Intrusion Detection System through Advance Machine Learning for the Internet of Things Networks

Abstract

Network security is the main issue in the Internet of Things networks because most manufacturers do not focus on security standards during design. The performance of firewalls, intrusion prevention systems, and intrusion detection systems depend upon accuracy, thus its essentials, to enhance the detection rate to lessen false alarms. The objective of the firewall is to find violations as per predefined rules and block the incoming risky traffic. However, it is very tough to differentiate between malicious and regular traffic due to advanced techniques of attack. To address these issues, a two-stage hybrid method is proposed. First, the genetic algorithm (GA) is applied to select appropriate features to improve the accuracy of the proposed framework. Next, the well-known machine learning (ML) algorithm, including the support vector machine (SVM), ensemble classifier, and decision tree are employed. The achieved accuracy is 99.8% through 10-fold cross-validation using a multiclass NSL-KDD database.