# Forensic Analysis on Internet of Things (IoT) Device Using Machine-to-Machine (M2M) Framework
## 2 Literature Survey  
The Internet of Things is divided into several categories. Cloud computing, virtualization, sensor technologies, and A.I technologies are examples of some technologies. When forensic analysis on an IoT device is performed, then this forensic procedure automatically involves these areas as well. Many IoT devices are dispersed in diverse areas outside of user control, creating a challenge to the forensic investigative process. Furthermore, the IoT forensics process faces additional obstacles, such as the short time to capture evidence before it vanishes and the limited storage capacity of IoT devices [26]. There has been research in the field of forensic analysis on IoT devices, which involves many built-in mechanisms and machine learning models. Various frameworks and tools have been designed for attack detection in the IoT domain. Table 1 elaborates the forensic techniques of IoT devices. [PERSON] et al. 's [27] proposed framework elaborates the various steps involved for a digital investigation procedure, but it lacks a feedback and evaluation plan and is unconcerned about privacy and integrity. [PERSON] et al. [28] presented an IoT forensic framework for smart environments. The main objective of the proposed framework is to provide an effective IoT forensics framework that is both compatible with IoT devices and capable of investigating crimes committed in the IoT infrastructure. This framework is dependent on some data privacy principles, which is not suitable for IoT devices which have limited resources. [PERSON] et al. [29] proposed a framework for sharing information related to cyberattacks with investigators. This framework is based on the PROFIT technique. It is basically a digital witness scheme for IoT attacks. The author proposes a new approach to researching IoT environments that considers a variety of privacy and security concerns. The goal of [PERSON] et al. 's [30] research paper is to provide an application-specific digital forensic investigation model. This model can be used in the IoT-related forensic investigation framework. In this research, a forensically significant artefact has been discovered in three common IoT applications such as home automation, smart cities, and smart wearables. [PERSON] et al. [31] presented a new approach to IoT forensics that prioritizes privacy. The goal is to put ISO/IEC 29100:2011's various principles into practice. All the key stages of any forensic process model are included in the proposed framework, as well as new stages such as review, initialization, and feedback. In contrast, the proposed framework is a privacy-conscious framework that considers a set of privacy principles that can increase data privacy, but it is ineffective for IoT devices with low resources. [PERSON] et al. [32] proposed a framework called particle deep framework (PDF). PDF describes a forensic analysis of encrypted traffic in IoT networks. This framework completes the process in three stages. First, to cope with encrypted IoT network traffic, it identifies data flows and validates their integrity. Second, PDF uses the particle swarm optimization (PSO) algorithm to automate the adoption of deep learning parameters. Finally, a deep neural network (DNN) is designed. DNN is based on a PSO for identification and tracing of the cyberattacks in the IoT network. This framework is also evaluated on smart home use case. Overall, the PDF achieves 98% accuracy. It is trained and tested for various datasets to evaluate the performance of the proposed framework. [PERSON] et al. [33] proposed a method for IoT device rapid digital forensics. Attackers are using vulnerabilities of IoT devices to target digital and physical infrastructures of systems. The forensic analysis of billions of networked devices is difficult due to the small amount of evidence they provide. As a result, computer forensics necessitates the creation of smart and fast digital research methodologies.