Hybrid intelligent intrusion detection system for wireless network attacks in the internet of things iot using computational intelligence techniques

Abstract

Through the ascent of online business and the Internet of Things (IoT), newlinethe safety of such frameworks over wireless systems are getting even more an newlinealarm. Hence, the Intrusion Detection System (IDS) is critical to direct and newlinehelp the movement of actions over the wireless systems. One of the essential newlinedifficulties to IDS is the issue of confusion, misidentification, and absence of newlinecontinuous reaction to the assault. Thus, a hybrid IDS structure that relies newlineupon machine learning classification and clustering procedures is anticipated newlineto decrease the false positive rate and false-negative rate to increase the newlinediscovery rate and identify zero-day assaults. The main goal of feature newlineselection was to ensure a negligible list consisting of features is distinguished newlineto signify all unique information without any decrease in precision. newlineAdvancement methods have been used by analysts for feature selection in newlineidentifying the set of features that are the best.The work has been assessed based on an openly accessible Aegean Wireless Intrusion Dataset (AWID) through various Machine Learning (ML) along with the methods of attribute reduction in the wireless networks of the IoT. The actual hints are available in the AWID data collection that includes benign and legitimate data packets of 802.11. The wireless network system may be grouped into two which are the high-level marked data collection with four types of data packets and the finer-grained data collection. With the ML and the methods of attribute reduction, the data packets found in the AWID data collection may be detected as a category of interruption which is legitimate. These AWID datasets are primarily classified into two different types that are based on class labeling. The High-level labelled dataset has four main classes and the other dataset has fine-grained class labelling. There are three traditional techniques used for attribute reduction Information Gain (IG), Chi-Squared statistics (CH), and Correlation-based Feature Selection newline (CFS). The IG method will assess t