The fourth industrial revolution (Industry 4.0), which began in recent years, is marked by the exponential growth of the Internet of Things (IoTs), fog computing, computer security, and cyberattacks. IoT networks and devices are rapidly evolving, producing massive volumes of data that require rigorous authentication and security. One of the most promising approaches for combating cybersecurity risks and providing security is machine learning and artificial intelligence (AI). We categorise, map, and survey the available literature on ML and AI technologies used to detect cybersecurity assaults in the IoT context in this work. This is known as a systematic literature review (SLR).This SLR's scope covers a thorough analysis of the majority of ML and AI trending methodologies in cybersecurity and cutting-edge solutions. The usefulness of machine learning (ML) techniques employed in IoT security and their application to attack detection have been examined in this research. To address the current security and privacy issues, various research have suggested using intelligent architectural frameworks and smart intrusion detection systems (IDS) with AI.

internet of things; artificial intelligence; machine learning; Artificial intelligence; cybersecurity; cyberattacks