Increasing network intrusion becoming crucial problem in security infrastructures. Data mining techniques have been successfully applied in many different fields including insolvency prediction, churn prediction, marketing, process control, fraud detection, and network management. Today number of research projects is using data mining for intrusion detection system (IDS) and prevention. The goa...

In this paper, we enhance the functionalities of Snort network-based intrusion detection system to automatically generate patterns of misuse from attack data, and the ability of detecting sequential intrusion behaviors. To that, we implement an intrusion pattern discovery module which applies data mining technique to extract single intrusion patterns and sequential intrusion patterns from a col...

Artificial Neural Networks (ANNs), especially back-propagation (BP) neural network, can improve the performance of intrusion detection systems. However, for the current network intrusion detection methods, the detection precision, especially for lowfrequent attacks, detection stability and training time are still needed to be enhanced. In this paper, a new model which based on optimized BP neur...

With the rapid development and comprehensive application of network technology, network security problems gradually appear serious. Traditional firewall technologies can’t provide sufficient security protection against various attacks and intrusions (Anderson, 1980), while intrusion detection systems (IDS) are faced with compromise between false alarms and false positives (Denning, 1987). In th...

Problem statement: Past few years have witnessed a growing recognition of intelligent techniques for the construction of efficient and reliable Intrusion Detection Systems (IDS). Many methods and techniques were used for modeling the IDS, but some of them contribute little or not to resolve it. Approach: Intrusion detection system for local area network by using Support Vector Machines (SVM) wa...

Ad hoc wireless network with their changing topology and distributed nature are more prone to intruders. The network monitoring functionality should be in operation as long as the network exists with nil constraints. The efficiency of an Intrusion detection system in the case of an ad hoc network is not only determined by its dynamicity in monitoring but also in its flexibility in utilizing the...

Intrusion detection is the method of identifying intrusions or misuses in a computer network, which compromise the confidentiality and integrity of the network. Intrusion Detection System (IDS) is a security tool used to monitor network traffic and detect unauthorized activities in the network [23, 28, 30]. A security monitoring surveillance system, which is an intrusion detectionmodel based on...

The two important problems of collaborative intrusion detection are aggregation and correlation of intrusion events. The enormous amount of data generated by detection probes requires significant network and computational capacity to be processed. In this article we show that a distributed hash table based approach can reduce both network and computational load of intrusion detection, while pro...

Sensors in a wireless sensor network depend on their neighbours to route their messages. Yet, routing protocols in wireless sensor network are vulnerable to different types of attacks. In this paper, we consider the wormhole attack in which the adversary diverts traffic from one part of the network to another part by introducing a low cost tunnel between the two parts. We introduce a distribute...

This paper introduces a method of distributed network intrusion detection which scales with the number of computers on a network and is tunable (the probability of detection can be traded off against overhead). Experiments with real network traffic show that the system has high detection rates for several common routes of intrusion, and low false-positive rates under normal behavior. The method...