Modified graph-based algorithm to analyze security threats in IoT

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.