Identifying Intruder in Artificial Intelligence of Things Using Digital Forensic Framework: A Review

Artificial intelligence (AI) is a rapidly growing field in today’s digital world, capable of doing a variety of activities that would normally require human intelligence. The aim of AI is to develop systems that can work intelligently and independently. AI was integrated into the Internet of Things (IoT) to create Artificial Intelligence of Things (AIoT). The AIoT is a developing sector that resolves the challenges presented in the real-time world, and various autonomous systems are performed under the sector. Information security has become one of the main challenges presented in today’s world. Cyber security is the collection of policies, tools, guidelines, security safeguards, security concepts, and technologies used to protect the cyber environment and user assets. Security measures serve to maintain the confidentiality, availability, and integrity of information systems by reducing asset loss from cyber security threats. There are various combinations of hardware and software presented in AIoT. The AIoT presents a variety of Deep Learning (DL) and Machine Learning (ML)-based methods for identifying intrusions. There are various digital forensic frameworks used to detect the intruders. This review study examined various strategies for intrusion detection in autonomous vehicles for the years 2018–2023. This article’s effectiveness is enhanced by its brief explanation of AIoT challenges, applications, and future recommendations. Based on this review article, the most effective strategies for detecting intrusions in autonomous vehicles are proven for future use.