Machine learning and data mining algorithms are increasingly being used in the intrusion detection systems (IDS), but their performances are laggard to some extent especially applied in network based intrusion detection: the larger load of network traffic monitoring requires more efficient algorithm in practice. In this paper, we propose and design an anomaly intrusion detection (AID) system ba...

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...

In order to overcome the separately selection advantages of traditional feature and RBF neural network parameter, increase accuracy rate of network’s intrusion detection, there came up with a research on neural network intrusion detection of improved particle swarm optimization. According to optimize the feature selection of network and RBF neural network parameter, established a neural network...

In this paper, we introduce new learning algorithms for reducing false positives in intrusion detection. It is based on decision tree-based attribute weighting with adaptive naïve Bayesian tree, which not only reduce the false positives (FP) at acceptable level, but also scale up the detection rates (DR) for different types of network intrusions. Due to the tremendous growth of network-based se...

Currently available products only provide some support in terms of Intrusion Prevention and Intrusion Detection, but they very much lack Intrusion Diagnosis features. We discuss the limitations of current Intrusion Detection System (IDS) technology, and propose a novel approach which we call Intrusion Detection & Diagnosis System (IDS) technology to overcome such limitations. The basic idea is ...

This paper analysis and criticizes the way of using, functioning the intrusion detection system in data mining. Understanding the techniques. data mining approach such as intrusion detection system using association datasets where as in event correlation data mining method we will maintain. traffic analysis and anomaly intrusion detection systems are needed. log data by using a knowledge discov...

Data mining for intrusion detection is one of the most cutting-edge researches which focus on network security, database, and information decision-making. Due to the emergence of new forms of attacks and intrusion on the network, we need a new intrusion detection system which would be able to detect new and unknown attacks. In the paper, by studying the characteristics of network data intrusion...

Intrusion detection for mobile ad hoc network (MANET) is a complex and difficult task due to the nature of the network. The mobile ad hoc network is vulnerable due to its features of open medium, cooperative algorithms, dynamic changing topology, lack of centralized monitoring and management point. The mobile ad hoc networks (MANETs) combine wireless communication with high degree of node mobil...

Assuring secure and reliable operation of networks has become a priority research area these days because of ever growing dependency on network technology. Intrusion detection systems (IDS) are used as the last line of defence. IDS identifies patterns of known intrusions (misuse detection) or differentiates anomalous network data from normal data (anomaly detection). In this paper, a novel Intr...

1. Intrusion detection is the process of monitoring the events occurring in a computer system or network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of computer security policies, acceptable use policies, or standard security practices. Intrusion prevention is the process of performing intrusion detection and attempting to stop detec...