This paper analysis and criticizes the way of using, functioning the intrusion detection system in data mining. Understanding the techniques. data mining approach such as intrusion detection system using association datasets where as in event correlation data mining method we will maintain. traffic analysis and anomaly intrusion detection systems are needed. log data by using a knowledge discov...

Intrusion-detection systems (IDS) which were essential in computer security because of difficulties in ensuring the information systems are security free. Literature has numerous intrusion detection approaches for network security. IDS efficiency was based on the ability to differentiate between normal and harmful activity. Hence, it becomes crucial to achieve better detection rates and lower f...

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 (IDS) aim at detecting attacks against computer systems and networks or, in general, against information systems. Its basic aim is to protect the system against malwares and unauthorized access of a network or a system. Intrusion Detection is of two types Network-IDS and Host BasedIDS. This paper covers the scope of both the types and their result analysis along with...

Intrusion detection for mobile ad hoc network (MANET) is a complex and difficult task due to the nature of the network. The mobile ad hoc network is vulnerable due to its features of open medium, cooperative algorithms, dynamic changing topology, lack of centralized monitoring and management point. The mobile ad hoc networks (MANETs) combine wireless communication with high degree of node mobil...

-Intrusion Detection System is used to detect the unwanted activities over the network and to design IDS soft computing techniques are used. This paper describes the role of Artificial Neural Network, Fuzzy Logic and Genetic Algorithm in Intrusion Detection System. The artificial neural network learning algorithms, data retrieval using fuzzy logic under uncertainty and power of Genetic algorith...

Intrusion Detection System (IDS) is a system, that monitors network traffic and tries to detect suspicious activity. In this paper we discuss the possibilities of application of clustering algorithms and Support Vector Machines (SVM) for use in the IDS. There we used K-means, FarthestFirst and COBWEB algorithms as clustering algorithms and SVM as classification SVM of type 1, known too as C-SVM...

A fundamental problem in intrusion detection is what metric(s) can be used to objectively evaluate an intrusion detection system (IDS) in terms of its ability to correctly classify events as normal or intrusion. In this paper, we provide an in-depth analysis of existing metrics. We argue that the lack of a single unified metric makes it difficult to fine tune and evaluate an IDS. The intrusion ...

The proposed framework applies two game theoretic models for economic deployment of intrusion detection system (IDS). The first scheme models and analyzes the interaction behaviors of between an attacker and intrusion detection agent within a non-cooperative game, and then the security risk value is derived from the mixed strategy Nash equilibrium. The second scheme uses the security risk value...

When system administrators monitor the security of computer network(s) they often use an intrusion detection system (IDS). The IDS examines events (in network traffic, operating systems, etc.) and raises an alarm if the events are believed to be symptoms of an intrusion. A number of studies investigate IDSs from a technical perspective. These studies typically investigate the technical quality ...