A A Comprehensive Review of Intrusion Detection Systems in IoT networks Using ML and DL Techniques
A Comprehensive Review of Intrusion Detection Systems in IoT Networks Using ML and DL Techniques
Keywords:
IoT, Intrusion Detection System, Machine learning (ML), Deep learning (DL).Abstract
The Internet of Things (IoT) is growing at an extremely rapid rate, impacting all aspects of our lives and extending to various fields, including wearable technology, smart sensors, and home appliances. However, the rapid growth is coupled with serious security concerns that render these technologies vulnerable to hacking opportunities and erode user privacy, as well as data protection, especially as cyber-attacks become more complex. Intrusion detection is a crucial aspect for tracking and thwarting such attacks. Machine learning (ML) and deep learning (DL) algorithms have ever-increasing efficiency in automating procedures like these. This study aims to provide researchers with a comprehensive overview of contemporary Intrusion Detection System (IDS) techniques employed in the IoT environment, highlighting strengths and weaknesses. It also gives direction to future research by suggesting that more adaptive, lightweight, and efficient intrusion detection systems can be developed to address the unique constraints of IoT networks.
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Copyright (c) 2025 fatima Rahim, Asst.Prof.Dr.Saif Ali Abd Alradha Alsaidi (Author)
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