Machine Learning and Artificial Intelligence for Detecting Cyber Security Threats in IoT Environmment
Ravindra Bhardwaj, Sreenivasulu Gogula, Bidisha Bhabani, K. Kanagalakshmi, Aparajita Mukherjee, D. Vetrithangam · 2025
The Internet of Things (IoT) refers to the increasing connectivity of many human-made entities, such as healthcare systems, smart homes, and smart grids, through the internet. Currently, a vast amount of material and expertise has been widely spread. These networks give rise to several security threats and privacy concerns. Intrusions refer to malevolent and unlawful actions that cause harm to the network. IoT networks are susceptible to a diverse range of security issues due to their widespread presence. Cyber attacks on the IoT architecture can lead to the loss of information or data, as well as the sluggishness of IoT devices. For the past twenty years, an Intrusion Detection System has been utilized to ensure the security of data and networks. Conventional intrusion detection technologies are ineffective in detecting security breaches in the Internet of Things (IoT) because of the distinct standards and protocol stacks used in its network. Regularly analyzing the vast amount of data created by IoT is a tough task due to its endless nature. An intrusion detection system (IDS) is employed to safeguard a system or network against unauthorized access by actively monitoring and identifying any potentially malicious or suspicious activities. Machine learning technologies provide robust and efficient approaches for mitigating these distinct hazards. The establishment of a robust machine learning system is the key to acquiring networks that are free from any form of threats.