Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey

Abstract

Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for smart devices and network, IoT is now facing more security challenges than ever before. There are existing security measures that can be applied to protect IoT. However, traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness. Thus, a strong-dynamically enhanced and up to date security system is required for next-generation IoT system. A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT. In order to detect attacks and identify abnormal behaviors of smart devices and networks, ML is being utilized as a powerful technology to fulfill this purpose. In this survey paper, the architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks. Moreover, ML-based potential solutions for IoT security has been presented and future challenges are discussed.

References in corpus (9)

• Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
• IoT Sentinel: Automated Device-Type Identification for Security Enforcement in IoT
• The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved
• Securing the Internet of Things in the Age of Machine Learning and Software-defined Networking
• In-Network Outlier Detection in Wireless Sensor Networks
• Detection of Unauthorized IoT Devices Using Machine Learning Techniques
• Microlocation for Smart Buildings in the Era of the Internet of Things: A Survey of Technologies, Techniques, and Approaches
• Lifelong Learning for Sentiment Classification
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

Cited by in corpus (7)

• Recent Advances in the Internet of Medical Things (IoMT) Systems Security
• IoT Data Analytics in Dynamic Environments: From An Automated Machine Learning Perspective
• Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions
• A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection
• Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
• Forensics and security issues in the Internet of Things
• Current Lightweight Cryptography Protocols in Smart City IoT Networks: A Survey