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The Internet of Things (IoT) refers to the millions of devices around the world that are connected to the Internet. Insecure IoT devices designed without proper security features are the targets of many Internet threats. The rapid integration of the Internet into the IoT infrastructure in various areas of human activity, including vulnerable critical infrastructure, makes the detection of malware in the Internet of Things increasingly important. Annual reports from IoT infrastructure cybersecurity companies and antivirus software vendors show an increase in malware attacks targeting IoT infrastructure. This demonstrates the failure of modern methods for detecting malware on the Internet of things. This is why there is an urgent need for new approaches to IoT malware detection and to protect IoT devices from IoT malware attacks. The subject of the research is the malware detection process on the Internet of Things. This study aims to develop a technique for malware detection based on the control flow graph analysis. Results. This paper presents a new approach for IoT malware detection based on control flow graph analysis. Control flow graphs were built for suspicious IoT applications. The control flow graph is represented as a directed graph, which contains information about the components of the suspicious program and the transitions between them. Based on the control flow graph, metrics can be extracted that describe the structure of the program. Considering that IoT applications are small due to the simplicity and limitations of the IoT operating system environment, malware detection based on control flow graph analysis seems to be possible in the IoT environment. To analyze the behavior of the IoT application for each control flow graph, the action graph is to be built. It shows an abstract graph and a description of the program. Based on the action graph for each IoT application, a sequence is formed. This allows for defining the program’s behavior. Thus, with the aim of IoT malware detection, two malware detection models based on control flow graph metrics and the action sequences are used. Since the approach allows you to analyze both the overall structure and behavior of each application, it allows you to achieve high malware detection accuracy. The proposed approach allows the detection of unknown IoT malware, which are the modified versions of known IoT malware. As the mean of conclusion-making concerning the malware presence, the set of machine learning classifiers was employed. The experimental results demonstrated the high accuracy of IoT malware detection. Conclusions. A new technique for IoT malware detection based on control flow graph analysis has been developed. It can detect IoT malware with high efficiency.

Guest Editorial Distributed Data Processing in Industrial ApplicationsI NDUSTRIAL applications become more and more distributed. The "intelligence" is embedded in smaller and smaller devices connected via various types of computer and communication networks. This is possible due to the strong development of processing infrastructure as well as information and interconnection technologies. As a result, systems simply get "smarter," both from the user's and developer's point of view. They can handle more data in more sophisticated ways. They are able to deliver functionalities and services which were not even thinkable ten years ago. Temporal characteristics of data processing in distributed applications are obtainable as more in line with the requested ones. Finally, they can be created with tools, methods, and standards providing better support in the domain of modeling, simulation, validation, and other aspects of designing, testing, and commissioning. Taking into consideration the last decade, it can be claimed that the increasing usage of system architectures based on dispersed applications is a constant trend.Thus, the further perspective of distributed processing in industrial systems is promising. As usual, the advances in distributed data processing will be propelled by requirements imposed by production quality, efficiency, sustainability, flexibility, and new industrial technologies. The current stage of research in the domain is on the verge of another breakthrough. The old approaches that are still widely used reached the mature stage. Many existing solutions are based on technologies invented in the 1990s and before. For instance, recent statistical reports show that systems worth more than 50 billion dollars worldwide are more than 20 years old [1].This also pushes for the continuous development of new IT technologies, and despite the natural inertia in automation, they are being introduced into new solutions. Moreover, there is strong drive in industry to design, create, and analyze systems in entirely new ways.Currently, automation systems are mostly designed based on distributed architectures. In a typical approach, such systems are considered as a set of various computerized nodes, physically scattered on the shop floor, and connected together via computer networks [2]. The whole system, seen as a holistic but abstract entity, delivers several functionalities needed for the control and maintenance of a technological process. The system is processing information coming from a given industrial process. Information is coded as a set of data in the form of