Review on the Security Threats of Internet of Things

TL;DR

This paper systematically reviews IoT security threats, malware types, recent attacks, and machine learning-based mitigation techniques, highlighting the effectiveness of kNN in malware detection and discussing future security challenges.

Contribution

It provides a comprehensive overview of IoT security issues, malware, recent attack cases, and evaluates machine learning algorithms for threat mitigation, especially emphasizing kNN's effectiveness.

Findings

kNN achieves high accuracy in malware detection

Recent malware attacks like Mirai demonstrate IoT vulnerabilities

Discussion of tools for ransomware detection and analysis

Abstract

Internet of Things (IoT) is being considered as the growth engine for industrial revolution 4.0. The combination of IoT, cloud computing and healthcare can contribute in ensuring well-being of people. One important challenge of IoT network is maintaining privacy and to overcome security threats. This paper provides a systematic review of the security aspects of IoT. Firstly, the application of IoT in industrial and medical service scenarios are described, and the security threats are discussed for the different layers of IoT healthcare architecture. Secondly, different types of existing malware including spyware, viruses, worms, keyloggers, and trojan horses are described in the context of IoT. Thirdly, some of the recent malware attacks such as Mirai, echobot and reaper are discussed. Next, a comparative discussion is presented on the effectiveness of different machine learning algorithms in mitigating the security threats. It is found that the k-nearest neighbor (kNN) machine learning algorithm exhibits excellent accuracy in detecting malware. This paper also reviews different tools for ransomware detection, classification and analysis. Finally, a discussion is presented on the existing security issues, open challenges and possible future scopes in ensuring IoT security.