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

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 adv...

Full description

Saved in:
Bibliographic Details
Published in:International journal of communication networks and information security 2022-04, Vol.14 (1), p.93-117
Main Authors: Chiba, Zouhair, El Kasmi Alaoui, Moulay Seddiq, Abghour, Noreddine, Moussaid, Khalid
Format: Article
Language:English
Subjects:
Access control
Adaptive algorithms
Artificial neural networks
Cloud computing
Computer simulation
Customer services
Cybercrime
Data storage
Empowerment
False alarms
Genetic algorithms
Internet
Intrusion detection systems
Malware
Neural networks
Optimization
Privacy
Search algorithms
Simulated annealing
Software services
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2076-0930
2073-607X
2073-607X
2076-0930
DOI:10.17762/ijcnis.v14i1.5264