According to the different analysis methods, the intrusion detection is divided into anomaly detection and misuse detection. In this thesis, we analyze the advantages and drawbacks of detection technology, Due to that, this paper proposes a IDS model based on multi-technique with misuse detection and anomaly detection which can overcome their drawbacks and develop their advantages to detect the...

This paper presents a survey of techniques of intrusion detection system using supervised and unsupervised learning. The techniques are categorized based upon different approaches like Statistics, Data mining, Neural Network Based and Self Organizing Maps Based approaches. The detection type is borrowed from intrusion detection as either misuse detection or anomaly detection. It provides the re...

Today network security has become an everyday problem with virtually all computers connected to the Internet. Intrusion detection serves the important function of identifying malicious activities and determining their nature, origin, and seriousness. Inspired by the many excellent characteristics of biological immune System (BIS), the network intrusion detection system (NIDS) which based on art...

The rapid growth of computer networks has changed the prospect of network security. An easy accessibility condition causes computer networks to be vulnerable against numerous and potentially devastating threats from hackers. Up to the moment, researchers have developed Intrusion Detection Systems (IDS) capable of detecting attacks in several available environments. A boundlessness of methods fo...

There is often the need to update an installed Intrusion Detection System (IDS) due to new attack methods or upgraded computing environments. Since many current IDSs are constructed by manual encoding of expert security knowledge, changes to IDSs are expensive and slow. In this paper, we describe a data mining framework for adaptively building Intrusion Detection (ID) models. The central idea i...

Traditional intrusion detection systems can be broadly classified as misuse and anomaly detectors. Misuse detectors attempt detection by matching the current system/user activity against known signatures and patterns. As opposed to this, anomaly detection works by developing a reference graph and comparing the ongoing activity against it. Any significant deviation is flagged as an intrusion. An...

This paper describes the advantages of using the anomaly detection approach over the misuse detection technique in detecting unknown network intrusions or attacks. It also investigates the performance of various clustering algorithms when applied to anomaly detection. Five different clustering algorithms: k-Means, improved k-Means, k-Medoids, EM clustering and distance-based outlier detection a...

Initially, for each user, we obtain a profile. A system administrator assigns profiles in cases where allowable task vocabularies are known a priori. Otherwise, profiles are generated via relevance feedback recording schemes during an initial proper use period. Any potential misuse is then detected by comparing the new user queries against the user profile. The existing system requires a manual...

In today’s life Intrusion Detection System gain the attention, because of ability to detect the intrusion access efficiently and effectively as security is the major issue in networks. This system identifies attacks and reacts by generating alerts or blocking the unwanted data/traffic. Intrusion Detection System mainly classified as Anomaly based intrusion detection systems that have benefit of...

An intrusion detection system detects various malicious behaviors and abnormal activities that might harm security and trust of computer system. IDS operate either on host or network level via utilizing anomaly detection or misuse detection. Main problem is to correctly detect intruder attack against computer network. The key point of successful detection of intrusion is choice of proper featur...