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Article

Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach

by 1,2,* and 1
1
College of Computer Science and Technology, Guizhou University (GZU), Guiyang 550025, China
2
School of Information Communication and Technology, University of The Gambia (UTG), Banjul, Peace Building, Kanifing P.O. Box 3530, The Gambia
*
Author to whom correspondence should be addressed.
Academic Editors: José Carlos R. Alcantud and Lorentz Jäntschi
Symmetry 2021, 13(10), 1764; https://doi.org/10.3390/sym13101764
Received: 16 August 2021 / Revised: 12 September 2021 / Accepted: 15 September 2021 / Published: 22 September 2021
(This article belongs to the Special Issue Information Technologies and Electronics Ⅱ)
The emergence of ground-breaking technologies such as artificial intelligence, cloud computing, big data powered by the Internet, and its highly valued real-world applications consisting of symmetric and asymmetric data distributions, has significantly changed our lives in many positive aspects. However, it equally comes with the current catastrophic daily escalating cyberattacks. Thus, raising the need for researchers to harness the innovative strengths of machine learning to design and implement intrusion detection systems (IDSs) to help mitigate these unfortunate cyber threats. Nevertheless, trustworthy and effective IDSs is a challenge due to low accuracy engendered by vast, irrelevant, and redundant features; inept detection of all types of novel attacks by individual machine learning classifiers; costly and faulty use of labeled training datasets cum significant false alarm rates (FAR) and the excessive model building and testing time. Therefore, this paper proposed a promising hybrid feature selection (HFS) with an ensemble classifier, which efficiently selects relevant features and provides consistent attack classification. Initially, we harness the various strengths of CfsSubsetEval, genetic search, and a rule-based engine to effectively select subsets of features with high correlation, which considerably reduced the model complexity and enhanced the generalization of learning algorithms, both of which are symmetry learning attributes. Moreover, using a voting method and average of probabilities, we present an ensemble classifier that used K-means, One-Class SVM, DBSCAN, and Expectation-Maximization, abbreviated (KODE) as an enhanced classifier that consistently classifies the asymmetric probability distributions between malicious and normal instances. HFS-KODE achieves remarkable results using 10-fold cross-validation, CIC-IDS2017, NSL-KDD, and UNSW-NB15 datasets and various metrics. For example, it outclassed all the selected individual classification methods, cutting-edge feature selection, and some current IDSs techniques with an excellent performance accuracy of 99.99%, 99.73%, and 99.997%, and a detection rate of 99.75%, 96.64%, and 99.93% for CIC-IDS2017, NSL-KDD, and UNSW-NB15, respectively based on only 11, 8, 13 selected relevant features from the above datasets. Finally, considering the drastically reduced FAR and time, coupled with no need for labeled datasets, it is self-evident that HFS-KODE proves to have a remarkable performance compared to many current approaches. View Full-Text
Keywords: HFS-KODE; intrusion detection systems; CfsSubsetEval; hybrid feature selection; rule-based engine; machine learning; genetic search; unsupervised and ensemble learning HFS-KODE; intrusion detection systems; CfsSubsetEval; hybrid feature selection; rule-based engine; machine learning; genetic search; unsupervised and ensemble learning
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MDPI and ACS Style

Jaw, E.; Wang, X. Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach. Symmetry 2021, 13, 1764. https://doi.org/10.3390/sym13101764

AMA Style

Jaw E, Wang X. Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach. Symmetry. 2021; 13(10):1764. https://doi.org/10.3390/sym13101764

Chicago/Turabian Style

Jaw, Ebrima, and Xueming Wang. 2021. "Feature Selection and Ensemble-Based Intrusion Detection System: An Efficient and Comprehensive Approach" Symmetry 13, no. 10: 1764. https://doi.org/10.3390/sym13101764

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