Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment

Bharti Yadav¹, Nasib Singh Gill¹, Preeti Gulia1, Deepak Dasaratha Rao², Yasaswini Mandiga³, Piyush Kumar Pareek^4,*

¹Department of Computer Science & Applications, Maharshi Dayanand University,

Rohtak, Haryana, India

²Department of Computer Science, Indian Institute of Technology, Patna, Orchid- 0000-0001-5959-3136, India

³Asst. Professor, Dept. of IT, Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College, Chennai, TN, India

⁴Professor and Head Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru, India  

Emails: bharti.yadav0801@gmail.com; nasib.gill@mdurohtak.ac.in; preeti@mdurohtak.ac.in; eepakrao@ieee.org; mandigayasaswini@velhightech.com; piyush.kumar@nmit.ac.in

Abstract

The Internet of Things (IoT) has revolutionized our daily lives, impacting everything from healthcare to transportation and even home automation and industrial control systems. However, as the number of connected devices continues to rise, so do the security risks. In this review, we explore the different types of attacks that target various layers of IoT infrastructure. To counter these threats, researchers have proposed using machine learning (ML) and deep learning (DL) techniques for detecting different types of attacks. However, our examination of existing literature reveals that the effectiveness of these techniques can vary greatly depending on factors like the dataset used, the features considered, and the evaluation methods employed. Finally, we delve into the current challenges facing Intrusion Detection Systems (IDS) in their mission to protect IoT environments from evolving threats.

Keywords: IoT; Machine Learning; Security; Threats