve many real-world tasks. For
Network Intrusion Detection (NID), however, scientific advances in ML are still seen with …

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