Machine learning for network application security: Empirical evaluation and optimization
Department
Computer Science
Document Type
Article
Publication Date
5-1-2021
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.
Journal Title
Computers and Electrical Engineering
Journal ISSN
00457906
Volume
91
Digital Object Identifier (DOI)
10.1016/j.compeleceng.2021.107052