Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications

Abstract

In the last year, many modern attacks have targeted Internet of Things (IoT) devices. IoT botnet attacks are the most common. Despite the plethora of detection and prevention systems for cyberattacks, a security mechanism for IoT settings is still required since these devices have certain restrictions that make it difficult to properly implement the detection system. These memory resources are limited to processing, storing, and calculating. An effective defense against cyberattacks is an intrusion detection system (IDS). To develop the rules for IDS_MDL-IoT, we used the contemporary botnet attack dataset. Because IoT devices have limited resources, it is difficult to employ all attack patterns in the initial anomaly dataset in traditional anomaly-based IDS. Therefore, in this study, the tested ML model resulting in NB has proven to be the best classification algorithm to classify the risk of attack with 99.7% accuracy predicting from a time-speed of 3 seconds, and the tested DL model resulting in RNN has proven to be the best classification algorithm to classify the risk of attack with 99% accuracy predicting from a time-speed of 8 seconds.