This paper presents our statistical based intrusion detection framework for computer networks. This framework uses the six sigma technique to identify the thresholds for the critical network parameters. With the help of raw network data, the thresholds identified are used to differentiate normal, uncertain and abnormal behavior due to network intrusion. This is then used for efficient detection...

The goal of the article is to presents intrusion detections systems and design architecture of intrusion detection based on neural network self organizing map. In the report is described base problematic of neural network and intrusion detection system. The article further deals with specific design of intrusion detection architecture based on user anomaly behavior. A core of the designed archi...

Intrusion behavior has the characteristics of fast upgrade, strong concealment In this paper, an integrated network intrusion detection algorithm by combining. judge the safety of a system or network. (6). Intrusion Detection Systems (IDS) are primarily centered on characteristic probable incidents, observation data. ABSTRACT: Intrusion Detection Systems are designed to detect system attacks Re...

Intrusion Detection Systems (IDSs) detects the network factor for traditional SNORT (Network Based Intrusion. Detection Using Genetic Algorithms for intrusion detection has proven to be an implementation method. Section IV. Designing of On Line Intrusion Detection System Using Rough Set Theory and Q Evaluation of Rough Set Theory Based Network Traffic Data Classifier Using Traffic Data Classifi...

To improve network security different steps has been taken as size and importance of the network has increases day by day. Then chances of a network attacks increases Network is mainly attacked by some intrusions that are identified by network intrusion detection system. These intrusions are mainly present in data packets and each packet has to scan for its detection. This paper works to develo...

security term in mobile ad hoc networks has several aspects because of the special specification of these networks. in this paper a distributed architecture was proposed in which each node performed intrusion detection based on its own and its neighbors’ data. fuzzy-neural interface was used that is the composition of learning ability of neural network and fuzzy ratiocination of fuzzy system as...

Computer and network security has received and will still receive much attention. Any unexpected intrusion will damage the network. It is therefore imperative to detect the network intrusion to ensure the normal operation of the internet. There are many studies in the intrusion detection and intrusion patter recognition. The artificial neural network (ANN) has proven to be powerful for the intr...

A distributed network intrusion detection system (IDS) called SA-NIDS is proposed based on the network-based intrusion detection architecture. It includes three basic components, Local Intrusion Detection Monitor (LIDM), Global Intrusion Detection Controller (GIDC), and Surveillance Agent (SA). Basically, the LIDM is used to do packets capturing, packets de-multiplexing, local intrusion detecti...

In this paper, we present a new learning algorithm for anomaly based network intrusion detection using improved self adaptive naïve Bayesian tree (NBTree), which induces a hybrid of decision tree and naïve Bayesian classifier. The proposed approach scales up the balance detections for different attack types and keeps the false positives at acceptable level in intrusion detection. In complex and...