mlsanityData to 14 Oct 2026

Machine Learning DDoS Detection for Consumer Internet of Things Devices

arXiv 1804.04159 ↗First posted 11 Apr 2018cs.CR cs.LGcited by 21

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Abstract (arXiv)

An increasing number of Internet of Things (IoT) devices are connecting to the Internet, yet many of these devices are fundamentally insecure, exposing the Internet to a variety of attacks. Botnets such as Mirai have used insecure consumer IoT devices to conduct distributed denial of service (DDoS) attacks on critical Internet infrastructure. This motivates the development of new techniques to automatically detect consumer IoT attack traffic. In this paper, we demonstrate that using IoT-specific network behaviors (e.g. limited number of endpoints and regular time intervals between packets) to inform feature selection can result in high accuracy DDoS detection in IoT network traffic with a variety of machine learning algorithms, including neural networks. These results indicate that home gateway routers or other network middleboxes could automatically detect local IoT device sources of DDoS attacks using low-cost machine learning algorithms and traffic data that is flow-based and protocol-agnostic.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Apr 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 7; 2025: 1; 2026: 4.

21 papers.

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  1. 2021Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPScited by 10
  2. 2021A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methodscited by 10
  3. 2024A Transformer-Based Framework for Payload Malware Detection and Classificationcited by 4
  4. 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
  5. 2024Packet Inspection Transformer: A Self-Supervised Journey to Unseen Malware Detection with Few Samplescited by 2
  6. 2024NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamicscited by 2
  7. 2021Evaluation of an Anomaly Detector for Routers using Parameterizable Malware in an IoT Ecosystemcited by 1
  8. 2023Online Self-Supervised Deep Learning for Intrusion Detection Systemscited by 1
  9. 2024An Investigation into the Performances of the State-of-the-art Machine Learning Approaches for Various Cyber-attack Detection: A Surveycited by 1
  10. 2022AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networkscited by 0
  11. 2023CADeSH: Collaborative Anomaly Detection for Smart Homescited by 0
  12. 2023Detecting Anomalous Microflows in IoT Volumetric Attacks via Dynamic Monitoring of MUD Activitycited by 0
9 more
  1. 20235G Networks and IoT Devices: Mitigating DDoS Attacks with Deep Learning Techniquescited by 0
  2. 2024Enhancing IoT Security Against DDoS Attacks through Federated Learningcited by 0
  3. 2024Leveraging Machine Learning for Accurate IoT Device Identification in Dynamic Wireless Contextscited by 0
  4. 2024Towards Efficient Machine Learning Method for IoT DDoS Attack Detectioncited by 0
  5. 2025Leveraging Machine Learning for Botnet Attack Detection in Edge-Computing Assisted IoT Networkscited by 0
  6. 2026Collaborative Zone-Adaptive Zero-Day Intrusion Detection for IoBTcited by 0
  7. 2026Explainable Threat Attribution for IoT Networks Using Conditional SHAP and Flow Behavior Modellingcited by 0
  8. 2026An Improved CNN-LSTM Based Intrusion Detection System for IoT Networkscited by 0
  9. 2026Maintaining IoT Device Identification under Concept Drift via Budget-Aware Traffic Labelingcited by 0