Threat analysis of IoT networks using artificial neural network intrusion detection system

By Elike Hodo, Xavier J. A. Bellekens, Andrew W. Hamilton, Pierre-Louis Dubouilh, E. Iorkyase, C. Tachtatzis, Robert C. Atkinson. International Symposium on Networks, Computers and Communications, 2016

487 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c4233cec037953a9cf40afcc9a857cb1faae959c

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Citations in the corpus

CNN-Based Network Intrusion Detection against Denial-of-Service Attacks (2020, background): “[13] suggest analysis about threat of IoT based on Artificial Neural Network (ANN) to detect DoS/DDoS attacks.”
A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges (2021, methodology): “A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces and then the model is assessed on its ability to thwart (DDoS/DoS) attacks (Hodo et al., 2016).”
Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices (2020, methodology): “[36] used ANN to detect DoS/DDoS attacks in IoTs using a simulated network.”
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2020, background): “FNN [72] Distributed denial of service attacks detectio.n K-means [73] Clustering of sensor networks to minimize packet error rate.”
Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset (2018, background): “…of network forensics -based ML are Intrusion detection, Honeypots, network ﬂow analysis, deep packet inspection, and email authorship, as discussed in the following points: • Intrusion Detection Systems (IDS) - [16, 17, 18] are classiﬁed into network and host intrusion detection systems.”
Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT (2017, methodology): “In [10] a neural network is used for detecting DoS attacks in a simulated IoT network, reporting an accuracy of 99.4%.”
Machine Learning Based Intrusion Detection Systems for IoT Applications (2019, background): “The main reason to perform this experiment is that the knowledge of classifier’s response time plays an important role in its selection as a intrusion detection system [36].”
Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, methodology): “References [17], [141], and [149] evaluate their proposal with their own data.”