Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System

Abstract

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.

Published in The 2016 International Symposium on Networks, Computers and Communications (IEEE ISNCC'16) , Hammamet, Tunisia, 2016

References in corpus (2)

• Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
• A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems

Cited by in corpus (4)

• Machine Learning Based Intrusion Detection Systems for IoT Applications
• A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
• IoT Threat Detection Testbed Using Generative Adversarial Networks
• Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments