## 3 State-of-the-Art Use of Machine-Learning Techniques in Digital Forensics
In DFI, automation uses ML techniques for classification. ML techniques can obtain important information for investigations more efficiently by exploiting existing digital evidence-processing knowledge. Additionally, digital-evidence triage was developed for the prompt detection, processing, and interpretation of digital evidence. Currently, with AI techniques, the investigator determines the priority of device gathering and processing at a crime scene [4]. Furthermore, ref. [20] proposed an intelligent framework based on clustering and classification. The model learns from past crimes, and, when a new crime is registered, some of the crime information needs to be inserted by the investigator, such as the crime type, location, and time. The clustering process then automatically groups the new crime with previous similar crimes in the system using the k-nearest neighbour and crime-matching classification algorithms. In this way, the investigator can gain insights into the pre-investigation process by exploring the new crime, which is then clustered with previous similar crimes. Moreover, with the growth of cybercrime that targets minors, chat logs can be examined to detect and report harmful behaviour to law authorities. This can make a significant difference in protecting youngsters on social media platforms from being abused by cyber predators. Since DFI is done primarily by hand, the enormous volume and variety of data cause DF investigators to have a tough assignment; Ref. [21] suggested an approach using a DF process model backed by ML methodologies, to enable the automatic finding of hazardous talks in chat logs. One of the most fundamental characteristics of any smart device in an IoT network is its ability to acquire a bigger set of data than has been produced and then send the obtained data to the destination/receiver server through the internet. Thus, IoT-based networks are particularly vulnerable to simple or sophisticated assaults, which must be discovered early in the data transmission process in order to protect the network against these hostile attacks. The authors of [22] developed and built an intelligent intrusion detection system utilising machine-learning models so that assaults in the IoT network may be discovered. The adaptability of IoT devices raises the probability of continual attacks on them. Due to the low processing power and memory of IoT devices, security researchers have found it challenging to preserve records of diverse attacks performed on these devices during a DFI. The authors of [23] proposed an intelligent forensic analysis mechanism, to automate the detection of attacks on IoT devices based on the machine-to-machine framework. However, the proposed mechanism combines several ML techniques and different forensic analysis tools to detect different types of attacks. Furthermore, by providing a third-party logging server, the problem of evidence gathering has been overcome. To assess the effects and types of attacks and violations, forensic analysis is done on logs utilising a forensic server. In addition, ref. [24] indicated that the use of ML and deep-learning algorithms is effective for cyber-attack discovery, identification, and tracing by proposing a framework of cyber-attacks against smart satellite networks. In addition, IoT forensics and smart environments, with their recognised challenges, provide a great opportunity to develop new forensic tools to make the task of forensic investigators easier, which can be used for acquiring, preserving, and also analysing such forensic data. The authors of [25] proposed a user-friendly tool for smart devices that support WiFi and used smart-environment scenarios to allow forensic investigators, network administrators, and data scientists access to various features of network traffic with simple steps.