4.7 Article

CyberLearning: Effectiveness analysis of machine learning security modeling to detect cyber-anomalies and multi-attacks

期刊

INTERNET OF THINGS
卷 14, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.iot.2021.100393

关键词

Cybersecurity; Machine learning; Deep learning; Classification; Feature selection; Anomaly detection; Cyber-attacks; Security intelligence; Cyber data analytics; Intelligent systems

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This paper introduces CyberLearning, a machine learning-based cybersecurity modeling approach, and evaluates the effectiveness of various machine learning-based security models through empirical analysis.
Detecting cyber-anomalies and attacks are becoming a rising concern these days in the domain of cybersecurity. The knowledge of artificial intelligence, particularly, the machine learning techniques can be used to tackle these issues. However, the effectiveness of a learning-based security model may vary depending on the security features and the data characteristics. In this paper, we present CyberLearning, a machine learning-based cybersecurity modeling with correlated-feature selection, and a comprehensive empirical analysis on the effectiveness of various machine learning based security models. In our CyberLearning modeling, we take into account a binary classification model for detecting anomalies, and multi-class classification model for various types of cyber-attacks. To build the security model, we first employ the popular ten machine learning classification techniques, such as naive Bayes, Logistic regression, Stochastic gradient descent, K-nearest neighbors, Support vector machine, Decision Tree, Random Forest, Adaptive Boosting, eXtreme Gradient Boosting, as well as Linear discriminant analysis. We then present the artificial neural network-based security model considering multiple hidden layers. The effectiveness of these learning-based security models is examined by conducting a range of experiments utilizing the two most popular security datasets, UNSW-NB15 and NSL-KDD. Overall, this paper aims to serve as a reference point for data-driven security modeling through our experimental analysis and findings in the context of cybersecurity. (C) 2021 Elsevier B.V. All rights reserved.

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