A Network Attack Classification for Multi Agent System Using Flow Based Intusion Detection System

Abstract

Intrusion detection system is precious for protecting the entire network from a large range of threats, modern intrusion detection techniques must cope with not only increase the detection rate but also increase the speed of the network line, signature based ids forced to sample meagerly, increasing the probability of harmful traffic flowing towards the network without inspection subsequently, flow based id is attaining awareness as an effective complement. Basically ids are classified in to network based or host based The independent multi agent design idea is a scalable, striking option for its potential to influence the strengths of both architectures: the broad perspective and visibility into distributed harmful activity provided by network-based ID, and the complete view of the local node provided by host-based ID. This paper develops an architecture for an new multi agent, flow based intrusion detection system. The architecture is designed in two iterations of increasing complexity. These innovative designs are used to find nodes by “reputation “concept that are most effective for classifying harmful network activity. Every system has to design which includes the growth of an innovative classifier that uses multi point evolutionary algorithms to assist in the search for efficient functioning parameter values. Extensive agent simulation framework which highlights the condition under the Reputation System provides a Classification.

Authors and Affiliations

K. A. VarunKumar| Department of Computer Science and Engineering Vel Tech Dr.RR & Dr.SR Technical University, Chennai, S. Sibi Chakkaravarthy| Department of Computer Science and Engineering Vel Tech Dr.RR & Dr.SR Technical University, Chennai, G. Saravanan| Department of Information Technology, Department of Electrical & Electronics Engineering Vel Tech Dr. RR & Dr.SR Technical University, Chennai, V. Vetriselvan| Department of Information Technology, Department of Electrical & Electronics Engineering Vel Tech Dr. RR & Dr.SR Technical University, Chennai, P. Dharani| Department of Information Technology, Department of Electrical & Electronics Engineering Vel Tech Dr. RR & Dr.SR Technical University, Chennai, C. Aravind| Department of Information Technology, Department of Electrical & Electronics Engineering Vel Tech Dr. RR & Dr.SR Technical University, Chennai

Keywords

Related Articles

Liquidity Management and Profitability: A Case Study of Listed Manufacturing Companies in Sri Lanka

Liquidity management and profitability are very important issues in the growth and survival of business and the ability to handle the trade-off between the two a source of concern for financial managers.The study is a...

Performance Analysis of CP-OFDM under Different Fading Channels with Energy-Efficient Binary Power Control

The main idea behind OFDM is, the so called Multi Carrier Modulation (MCM) transmission technique. MCM is the principle of transmitting data by dividing the input bit stream into several parallel bit streams, each of...

An Efficient Resource Allocation in MIMO-OFDMA Networks

Most of the future wireless communication system applications are purely multimedia applications with the different Quality of Services (QoS) and these systems are required to support a variety of data rate for commun...

Applications of Artificial Neural Networks in Friction Stir Welding: A Review

This paper gives a review on the applications of artificial neural network in friction stir welding. Friction stir welding is a fairly new solid state joining process and has found numerous applications including in a...

Anomaly Based Intrusion Detection using Feature Relevance and Negative Selection Algorithm

With the increase in the use of internet, the job of malicious people has been made easy to exploit vulnerabilities in existing system. Intrusion Detection System (IDS) plays a major role in computer/network security...

Download PDF file
  • EP ID EP8488
  • DOI -
  • Views 409
  • Downloads 23

How To Cite

K. A. VarunKumar, S. Sibi Chakkaravarthy, G. Saravanan, V. Vetriselvan, P. Dharani, C. Aravind (2013). A Network Attack Classification for Multi Agent System Using Flow Based Intusion Detection System. The International Journal of Technological Exploration and Learning, 2(5), 181-187. https://europub.co.uk/articles/-A-8488