The increased reliance on network has made it necessary to increase the security and privacy of the data across the network .One of the major threats in such a situation is of intruders who try to attack the network system. The main focus is to prepare the network against such attacks. In this paper, we present layered framework integrated with neural network to build an effective intrusion detection system. This system has experimented with Knowledge Discovery & Data Mining (KDD) 1999 dataset. Neuroph studio has been used to train the neurons. The results show that the proposed system has high attack detection accuracy and less false alarm rate.

 

Published In : IJCSN Journal Volume 4, Issue 2

Date of Publication : April 2015

Pages : 281 - 284

Figures : 08

Tables : --

Publication Link : Intrusion Detection System Integrating Layered Framework with Neural Network

 

 

 

Sajil Eruvenkai : Sinhgad Institute of Technology and Science, Pune-41, India

SSwati Tandale : Sinhgad Institute of Technology and Science, Pune-41, India

Chaitali Deochake : Sinhgad Institute of Technology and Science, Pune-41, India

Sayali Laigude : GSinhgad Institute of Technology and Science, Pune-41, India

 

 

 

 

 

 

 

IDS

neural network

layered framework

KDD cup99 dataset

In this paper, intrusion detection system is designed by integrating layered framework with neural network. From practical point of view, the experimental results imply that there is still scope of improvement as the proposed systems are not able to detect all types of attacks, thus it is interesting to investigate in this direction.

 

 

 

 

 

 

 

 

 

[1] Novel Intrusion Detection System integrating Layered Framework with Neural Network 2013 3rd IEEE. [2] Getting started with neuroph by Zoran Sevarac and Marko Koprivica. [3] A. Ghosh, A. Schwartzbard, "A study in using Neural Networks for Anomaly and Misuse Detection," Proceedings of the 8th USENIX Security Symposium, 1999.