Today's anomaly-based network intrusion detection systems (IDSs) are plagued with detecting new and unknown attacks. The review of the literature builds ideas for researching the problem of detecting these attacks using multi-layered feed forward neural network (MLFFNN) IDSs. The scope of the paper focused on a review of the literature from primarily 2008 to the present found in peer-review and...

Computer intrusion detection is to do with identifying computer activities that may compromise the integrity, confidentiality or the availability of an IT system. Anomaly Intrusion Detection Systems (IDSs) aim at distinguishing an abnormal activity from an ordinary one. However, even in a moderate site, computer activity very quickly yields Giga-bytes of information, overwhelming current IDSs. ...

It is important for intrusion detection systems (IDSs) to share information in order to discover attacks involving multiple sites. However, no framework exists for an IDS to request from and send to another IDS data relevant to specific events. The lack of such a framework may result in a waste of processing time, storage capacity and network bandwidth. This paper proposes a formal framework mo...

Intrusion detection systems (IDS) help detect unauthorized activities or intrusions that may compromise the confidentiality, integrity or availability of a resource. This paper presents a general overview of IDSs, the way they are classified and the different algorithms used to detect anomalous activities. It also describes the various approaches and the importance of IDSs in information security.

Intrusion detection is one of the important aspects in computer security. Many commercial intrusion detection systems (IDSs) are available and are widely used by organizations. However, most of them suffer from the problem of high false alarm rate, which added heavy workload to security officers who are responsible for handling the alarms. In this paper, we propose a new method to reduce the nu...

Intrusion Detection Systems (IDSs) are used to monitor computer systems for signs of security violations. Having detected such signs, IDSs trigger alerts to report them. These alerts are presented to a human analyst, who evaluates them and initiates an adequate response. In practice, IDSs have been observed to trigger thousands of alerts per day, most of which are mistakenly triggered by benign...

An adaptive intrusion detection algorithm which combines the Adaptive Resonance Theory(ART) with the Concept Vector and the Mecer-Kernel is presented. Compared to the supervisedand the clustering-based Intrusion Detection Systems(IDSs), our algorithm can detect unknown types of intrusions in on-line by generating clusters incrementally.

This paper investigates the suitability of linear genetic programming (LGP) technique to model efficient intrusion detection systems, while comparing its performance with artificial neural networks and support vector machines. Due to increasing incidents of cyber attacks and, building effective intrusion detection systems (IDSs) are essential for protecting information systems security, and yet...

Intrusion detection systems (IDSs) have become a critical part of security systems. The goal of an intrusion detection system is to identify intrusion effectively and accurately. However, the performance of misuse intrusion detection system (MIDS) or anomaly intrusion detection system (AIDS) is not satisfying. In this paper, we study the issue of building a compound intrusion detection model, w...

Intrusion Detection System (IDS) due to novel attack method or upgraded. Because many current IDSs are constructing by point instruction of professional knowledge, changes to IDSs are costly and slow. Intrusion detection techniques can be categorize into irregularity detection and mistreat detection. Anomaly detection systems, for example, IDES Intrusion detection systems (IDS) process large am...