A unified method for statistical anomaly detection in intrusion detection systems is theoretically introduced. It is based on estimating a dispersion measure of numerical or symbolic data on successive moving windows in time and finding the times when a relative change of the dispersion measure is significant. Appropriate dispersion measures, relative differences, moving windows, as well as tec...

Bio: Christopher Wee is a Researcher in the Computer Security Group at the University of California, Davis, Computer Science Department. His current research interests are intrusion detection, security auditing and Formal policy specifications. Chris has participated in three intrusion detection systems projects: DIDS, a LAN intrusion detection system, GrIDS: a wide-area IDS, and LAFS: a file-s...

Intrusion detection systems are designed to provide security in computer networks, so that if the attacker crosses other security devices, they can detect and prevent the attack process. One of the most essential challenges in designing these systems is the so called curse of dimensionality. Therefore, in order to obtain satisfactory performance in these systems we have to take advantage of app...

In this paper we discuss the main research approaches in the development of automated and systematic methods for intrusion detection. In this critical overview, the key concept underlying the presented intrusion detection systems is that they involve pattern analysis techniques to discover consistent and useful patterns of system features that describe program and user behaviour, and the set of...

Intrusion Detection Systems (IDS) are now becoming one of burning issue for any organization’s network. Intruders always search for vulnerabilities or flaws in target system and attack using different techniques. An intrusion detection system (IDS) is needed to detect and respond effectively whenever the confidentiality, integrity, and availability of computer resources are under attack. Today,...

With the thriving technology and the great increase in the usage of computer networks, the risk of having these network to be under attacks have been increased. Number of techniques have been created and designed to help in detecting and/or preventing such attacks. One common technique is the use of Intrusion Detection Systems (IDS). Today, number of open sources and commercial IDS are availabl...

Intrusion detection systems (IDSs) are one of the promising tools for protecting data and networks; many classification algorithms, such as neural network (NN), Naive Bayes (NB), decision tree (DT), support vector machine (SVM) have been used IDS in last decades. However, these classifiers is not working well if they applied alone without any other algorithms that can tune parameters or choose ...

Due to the increasing cyber-attacks, various Intrusion Detection Systems (IDSs) have been proposed identify network anomalies. Most existing machine learning-based IDSs learn patterns from features extracted traffic flows, and deep approaches can data distribution raw differentiate normal anomalous flows. Although having used in real world widely, above methods are vulnerable some types of atta...

INTRUSION Detection systems are now an essential component in the overall network and data security arsenal. With the rapid advancement in the network technologies including higher bandwidths and ease of connectivity of wireless and mobile devices, the focus of intrusion detection has shifted from simple signature matching approaches to detecting attacks based on analyzing contextual informatio...

It is a known fact that computer and network systems have certain design flaws which leads to security hazards. Intruders can exploit the security flaws and break the computer systems, and is very expensive and sometimes nearly impossible to fix all the design and programming errors. This suggests that prevention-based approaches are no more reliable and hence intrusion detection is needed as a...