Function Of Intrusion Detection System Research Articles

Intrusion Detection System Using Back Propagation Algorithm and Compare its Performance with Self Organizing Map

<p>In recent years, internet and computers have been utilized by many people all over the world in several fields. On the other hand, network intrusion and information safety problems are ramifications of using internet. In this thesis it propose a new learning methodology towards developing a novel intrusion detection system (IDS) by back propagation neural networks (BPN) and self organizing map (SOM) and compare the performance between them. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviors of users, and then these behaviors will be considered an attack or a normal behavior. The proposed method can significantly reduce the training time required. Additionally, the training results are good. It provides a powerful tool to help supervisors and unsupervisors analyze, model and understand the complex attack behavior of electronic crime.</p><p><em>Journal of Advanced College of Engineering and Management, Vol. 1, 2015</em>, pp. 127-138</p>

Testing of IDS model using several intrusion detection tools

The aim of this work is to provide set of selected tests on IDS model that would enlarge the functionality of intrusion detection systems. Designed model is using several IDS, which allows it to investigate larger bandwidth and capture more attacks. This system consists of central master node and devices on which the intrusion detection systems are. The tests were designed with the attribute of repeatability and universality. They are divid- ed into five categories which explore specific attributes of intrusion detection system.

Kernelized Extreme Learning Machine with Levenberg-Marquardt Learning Approach towards Intrusion Detection

and system security is of vital importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS) are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. There are several techniques which exist at present to provide more security to the network, but most of these techniques are static. On the other hand, intrusion detection is a dynamic one, which can give dynamic protection to the network security by observing the attack. In recent times, Extreme Learning Machine (ELM) has been extensively applied to provide potential solutions for the IDS problem. But, the practicability of ELM is affected because of the complexity in choosing the suitable ELM parameters. Hence, in this paper sigma ( ) of the radial basis kernel function is tuned using Levenberg-Marquardt (LM) learning and proposed kernelized Extreme Learning Machine with LM. In order to obtain a converged solution, LM learning is utilized. The experiment is carried out with the help of WEKA by using KDD Cup 1999 dataset and the results indicate that the proposed technique can achieve higher detection rate, very low false alarm rate and to achieve high accuracy than the regular ELM algorithms. This method is used to decrease the space densisty of the data. KeywordsDetection System (IDS), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Kernel Function, Leave-One-Out (LOO), Kernel Partial Least Squares (K-PLS)

Network Intrusion Detection System Based On Machine Learning Algorithms

Network and system security is of paramount importance in the present data communication environment. Hackers and intruders can create many successful attempts to cause the crash of the networks and web services by unauthorized intrusion. New threats and associated solutions to prevent these threats are emerging together with the secured system evolution. Intrusion Detection Systems (IDS) are one of these solutions. The main function of Intrusion Detection System is to protect the resources from threats. It analyzes and predicts the behaviours of users, and then these behaviours will be considered an attack or a normal behaviour. We use Rough Set Theory (RST) and Support Vector Machine (SVM) to detect network intrusions. First, packets are captured from the network, RST is used to pre-process the data and reduce the dimensions. The features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments compare the results with Principal Component Analysis (PCA) and show RST and SVM schema could reduce the false positive rate and increase the accuracy.

ANOMALY DETECTION IN DISTRIBUTED COMPUTER COMMUNICATION SYSTEMS

Anomaly detection is a basic functionality of intrusion detection systems. The aim of such systems in distributed computer communication systems is to recognize and notify about various events that influence a system's security. In a gain to assure efficiency, flexibility, and a quality of detection of systems security violation in a distributed environment, required detection systems should be responsive, adaptive, proactive, and less centralized than those currently deployed. Such required properties are offered by agents and multiagent systems, i.e., agent-based technology has the continuously increasing potential to offer a solution to the growing problem of designing intelligent, efficient, and flexible management systems. An agent-based approach offers the potential to develop advanced and effective distributed, network-based strategies replacing traditional node-based approaches by more perspective network-based approaches. This article is devoted to present various architectures of anomaly detection systems, which may be implemented as multiagent systems supporting the classification of observed activities as normal or abnormal. Some simple example presents hierarchical architecture of a distributed anomaly detection system, which may be implemented in the form of a multiagent decision supporting system.