One of the main problems of existing approaches in anomaly detection in intrusion detection system (IDS) is that IDSs provide only binary detection result: intrusion (attack) or normal. If some attack data or normal data is belonged to boundary, they may be classified wrongly. That is a main cause of high false rates and inaccurate detection rates in IDS. We propose a new approach named Quantit...

intrusion detection system (IDS) is a device or software application that Li et al (2) proposed a fuzzy multi-class support vector machine algorithm, denoted. Detection System. Multiclass SVM, Neural Network, Support Vector Machine. network and (2) network based intrusion detection system which analyzes. of network intrusion detection systems for struggling progressively leading state-of-the ar...

With the increased usage of computer networks, security becomes a critical issue. Recently, data mining methods have gained lot of attention in addressing network security issues, including intrusion detection. Consequently, unsupervised learning methods have been given much importance for anomaly based network intrusion detection. In this paper, we investigate new clustering algorithms like fa...

Intrusion detection is an important security tool. It has the possibility to provide valuable information about the current status of security. However, as enterprises deploy multiple intrusion detection sensors at key points in their networks, the issue of correlating messages from these sensors becomes increasingly important. A correlation capability reduces alert volume, and potentially impr...

Timing attacks are a challenge for current intrusion detection solutions. Timing attacks are dangerous for web applications because they may leak information about side channel vulnerabilities. This paper presents a massive-multi-sensor zeroconfiguration Intrusion Detection System that is especially good at detecting timing attacks. Unlike current solutions, the proposed Intrusion Detection Sys...

Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques tha...

Recently, research on intrusion detection in computer systems has received much attention to the computational intelligence society. Many intelligence learning algorithms applied to the huge volume of complex and dynamic dataset for the construction of efficient intrusion detection systems (IDSs). Despite of many advances that have been achieved in existing IDSs, there are still some difficulti...

Intrusion detection and response has traditionally been performed at the network and host levels That is intrusion monitors will typically analyze network packet logs or host machine audit logs for signs of intrusion activity More often than not commercial o the shelf COTS intrusion detection tools use ngerprints of known intrusions to detect their presence in these audit trails Both these appr...

Intrusion Detection System plays a reasonable supplementary role for the firewall in the network security. It can help protect computers from network attacks and improve the security and reliability of the computer. At present intrusion detection system analysis module uses the pattern matching technology. In this article an optimized algorithm was proposed through analyzing the advantages and ...

Today we are suffering from many problems because of intruder interference in our communication with other person/organisation. We need a very safe and secure intrusion detection system. So, intrusion detection has become an important area of research The existing systems are not completely flawless and secure. So, there is the need to improve the existing system. In this paper, firstly we are ...