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This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse…

Most intrusion detection systems still identify attacks only after significant damage has occurred, detecting late-stage tactics rather than early indicators of compromise. This paper introduces a temporal analysis framework and taxonomy…

With the increasing amount of reliance on digital data and computer networks by corporations and the public in general, the occurrence of cyber attacks has become a great threat to the normal functioning of our society. Intrusion detection…

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems (IDS) has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated…

The Internet has become a prime subject to security attacks and intrusions by attackers. These attacks can lead to system malfunction, network breakdown, data corruption or theft. A network intrusion detection system (IDS) is a tool used…

A Network Intrusion Detection System (NIDS) is a network security technology for detecting intruder attacks. However, it produces a great amount of low-level alerts which makes the analysis difficult, especially to construct the attack…

Graph Neural Networks (GNNs) have garnered intensive attention for Network Intrusion Detection System (NIDS) due to their suitability for representing the network traffic flows. However, most present GNN-based methods for NIDS are…

The detection of network flows that send excessive amounts of traffic is of increasing importance to enforce QoS and to counter DDoS attacks. Large-flow detection has been previously explored, but the proposed approaches can be used on…

Network Intrusion Detection Systems (NIDS) have been extensively investigated by monitoring real network traffic and analyzing suspicious activities. However, there are limitations in detecting specific types of attacks with NIDS, such as…

Open set recognition requires a classifier to detect samples not belonging to any of the classes in its training set. Existing methods fit a probability distribution to the training samples on their embedding space and detect outliers…

Detecting Zero-Day intrusions has been the goal of Cybersecurity, especially intrusion detection for a long time. Machine learning is believed to be the promising methodology to solve that problem, numerous models have been proposed but a…

As the most competitive solution for next-generation network, software-defined network (SDN) and its dominant implementation OpenFlow, are attracting more and more interests. But besides convenience and flexibility, SDN/OpenFlow also…

Deep learning (DL)-based Network Intrusion Detection System (NIDS) has demonstrated great promise in detecting malicious network traffic. However, they face significant security risks due to their vulnerability to adversarial examples…

In critical IoT environments, such as smart homes and industrial systems, effective Intrusion Detection Systems (IDS) are essential for ensuring security. However, developing robust IDS solutions remains a significant challenge. Traditional…

Machine Learning (ML)-based network intrusion detection systems bring many benefits for enhancing the cybersecurity posture of an organisation. Many systems have been designed and developed in the research community, often achieving a close…

Network Intrusion Detection Systems (NIDS) are essential for protecting computer networks from malicious activities, including Denial of Service (DoS), Probing, User-to-Root (U2R), and Remote-to-Local (R2L) attacks. Without effective NIDS,…

In the recent years, cybersecurity has gained high relevance, converting the detection of attacks or intrusions into a key task. In fact, a small breach in a system, application, or network, can cause huge damage for the companies. However,…

When concept drift is detected during classification in a data stream, a common remedy is to retrain a framework's classifier. However, this loses useful information if the classifier has learnt the current concept well, and this concept…

Deep learners tend to perform well when trained under the closed set assumption but struggle when deployed under open set conditions. This motivates the field of Open Set Recognition in which we seek to give deep learners the ability to…

Insider Attack Detection in commercial networks is a critical problem that does not have any good solutions at this current time. The problem is challenging due to the lack of visibility into live networks and a lack of a standard feature…