Edge Computing based IDS Detecting Threats using Machine Learning and PyCaret
A. P. Siva Kumar, Sanjeev Kumar, Vivek Kumar, Amrita Kumari, Ashish Kumar Saini, Sarthak Gupta · 2023
The Internet of Things is getting connected with the edge computing network day by day. These devices generate large amounts of confidential data that is stored and processed on edge computing. There is a need to protect such data and edge computing networks from new types of attacks. Therefore, intrusion detection system (IDS) is an effective defense to secure edge computing network. In this study, an IDS based on machine learning is designed and developed. This article elaborates the technique of Recursive Feature Elimination (RFE) using the Random Forest algorithm (RF). The contribution of this paper is to select a relevant and efficient feature set using the recently developed Python library PyCaret. This paper proposes a novel approach for detecting new types of attacks in network traffic. The recently released dataset CICIDS2017 has been used to evaluate the proposed method, which includes state-of-the-art attacks. In this paper, two classification techniques are also used to build the IDS model. The first is a binary classification technique using the recently developed PyCaret library, and the second is a multiclass classification technique using RFE.