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

Telecommunication Engineering Department, College of Engineering, Ahlia University, Manama 10878, Bahrain
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Academic Editors: Peng-Yeng Yin, Ray-I Chang, Youcef Gheraibia, Ming-Chin Chuang, Hua-Yi Lin and Jen-Chun Lee
Symmetry 2021, 13(12), 2306; https://doi.org/10.3390/sym13122306
Received: 10 October 2021 / Revised: 10 November 2021 / Accepted: 16 November 2021 / Published: 3 December 2021
(This article belongs to the Topic Applied Metaheuristic Computing)
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. View Full-Text
Keywords: intrusion detection system; genetic algorithm; support vector machine; machine learning; fitness function intrusion detection system; genetic algorithm; support vector machine; machine learning; fitness function
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MDPI and ACS Style

Aldallal, A.; Alisa, F. Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning. Symmetry 2021, 13, 2306. https://doi.org/10.3390/sym13122306

AMA Style

Aldallal A, Alisa F. Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning. Symmetry. 2021; 13(12):2306. https://doi.org/10.3390/sym13122306

Chicago/Turabian Style

Aldallal, Ammar, and Faisal Alisa. 2021. "Effective Intrusion Detection System to Secure Data in Cloud Using Machine Learning" Symmetry 13, no. 12: 2306. https://doi.org/10.3390/sym13122306

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