Data-driven network intrusion detection (NID) has a tendency towards minority attack
classes compared to normal traffic. Many datasets are collected in simulated environments …

Billions of IoT devices lacking proper security mechanisms have been manufactured and
deployed for the last years, and more will come with the development of Beyond 5G …

In order to provide an accurate and timely response to different types of the attacks, intrusion
and anomaly detection systems collect and analyze a lot of data that may include personal …

Intrusion detection systems (IDS) have already demonstrated their effectiveness in detecting
various attacks in cellular vehicle-to-everything (C-V2X) networks, especially when using …

Machine Learning (ML) techniques have been widely applied for network intrusion
detection. However, existing ML-based network intrusion detection systems (NIDSs) suffer …

Network intrusion detection is a crucial task since malicious traffic occurs every second
these days. Various research has been studied in this field and shows high performance …

Managed security service providers increasingly rely on machine-learning methods to
exceed traditional, signature-based threat detection and classification methods. As machine …

With the advantage of analyzing data of multiple work sites comprehensively while ensuring
data privacy, federated learning-based intrusion detection systems (IDS) are emerging as a …

Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to
an explosive utilization of intelligent devices. Notably, such solutions are especially …

Current intrusion detection systems, which rely on signature-based detection using rules
derived from the inspection of past traffic flows and their signatures, are incapable of …