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In recent years, the Industrial Internet of Things (IIoT) has grown rapidly, a fact that has led to an increase in the number of cyberattacks that target this environment and the technologies that it brings together. Unfortunately, when it comes to using tools for stopping such attacks, it can be noticed that there are inherent weaknesses in this paradigm, such as limitations in computational capacity, memory and network bandwidth. Under these circumstances, the solutions used until now in conventional scenarios cannot be directly adopted by the IIoT, and so it is necessary to develop and design new ones that can effectively tackle this problem. Furthermore, these new solutions must be tested in order to verify their performance and viability, which requires testing architectures that are compatible with newly introduced IIoT topologies. With the aim of addressing these issues, this work proposes MECInOT, which is an architecture based on openLEON and capable of generating test scenarios for the IIoT environment. The performance of this architecture is validated by creating an intelligent threat detector based on tree-based algorithms, such as decision tree, random forest and other machine learning techniques. Which allows us to generate an intelligent and to demonstrate, we could generate an intelligent threat detector and demonstrate the suitability of our architecture for testing solutions in IIoT environments. In addition, by using MECInOT, we compare the performance of the different machine learning algorithms in an IIoT network. Firstly, we present the benefits of our proposal, and secondly, we describe the emulation of an IIoT environment while ensuring the repeatability of the experiments. [ABSTRACT FROM AUTHOR]

The weakness of the security measures implemented on Internet of Things (IoT) devices, added to the sensitivity of the data that they handle, has created an attractive environment for cybercriminals to carry out attacks. This has caused a substantial increase in the number of cyberincidents, requiring the opening of digital investigations in order to shed light on what has occurred. However, the characteristics of this new environment, such as its variety of contexts, make it impossible to use the methodology followed until now in conventional analysis. Therefore, a new common procedure is needed to ensure that IoT examinations are carried out in a complete and efficient manner. In this article, after reviewing the methodological requirements of IoT forensics, and studying the suggestions made by the research community, a methodology to perform investigations in a certain context of the IoT environment is proposed. In addition, its practicality is evaluated in three different security incident scenarios, proving its effectiveness and appropriateness to be used in future cases. [ABSTRACT FROM AUTHOR]

The increase in the number of cybersecurity incidents in which internet of things (IoT) devices are involved has called for an improvement in the field of computer forensics, which needs to provide techniques in order to perform complete and efficient investigations in this new environment. With the aim of doing so, new devices and systems are being studied in order to offer guidelines for investigators on how to examine them. This papers follows this approach and presents a forensic analysis of the non-volatile memory of Windows 10 IoT Core. It details how the investigation should be performed and highlights the relevant information that can be extracted from storage. In addition, a tool for the automation of the retrieval of the pieces of evidence detected is provided. [ABSTRACT FROM AUTHOR]