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

TL;DR

This paper analyzes IoT network threats and employs an Artificial Neural Network to detect DDoS/DoS attacks, achieving high accuracy in classifying normal and malicious traffic in a simulated environment.

Contribution

It introduces a supervised ANN approach for IoT threat detection, demonstrating effective classification of attack patterns with high accuracy.

Findings

Achieved 99.4% detection accuracy.

Successfully classified normal and attack traffic.

Validated on a simulated IoT network.

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.