Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm

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Published: Apr 15, 2022
Updated: 2022-04-15
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2022-04-15 (2)
DOI: https://doi.org/10.17762/ijcnis.v14i1.5264
Keywords:
Cloud computing, Network intrusion detection system, Deep neural network, Genetic algorithm, Self-adaptive heuristic search algorithm, Simulated annealing algorithm.

Main Article Content

Zouhair Chiba
Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco
https://orcid.org/0000-0002-6200-0087
Moulay Seddiq El Kasmi Alaoui
Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco
Noreddine Abghour
Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco
Khalid Moussaid
Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Abstract

Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods.

Article Details

How to Cite
Chiba, Z., El Kasmi Alaoui, M. S., Abghour, N., & Moussaid, K. (2022). Automatic Building of a Powerful IDS for The Cloud Based on Deep Neural Network by Using a Novel Combination of Simulated Annealing Algorithm and Improved Self- Adaptive Genetic Algorithm. International Journal of Communication Networks and Information Security (IJCNIS), 14(1). https://doi.org/10.17762/ijcnis.v14i1.5264 (Original work published April 12, 2022)
Issue
Vol. 14 No. 1 (2022)
Section
Research Articles
Author Biographies

Zouhair Chiba, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

Moulay Seddiq El Kasmi Alaoui, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

Noreddine Abghour, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

Khalid Moussaid, Faculty of Sciences, Hassan II University of Casablanca, Casablanca, Morocco

Department of Mathematics and Computers, LIS Labs

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