Malicious Traffic Flow Detection in IOT Using Ml Based Algorithms

Abstract

Identifying the malicious traffic flows in Internet of things (IOT) is very important to monitor and avoid unwanted errors or the unwanted flows in the network. So, for a security to this network various machine learning algorithms (ML) has been introduced by various analyst to avoid this flow of error in the network. But, owing to the unsuitable selection of features, the ML models which introduced previously suffer from misclassify errors. So, there arises a need to study the problem of feature selection more depth to predict the accurate traffic flow observation in the network. To overcome this problem, a new structure in machine learning (ML) is introduced. So, for thisa novel features selection metric CorrAUC is suggested. So, based on this metric approach, a new feature selection algorithm CorrAUC is develop and design, it is based on wrapper technique to get features accurately by filtering to predict flow of traffic is suggested. Then, we applied multicriteria decision method called VIKOR which is used for validating the features selected for recognition the flow of traffic errors in the network. We estimate our approach by using the NSL-KDD dataset and three different ML algorithms.