Comparative Study of the IoT Forensics Framework Using AI/ML Approaches for the Detection and Prevention of Cyberattacks

The growing proliferation of Internet of things (IoT) devices offers numerous advantages; there are also emerging security and forensics challenges. Digital analysts and professionals confront major challenges when interacting with IoT gadgets in order to investigate cybercrimes in a timely and forensically sound manner, as the billions of devices in the IoT system generate vast amounts of evidence. These problems increase the opportunity for cybercriminals to target the different IoT applications and solutions, which directly affect the IoT users. Because the IoT has permeated almost every part of lives, cybercrimes will possibly endanger human lives; thus, IoT forensics is essential to detect and prevent such attacks. IoT systems are typically made up of disconnected, resource-limited devices that enormous information. This information is useful for assessments, behavior assessments, and decisions. The IoT forensic study determines the degree of damage caused to the IoT devices as a result of various attacks. Machine learning (ML) and artificial intelligence (AI) are two effective techniques to revolutionize IoT forensics, allowing researchers to analyze huge volumes of data quickly and correctly, consequently finding key evidence. This study is to perform the comparative examination of AI/ML-based IoT forensic frameworks in order to analyze their effectiveness and gain a better knowledge.