Anomaly Based Intrusion Detection on IOT Devices using Logistic Regression

Abstract

The collecting and exchange of information without human intervention will soon be possible thanks to the Internet of Things. Numerous conflicts with IOT technology are emerging due to the fast increase in connected devices, including those related to diversity, expansibility, service quality, security requirements, and many more. IOT technology has advanced as a result oftechnological developments like machine learning. To reduce learning difficulty by computing features, factor selection, also called feature selection, is crucial, especially for a large, huge data set like network traffic. Despite the ease of the new selection approaches, it is actually not an easy task to do feature selection properly. The Internet of Things will soon make it feasible to gather and transmit information without human involvement. Due to the rapid growth in connected devices, a number of conflicts with IOT technologies are arising. These conflicts include those involving diversity, expansibility, quality of service, security needs, and many more. As a consequence of technical advancements like machine learning, IOT technology has improved. Factor selection, also known as feature selection, is essential to lessen the complexity of learning by computing features, especially for a massive, enormous data set like internet traffic. Even though the new selection methods are simple, selecting features correctly is a difficult undertaking. Systems that detect and prevent intrusions are the most popular technology for spotting suspicious behaviour and defending diverse infrastructures against network intrusions (IDPSs). On the UNSW (University of New South Wales) -NBl5 data set, our suggested logistic regression algorithm makes predictions of anomalies with an accuracy of 98% using the automated feature selection approach since the accuracy of the model depends on the feature. The dimensionality reduction approach is used to reduce the misleading data.