Anomaly detection has been used for decades to identify and extract anomalous
components from data. Many techniques have been used to detect anomalies. One of the …

This study investigates the performance of two open source intrusion detection systems
(IDSs) namely Snort and Suricata for accurately detecting the malicious traffic on computer …

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT)
systems are causing a wide range of problems for individuals and organizations. The IoT is …

Abstract Machine-learning techniques have been actively employed to information security
in recent years. Traditional rule-based security solutions are vulnerable to advanced attacks …

Intrusion detection systems are widely implemented to protect computer networks from
threats. To identify unknown attacks, many machine learning algorithms like neural networks …

The KDD CUP 1999 intrusion detection dataset was introduced at the third international
knowledge discovery and data mining tools competition, and it has been widely used for …

Recent advancements in software-defined networking (SDN) make it possible to overcome
the management challenges of traditional networks by logically centralizing the control …

Nowadays, we see more and more cyber-attacks on major Internet sites and enterprise
networks. Intrusion Detection System (IDS) is a critical component of such infrastructure …

Intrusion detection system (IDS) is an important component to maintain network security. As
network applications grow rapidly, network security mechanisms require more attention to …

To safeguard critical services and assets in a distributed environment, collaborative intrusion
detection systems (CIDSs) are usually adopted to share necessary data and information …