Integration of machine learning algorithms for intrusion detection in IoT networks

The Internet of Things (IoT) is a powerful technology transforming many aspects of our lives, from how we connect and work to how we receive healthcare and manage the economy. IoT holds promise for enhancing life across diverse settings, from urban environments to educational institutions, through task automation, productivity enhancement, and stress reduction. As new threats and vulnerabilities emerge, the old ways of securing IoT devices are no longer sufficient. The future of secure IoT systems relies on machine learning and deep learning optimised for efficiency. To ensure robust security in constantly evolving next-generation IoT systems, we need to harness the power of artificial intelligence, particularly machine learning and deep learning solutions. To achieve this vision of constantly adapting security for next-generation IoT, the authors must create new methods that guarantee the highest levels of security within the entire IoT infrastructure. The study subject is detection systems for intrusions into IoT infrastructure and compromised IoT devices based on machine learning algorithms. The study object is a machine learning model that will detect anomalies in an IoT network's behaviour and identify patterns that indicate normal behaviour and deviations that may signal an intrusion. The study aims to enhance the security of IoT networks by developing effective and efficient intrusion detection systems using machine learning techniques. The study used scientific methods such as data collection and preprocessing, algorithm selection and development, model training and evaluation, experimentation and analysis, scalability and efficiency testing. The authors used the works of such scientists and researchers as A. Géron, N. Sengupta, R. Vinayakumar, S. Sarwar, and Wang Meng. The study investigates security mechanisms for understanding attacker behaviour in the realm of the IoT. This could be a significant step forward in fortifying IoT security. This approach to securing IoT devices relies on machine learning to analyse the data traffic these devices produce during communication. Additionally, this paper proposes incorporating machine learning methods to enhance honeypot operation by integrating them into the lambda function's design. Machine learning is becoming increasingly popular across many fields because it often performs better than traditional rule-based approaches. While fully automated cyber security detection and analysis using machine learning is appealing, it is essential to carefully assess how well machine learning works in this area. The authors offer an analysis tailored for security professionals, focusing on utilising machine learning techniques to develop a honeypot for detecting intrusions.