No Detail Found!

New Anomaly Network Intrusion Detection System in Cloud Environment Based on Optimized Back Propagation Neural Network using Improved Genetic Algorithm.

Author(s)
Zouhair Chiba LIMSAD Labs, Faculty of Sciences, Hassan II University of Casablanca, 20100, Casablanca, Morocco
Noreddine Abghour LIMSAD Labs, Faculty of Sciences, Hassan II University of Casablanca, 20100, Casablanca, Morocco
Khalid Moussaid LIMSAD Labs, Faculty of Sciences, Hassan II University of Casablanca, 20100, Casablanca, Morocco
Amina El omri LIMSAD Labs, Faculty of Sciences, Hassan II University of Casablanca, 20100, Casablanca, Morocco
Mohamed Rida LIMSAD Labs, Faculty of Sciences, Hassan II University of Casablanca, 20100, Casablanca, Morocco
Abstract
Cloud computing is distributed architecture, providing computing facilities and storage resource as a service over an open environment (Internet), this lead to different matters related to the security and privacy in cloud computing. Thus, defending network accessible Cloud resources and services from various threats and attacks is of great concern. To address this issue, it is essential to create an efficient and effective Network Intrusion System (NIDS) to detect both outsider and insider intruders with high detection precision in the cloud environment. NIDS has become popular as an important component of the network security infrastructure, which detects malicious activities by monitoring network traffic. In this work, we propose to optimize a very popular soft computing tool widely used for intrusion detection namely, Back Propagation Neural Network (BPNN) using an Improved Genetic Algorithm (IGA). Genetic Algorithm (GA) is improved through optimization strategies, namely Parallel Processing and Fitness Value Hashing, which reduce execution time, convergence time and save processing power. Since, Learning rate and Momentum term are among the most relevant parameters that impact the performance of BPNN classifier, we have employed IGA to find the optimal or nearoptimal values of these two parameters which ensure high detection rate, high accuracy and low false alarm rate. The CloudSim simulator 4.0 and DARPA's KDD cup datasets 1999 are used for simulation. From the detailed performance analysis, it is clear that the proposed system called “ANIDS BPNN-IGA” (Anomaly NIDS based on BPNN and IGA) outperforms several state-of-art methods and it is more suitable for network anomaly detection.
Publication Details
Page(s) 61-84
DOI DOI not available
Published Journal: International Journal of Communication Networks and Information Security, Volume: 11, Issue: 1, Year: 2019

Request the full text from PASTIC

More from International Journal of Communication Networks and Information Security
0

Citations

0

Downloads

22

Views

Copyright ©  PASTIC National Center, Islamabad www.pastic.gov.pk