ATTACK DETECTION AND MITIGATION IN INTERNET OF THINGS BASED ON TRUST MODEL

Abstract

Attacks and malicious nodes detection is gaining a importance in Service oriented Trust based model in Internet of Things (IoT). With the rising demand of IoT in every field, attacks and malicious nodes with threats is also increasing. Bad Mouthing, Good Mouthing, Ballot stuffing, Denial of Service, On-off attack can severally affect the trust factor of such models. In this paper, DDoS attack has been successful detected among three datasets using training and testing. The proposed system shows 99.76% training accuracy. Performance is measured in terms of confusion matrix, accuracy, precision, sensitivity, and specificity.