mlsanityData to 7 Oct 2026

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

arXiv 1805.03409 ↗First posted 9 May 2018cs.CR cs.LGcited by 67

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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.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 May 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 13; 2023: 9; 2024: 11; 2025: 14; 2026: 6.

67 papers.

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  1. 2022Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challengescited by 137
  2. 2021Federated Learning for Malware Detection in IoT Devicescited by 36
  3. 2021Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detectioncited by 19
  4. 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
  5. 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Surveycited by 9
  6. 2021Demystifying the Transferability of Adversarial Attacks in Computer Networkscited by 9
  7. 2025PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environmentscited by 9
  8. 2022Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threatscited by 7
  9. 2022Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Studycited by 6
  10. 2022XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensicscited by 6
  11. 2024Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learningcited by 6
  12. 2025Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encodercited by 6
38 more
  1. 2022Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networkscited by 5
  2. 2023FederatedTrust: A Solution for Trustworthy Federated Learningcited by 5
  3. 2023Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systemscited by 5
  4. 2025Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasetscited by 5
  5. 2021SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learningcited by 4
  6. 2022Joint Semantic Transfer Network for IoT Intrusion Detectioncited by 4
  7. 2023Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approachcited by 3
  8. 2024Federated PCA on Grassmann Manifold for IoT Anomaly Detectioncited by 3
  9. 2025Graph Attention Neural Network for Botnet Detection: Evaluating Autoencoder, VAE and PCA-Based Dimension Reductioncited by 3
  10. 2025Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approachescited by 3
  11. 2021Enhancing Robustness of On-line Learning Models on Highly Noisy Datacited by 2
  12. 2021Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networkscited by 2
  13. 2022Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attackscited by 2
  14. 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
  15. 2023A systematic literature review on the code smells datasets and validation mechanismscited by 2
  16. 2023TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detectioncited by 2
  17. 2024The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Surveycited by 2
  18. 2024Meta-learning for Positive-unlabeled Classificationcited by 2
  19. 2024FedMSE: Semi-supervised federated learning approach for IoT network intrusion detectioncited by 2
  20. 2021Robust Attack Detection Approach for IIoT Using Ensemble Classifiercited by 1
  21. 2021Detecting Attacks on IoT Devices using Featureless 1D-CNNcited by 1
  22. 2022DP2-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoringcited by 1
  23. 2022Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Networkcited by 1
  24. 2023Online Self-Supervised Deep Learning for Intrusion Detection Systemscited by 1
  25. 2025AegisMCP: Online Graph Intrusion Detection for Tool-Augmented LLMs on Edge Devicescited by 1
  26. 2026NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasioncited by 1
  27. 2026BRIDGE and TCH-Net: Heterogeneous Benchmark and Multi-Branch Baseline for Cross-Domain IoT Botnet Detectioncited by 1
  28. 2021Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detectioncited by 0
  29. 2021RadArnomaly: Protecting Radar Systems from Data Manipulation Attackscited by 0
  30. 2021Dataset: Large-scale Urban IoT Activity Data for DDoS Attack Emulationcited by 0
  31. 2021Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learningcited by 0
  32. 2022Online Deep Learning based on Auto-Encodercited by 0
  33. 2022SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environmentcited by 0
  34. 2022DF-Captcha: A Deepfake Captcha for Preventing Fake Callscited by 0
  35. 2023Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learningcited by 0
  36. 2023Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environmentscited by 0
  37. 2023CADeSH: Collaborative Anomaly Detection for Smart Homescited by 0
  38. 2024Locality Sensitive Hashing for Network Traffic Fingerprintingcited by 0

50 most cited of 67 All 67 (JSON)