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

Enhancing IoT Security Through AI-Based Anomaly Detection and Intrusion Prevention

Narayana Swamy Ramaiah, S. Andrews, A. Shenbagharaman · International Conferences on Contemporary Computing and Informatics · 2023

Activity Recognition Using Deep Learning for Video Surveillance

Aleena Tariq, Muhammad Ahmad, Haris Ali · Machine Learning for Human Intelligence · 2023

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

N. Jeffrey, Qing Tan, J. Villar · Electronics · 2023

SSS-EC: Cryptographic based Single-Factor Authentication for Fingerprint Data with Machine Learning Technique

M. Nandhini Sharphathy, V. Sumalatha · 2023 2nd International Conference on Edge Computing and Applications (ICECAA) · 2023

DDoS Attack and Detection Methods in Internet-Enabled Networks: Concept, Research Perspectives, and Challenges

K. Adedeji, A. Abu-Mahfouz, A. Kurien · J. Sens. Actuator Networks · 2023

Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities

M. Khayyat · Alexandria Engineering Journal · 2023

IoTTPS: Ensemble RKSVM Model-Based Internet of Things Threat Protection System

Urooj Akram, Wareesa Sharif, Mobeen Shahroz · Italian National Conference on Sensors · 2023

Detect anomalies in cloud platforms by using network data: a review

M. Jayaweera, W. Kithulwatta, R. Rathnayaka · Cluster Computing · 2023

Deep Learning-Enabled Deceptive Jammer Detection for Low Probability of Intercept Communications

Hamda Bouzabia, Tri Nhu Do, Georges Kaddoum · IEEE Systems Journal · 2023

Ensemble-Learning Framework for Intrusion Detection to Enhance Internet of Things’ Devices Security

Yazeed Alotaibi, Mohammad Ilyas · Italian National Conference on Sensors · 2023

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

13

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.