Signature-based intrusion detection systems use a set of attack descriptions to analyze event streams, looking for evidence of malicious behavior. If the signatures are expressed in a well-defined language, it is possible to analyze the attack signatures and automatically generate events or series of events that conform to the attack descriptions. This approach has been used in tools whose goal...

Machine learning and data mining algorithms are increasingly being used in the intrusion detection systems (IDS), but their performances are laggard to some extent especially applied in network based intrusion detection: the larger load of network traffic monitoring requires more efficient algorithm in practice. In this paper, we propose and design an anomaly intrusion detection (AID) system ba...

This paper proposes an intrusion detection and prediction system based on uncertain and imprecise inference networks and its implementation. Giving a historic of sessions, it is about proposing a method of supervised learning doubled of a classifier permitting to extract the necessary knowledge in order to identify the presence or not of an intrusion in a session and in the positive case to rec...

The intrusion detection problem has been frequently studied, but intrusion detection methods are often based on a single point of view, which always limits the results. In this paper, we introduce a new intrusion detection model based on the combination of different current methods. First we use a notion of distance to unify the different methods. Second we combine these methods using the Pears...

Network security is one of the most important issues in the field of computer science. The network intrusion may bring disaster to the network users. It is therefore critical to monitor the network intrusion to prevent the computers from attacking. The intrusion pattern identification is the key point in the intrusion detection. The use of the support vector machine (SVM) can provide intelligen...

Intrusion detection is now a significant part in computer and network security. Various intrusion detection approaches are presented to secure the network, but the performance of the system is reduced. Thus, to improve the detection rates and decrease false alarm rates in intrusion detection is important. The crux of an efficient intrusion detection system is its ability to differentiate betwee...

Intrusion detection is an active research field in the development of reliable web-based information systems, where many artificial intelligence techniques are exploited to fit the specific application. Although some detection algorithms have been developed, they lack the adaptability to the frequently changing network environments, since they are mostly trained in batch mode. In this paper, we...

This chapter provides the overview of the state of the art in intrusion detection research. Intrusion detection systems are software and/or hardware components that monitor computer systems and analyze events occurring in them for signs of intrusions. Due to widespread diversity and complexity of computer infrastructures, it is difficult to provide a completely secure computer system. Therefore...

There exist many problems in intrusion detection systems such as large data volume, features and data redundancy which seriously affect the efficiency of the detection algorithm. Such problems need to be addressed in developing reliable intrusion detection systems. In this paper, we propose an intrusion detection model that combines Rough Set based Feature Selection Algorithm and Fuzzy SVM for ...

With the incredible expansion of network-based services and responsive information on networks, network protection and security is getting more and more significance than ever. Intrusion poses a serious security risk in network surroundings. The ever rising new intrusion or attacks type poses severe difficulties for their detection. The human labeling of the accessible network audit information...