Skip to content
Research Article Open access CC BY 4.0

Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review

Saad Hikmat Haji, Siddeeq Y. Ameen

Asian Journal of Research in Computer Science · pp. 30–46 · Published 2 Jun 2021

10.9734/ajrcos/2021/v9i230218

Abstract

The Internet of Things (IoT) is one of today's most rapidly growing technologies. It is a technology that allows billions of smart devices or objects known as "Things" to collect different types of data about themselves and their surroundings using various sensors. They may then share it with the authorized parties for various purposes, including controlling and monitoring industrial services or increasing business services or functions. However, the Internet of Things currently faces more security threats than ever before. Machine Learning (ML) has observed a critical technological breakthrough, which has opened several new research avenues to solve current and future IoT challenges. However, Machine Learning is a powerful technology to identify threats and suspected activities in intelligent devices and networks. In this paper, various ML algorithms have been compared in terms of attack detection and anomaly detection, following a thorough literature review on Machine Learning methods and the significance of IoT security in the context of various types of potential attacks. Furthermore, possible ML-based IoT protection technologies have been introduced.

Internet of Things (IoT) IoT attack machine learning anomaly detection

References (35)

  1. 1 A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures [DOI]
  2. 2 A Survey on Security and Privacy Issues in Internet-of-Things [DOI]
  3. 3 Distributed attack detection scheme using deep learning approach for Internet of Things [DOI]
  4. 4 Network Intrusion Detection for IoT Security Based on Learning Techniques [DOI]
  5. 5 A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues [DOI]
  6. 6 Internet of Things: A survey on the security of IoT frameworks [DOI]
  7. 7 IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security? [DOI]
  8. 8 Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics [DOI]
  9. 9 Machine learning based solutions for security of Internet of Things (IoT): A survey [DOI]
  10. 10 Multimodal Emotion Recognition using Deep Learning [DOI]
  11. 11 Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions [DOI]
  12. 12 AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning [DOI]
  13. 13 Internet of Things (IoT): Research, Simulators, and Testbeds [DOI]
  14. 14 A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems [DOI]
  15. 15 Evaluation of Machine Learning Algorithms for Anomaly Detection [DOI]
  16. 16 Energy Management for Internet of Things via Distributed Systems [DOI]
  17. 17 Task Scheduling Algorithms in Cloud Computing: A Review [DOI]
  18. 18 Internet of Things Cyber Attacks Detection using Machine Learning [DOI]
  19. 19 Internet of Things Security Research: A Rehash of Old Ideas or New Intellectual Challenges? [DOI]
  20. 20 Anomaly Detection Techniques using Deep Learning in IoT: A Survey [DOI]
  21. 21 A State of Art Survey for Intelligent Energy Monitoring Systems [DOI]
  22. 22 A State of Art for Smart Gateways Issues and Modification [DOI]
  23. 23 A walkthrough of the emerging IoT paradigm: Visualizing inside functionalities, key features, and open issues [DOI]
  24. 24 Supervised Machine Learning Based Network Intrusion Detection System for Internet of Things [DOI]
  25. 25 Machine Learning Based Intrusion Detection for IoT Botnet [DOI]
  26. 26 Real time pollution monitoring using Wireless Sensor Networks [DOI]
  27. 27 Real Time Video Streaming From Multi-Source Using Client-Server for Video Distribution [DOI]
  28. 28 Embedded Real-Time Video Surveillance System based on Multi-Sensor and Visual Tracking [DOI]
  29. 29 Two Levels Alert Verification Technique for Smart Oil Pipeline Surveillance System (SOPSS) [DOI]
  30. 30 Dynamic Switching of Scheduling Algorithm for Uplink LTE System [DOI]
  31. 31 Decode And Forward Cooperative Protocol Enhancement Using Interference Cancellation [DOI]
  32. 32 Detection and Prevention Cyber-Attacks for Smart Buildings via Private Cloud Environment [DOI]
  33. 33 An Energy-Efficient MIMO-Based 4G LTE-A Adaptive Modulation and Coding Scheme for High Mobility Scenarios [DOI]
  34. 34 Improved hybrid variable and fixed step size least mean square adaptive filter algorithm with application to time varying system identification [DOI]
  35. 35 Machine Learning for anomaly detection in IoT networks :Malware analysis on the IoT-23 data set

Cited by 239

Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review

Shereen S. Ismail, Diana W. Dawoud, Hassan Reza · Future Internet · 2023

High-speed Machine Learning-enhanced Receiver for Millimeter-Wave Systems

Dolores García, R. Ruiz, J. O. Lacruz · IEEE Conference on Computer Communications · 2023

Anomaly traffic detection based on feature fluctuation for secure industrial internet of things

Jie Yin, Chun-Tang Zhang, Wen-Wei Xie · Peer-to-Peer Networking and Applications · 2023

Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence

Tehseen Mazhar, Dhani Bux Talpur, Tamara Al Shloul · Brain Science · 2023

Anomaly-based cyberattacks detection for smart homes: A systematic literature review

Juan Ignacio Iturbe Araya, H. Rifà-Pous · Internet of Things · 2023

A Brief Survey on the Internet of Things (IoT) Security

Abdulelah Al Hanif, Mohammad Ilyas · Proceedings of the World Multi-Conference on Systemics, Cybernetics and Informatics · 2023

An Efficient DDoS Attack Detection Using Chaos Henry Gas Solubility Optimization Weight Initialization Based Rectified Linear Unit

Selvam Lakshmanan, Uma Maheswari Gnaniyan Ponnusamy, Senthilkumar Andi · Cybernetics and systems · 2023

Comparative Study of Detection of IoT Attacks using Machine Learning Techniques

Mohd Zain Khan, M. Bokhari · International Conference on Computing for Sustainable Global Development · 2023

Machine Learning Approach to Anomaly Detection Attacks Classification in IoT Devices

Aisha Al Obaidli, Deema Mansour, S. Abdulhamid · International Conference on Artificial Intelligence and Soft Computing · 2023

Anomaly detection in Internet of medical Things with Blockchain from the perspective of deep neural network

Jun Wang, Hanlei Jin, Junxiao Chen · Information Sciences · 2022

Showing 109 of 239 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

130

Page views

0

PDF downloads

7

Outbound clicks

239

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.