Machine learning for network application security: Empirical evaluation and optimization. (May 2021)

• Record Type: Journal Article
• Authors: Aledhari, Mohammed; Razzak, Rehma; Parizi, Reza M.
• Is Part Of: Computers & electrical engineering. Volume 91(2021)
• Publication Date: 2021-05
• Subjects: Machine learning -- Networking -- Cyber–physical systems -- Intrusion detection -- Anomaly detection -- Network traffic analysis -- Network security -- Neural networks; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2021.107052
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Machine learning (ML) has demonstrated great potential to revolutionize the networking field. In this paper, we present a large-scale empirical study to evaluate the effectiveness of state-of-the-art ML algorithms for network application security. In our experiments, six classical ML algorithms and three neural network algorithms are evaluated over three networking datasets, KDDCup 99, NSL-KDD, and ADFA IDS 2017. Measurements are made between the non-optimized and optimized versions of ML algorithms. Furthermore, various training and testing ratios are experimented to assess each algorithm's optimal performance. The results revealed that optimizing ML algorithms could help achieve better performance in detecting networking attacks. In particular, the Decision Tree proved to be the most accurate and fastest algorithm in the classical ML while the Recurrent Neural Network achieved the best performance among neural network algorithms. Graphical abstract: Highlights: Providing a comprehensive empirical evaluation of ML algorithms. Applying optimization methods to evaluate performance with respect to networking attacks. Providing accuracy results with and without optimization with different ML algorithms.