The escalating frequency and sophistication of cyber threats pose significant challenges to
traditional intrusion detection methods. Signature-based misuse detection, hybrid detection …

In this study, we set up a scalable framework for large-scale data processing and analytics
using the big data framework. The popular classification methods are implemented, tuned …

The continued ability to detect malicious network intrusions has become an exercise in
scalability, in which data mining techniques are playing an increasingly important role. We …

With the frequent occurrence of cyber-security incidents, intrusion detection system (IDS)
has been payed more and more attention recently. However, detecting attacks from traffic …

Anomaly detection and multi-attack classification are major concerns for cyber defense.
Several publicly available datasets have been used extensively for the evaluation of …

Intrusion detection systems (IDSs) are intrinsically linked to a comprehensive solution of
cyberattacks prevention instruments. To achieve a higher detection rate, the ability to design …

In today's digital world, the tremendous amount of data poses a significant challenge to
cyber security. The complexity of cyber-attacks makes it difficult to develop efficient tools to …

In network intrusion detection research, two characteristics are generally considered vital to
building efficient intrusion detection systems (IDSs): an optimal feature selection technique …

Machine learning has been leveraged to increase the effectiveness of intrusion detection
systems (IDSs). The focus of this approach, however, has largely be on detecting known …

An intrusion detection system is a security system that aims to detect sabotage and
intrusions on networks to inform experts of the attack and abuse of the network. Different …