Srivastava, Animesh (57224567252); Jindal, Aparna (60107568300); Shrivastava, Anurag (58127897200); Sawan, Vikash (58637136600)Animesh (57224567252); Jindal, Aparna (60107568300); Shrivastava, Anurag (58127897200); Sawan, Vikash (58637136600)Srivastava2026-02-052026-02-0520259798331503130https://dx.doi.org/10.1109/ICDICI66477.2025.11135398https://www.scopus.com/pages/publications/105016697985https://gnanaganga.alliance.edu.in/handle/123456789/9000The rapid growth of Internet of Things (IoT) devices increases their vulnerability to attacks, necessitating stronger security measures. The machine learning technique is one of the most effective ways to detect anomalies and malicious activity in IoT contexts. The use of machine learning for Internet of Things attack detection is reviewed in this paper. We have looked into the use of numerous popular machine learning techniques in this field, including Deep Neural Networks, Random Forests, and Support Vector Machines. The goal of this paper is to provide important insights to researchers and practitioners who want to use machine learning to fortify IoT systems against cyberattacks. We discuss the various ways attackers can attack, examine a summary of potential risks, and discuss the likely Machine Learning solutions. This paper provides a review of working in the field of IoT security with important insights by highlighting the significant potential of machine learning techniques in IoT attack detection. © 2025 IEEE.enAnomaly Detection; Attack Detection; Deep Learning; Intrusion Detection; IoT; Machine LearningConference paper