Intrusion Detection systems are increasingly a key part of system defence. Various approaches to Intrusion Detection are currently being used but false alarm rate is higher in those approaches. Network Intrusion Detection involves differentiating the attacks like DOS, U2L, R2L and Probe from the Normal user on the internet. Due to the variety of network behaviors and the rapid development of at...

Intrusion Detection Systems are systems built to detect the unwanted attacks. Genetic Algorithm is a method that mimics the process of natural evolution; it was used to support the Intrusion Detection Systems. Genetic Algorithm contains several elements such as population size, evaluation, encoding, crossover, mutation, replacement and stopping criterion. Elements specifications must be determi...

The security of computer networks plays a strategic role in modern computer systems. In order to enforce high protection levels against threats, a number of software tools have been currently developed. Intrusion Detection Systems aim at detecting intruders who elude “first line” protection. In this paper, a pattern recognition approach to network intrusion detection based on the fusion of mult...

The goal of Distributed Intrusion Detection System is to analyze events on the network and identify attacks. The increasing number of network security related incidents makes it necessary for organizations to actively protect their sensitive data with the installation of intrusion detection systems (IDS). There is a difficulty to find intrusion in an distributed network segment from inside as w...

Traditional network-based intrusion detection systems using deep packet inspection are not feasible for modern high-speed networks due to slow processing and inability to read encrypted packet content. As an alternative to packetbased intrusion detection, researchers have focused on flow-based intrusion detection techniques. Flow-based intrusion detection systems analyze IP flow records for att...

In this paper we present a intrusion detection module capable of detecting malicious network traffic in a SCADA (Supervisory Control and Data Acquisition) system. Malicious data in a SCADA system disrupt its correct functioning and tamper with its normal operation. OCSVM (One-Class Support Vector Machine) is an intrusion detection mechanism that does not need any labeled data for training or an...

Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to increasing incidents of cyber attacks, building effecti...

Supervisory control and data acquisition (SCADA) systems monitor and control physical processes associated with the critical infrastructure. Weaknesses in the application layer protocols, however, leave SCADA networks vulnerable to attack. In response, cyber security researchers have developed myriad intrusion detection systems. Researchers primarily rely on unique threat models and the corresp...

The Snort intrusion detection system is a widely used and well-regarded open sourcesystem used for the detection of malicious activity in conventional wired networks. Recently, software patches to enable 802.11 wireless intrusion detection capability in Snort have been released. This paper focuses on the lessons learned from a live deployment of these wireless extensions to the Snort IDS. Gener...

Intrusion detection systems (IDSs) must meet the security goals while minimizing risks of wrong detections. In this paper, we study the issue of building a risk-sensitive intrusion detection mode1. To determinate whether a system calls sequence is normal or not, we consider not only the probability of this sequence belonging to normal sequences set or intrusion sequences set, but also the risk ...