Published Paper Details:

One of the many types of crimes that exist in the technological age is cybercrime. Internet usage is rising quickly as more and more things become digital, and attackers are also exploiting it to perpetrate cybercrimes. Examples of invasions include hacking, banking fraud, email spamming, and others. In order to look into these fraudulent acts, the investigating authorities should use technology, which is a crucial component. In the domain of cyber forensics known as "digital forensic scrutiny," digital information that can be used as evidence in court is preserved and examined using scientific techniques and innovations. In order to reconstruct events, forensic investigators struggle with data collecting and analysis. Because people interact with one other quite a bit on a daily basis, machine learning enables investigators to use various algorithms to carry out investigations that are more successful and efficient.. This field of study focuses on developing computer models and algorithms that enable Every machine learning algorithm is domain-specific, capable of doing specialized tasks without the need for programming, like training and testing datasets, and can facilitate research. This study explores several machine learning approaches that look at and evaluate digital evidence while conducting an investigation. It also provides a complete overview of current digital forensics techniques and their applications. Furthermore, based on the features, this study assesses machine learning algorithms according to recognized digital forensics criteria.