Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning

When adopting cloud computing, cybersecurity needs to be applied to detect and protect against malicious intruders to improve the organization’s capability against cyberattacks. Having network intrusion detection with zero false alarm is a challenge. This is due to the asymmetry between informative...

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Bibliographic Details
Published in:Symmetry (Basel) Vol. 13; no. 12; p. 2306
Main Authors: Aldallal, Ammar, Alisa, Faisal
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.12.2021
Subjects:
Accuracy
Cloud computing
Cybersecurity
Data encryption
Datasets
Efficiency
False alarms
Feature selection
Genetic algorithms
Hybrid systems
Intrusion detection systems
Kernel functions
Machine learning
Medical records
Medical research
Performance evaluation
Security services
Support vector machines
User behavior
Bahrain
ISSN:2073-8994, 2073-8994
Online Access:Get full text
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Summary:When adopting cloud computing, cybersecurity needs to be applied to detect and protect against malicious intruders to improve the organization’s capability against cyberattacks. Having network intrusion detection with zero false alarm is a challenge. This is due to the asymmetry between informative features and irrelevant and redundant features of the dataset. In this work, a novel machine learning based hybrid intrusion detection system is proposed. It combined support vector machine (SVM) and genetic algorithm (GA) methodologies with an innovative fitness function developed to evaluate system accuracy. This system was examined using the CICIDS2017 dataset, which contains normal and most up-to-date common attacks. Both algorithms, GA and SVM, were executed in parallel to achieve two optimal objectives simultaneously: obtaining the best subset of features with maximum accuracy. In this scenario, an SVM was employed using different values of hyperparameters of the kernel function, gamma, and degree. The results were benchmarked with KDD CUP 99 and NSL-KDD. The results showed that the proposed model remarkably outperformed these benchmarks by up to 5.74%. This system will be effective in cloud computing, as it is expected to provide a high level of symmetry between information security and detection of attacks and malicious intrusion.
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym13122306