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

arXiv: https://arxiv.org/abs/1704.02286 · HTML: https://mlsanity.com/paper/1704.02286/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2017.

## Built on by (most cited first, 9 of 9)

- [Real-time Network Intrusion Detection via Decision Transformers](https://mlsanity.com/paper/2312.07696/) (2023)
- [RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecture](https://mlsanity.com/paper/2311.16018/) (2023)
- [Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems](https://mlsanity.com/paper/2105.10707/) (2021)
- [Network Intrusion Detection System in a Light Bulb](https://mlsanity.com/paper/2210.03254/) (2022)
- [EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning](https://mlsanity.com/paper/2403.17980/) (2024)
- [A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks](https://mlsanity.com/paper/2307.10252/) (2023)
- [Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes](https://mlsanity.com/paper/2205.08043/) (2022)
- [Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments](https://mlsanity.com/paper/2310.10778/) (2023)
- [Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks](https://mlsanity.com/paper/2604.14444/) (2026)

## Abstract (arXiv)

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.