# N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders

arXiv: https://arxiv.org/abs/1805.03409 · HTML: https://mlsanity.com/paper/1805.03409/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 30 of 67)

- [Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges](https://mlsanity.com/paper/2211.08413/) (2022)
- [Federated Learning for Malware Detection in IoT Devices](https://mlsanity.com/paper/2104.09994/) (2021)
- [Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection](https://mlsanity.com/paper/2106.07976/) (2021)
- [Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges](https://mlsanity.com/paper/2108.00974/) (2021)
- [Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey](https://mlsanity.com/paper/2101.10181/) (2021)
- [Demystifying the Transferability of Adversarial Attacks in Computer Networks](https://mlsanity.com/paper/2110.04488/) (2021)
- [PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments](https://mlsanity.com/paper/2501.15563/) (2025)
- [Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats](https://mlsanity.com/paper/2204.03433/) (2022)
- [Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study](https://mlsanity.com/paper/2203.09332/) (2022)
- [XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics](https://mlsanity.com/paper/2207.09088/) (2022)
- [FederatedTrust: A Solution for Trustworthy Federated Learning](https://mlsanity.com/paper/2302.09844/) (2023)
- [Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning](https://mlsanity.com/paper/2406.06207/) (2024)
- [Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder](https://mlsanity.com/paper/2504.14879/) (2025)
- [Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks](https://mlsanity.com/paper/2212.12121/) (2022)
- [Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems](https://mlsanity.com/paper/2312.02490/) (2023)
- [Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets](https://mlsanity.com/paper/2505.01437/) (2025)
- [SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning](https://mlsanity.com/paper/2101.02780/) (2021)
- [Joint Semantic Transfer Network for IoT Intrusion Detection](https://mlsanity.com/paper/2210.15911/) (2022)
- [Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach](https://mlsanity.com/paper/2301.09801/) (2023)
- [A systematic literature review on the code smells datasets and validation mechanisms](https://mlsanity.com/paper/2306.01377/) (2023)
- [Federated PCA on Grassmann Manifold for IoT Anomaly Detection](https://mlsanity.com/paper/2407.07421/) (2024)
- [Graph Attention Neural Network for Botnet Detection: Evaluating Autoencoder, VAE and PCA-Based Dimension Reduction](https://mlsanity.com/paper/2505.17357/) (2025)
- [Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approaches](https://mlsanity.com/paper/2505.17363/) (2025)
- [Enhancing Robustness of On-line Learning Models on Highly Noisy Data](https://mlsanity.com/paper/2103.10824/) (2021)
- [Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks](https://mlsanity.com/paper/2112.00988/) (2021)
- [Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks](https://mlsanity.com/paper/2209.12195/) (2022)
- [Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions](https://mlsanity.com/paper/2210.13547/) (2022)
- [TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection](https://mlsanity.com/paper/2310.10661/) (2023)
- [The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey](https://mlsanity.com/paper/2401.05896/) (2024)
- [Meta-learning for Positive-unlabeled Classification](https://mlsanity.com/paper/2406.03680/) (2024)

## Abstract (arXiv)

The proliferation of IoT devices which can be more easily compromised than desktop computers has led to an increase in the occurrence of IoT based botnet attacks. In order to mitigate this new threat there is a need to develop new methods for detecting attacks launched from compromised IoT devices and differentiate between hour and millisecond long IoTbased attacks. In this paper we propose and empirically evaluate a novel network based anomaly detection method which extracts behavior snapshots of the network and uses deep autoencoders to detect anomalous network traffic emanating from compromised IoT devices. To evaluate our method, we infected nine commercial IoT devices in our lab with two of the most widely known IoT based botnets, Mirai and BASHLITE. Our evaluation results demonstrated our proposed method's ability to accurately and instantly detect the attacks as they were being launched from the compromised IoT devices which were part of a botnet.