The Web Attack Detection System for Internet of Things via Ensemble Classification

Abstract

Internet of Things (IoT) networks contain millions of devices with the function of interacting with each other and providing useful things that were never available to us before. However, the diversity in types of IoT devices makes the IoT networks’ environments more complex and more vulnerable to various web attacks compared to traditional computer networks. We propose a novel machine learning based Web Attack Detection System (WADS) to alleviate the serious issues that IoT networks faces. Specifically, we have used two machine learning classifier to detect web attacks separately. We then use an MLP classifier to make the final decision according to the results obtained from the Dataset. In order to evaluate the proposed system, we have performed experiments on a public dataset as well as a real-word dataset running in a distributed environment. Experimental results show that the proposed system can detect web attacks accurately with low false positive and negative rates.