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Abstract
Complex Engineering Systems are subject to cyber-attacks due to inherited vulnerabilities in the underlying entities constituting them. System Resiliency is determined by its ability to return to a normal state under attacks. In order to analyze the resiliency under various attacks compromising the system, a new concept of Hybrid Attack Graph (HAG) is introduced. A HAG is a graph that captures the evolution of both logical and real values of system parameters under attack and recovery actions. The HAG is generated automatically and visualized using Java based tools. The results are illustrated through a communication network example.
Highlights
As a result of the rapid advancement of complex engineering systems such as infrastructure, communications, energy systems, industrial automation, artificial intelligence, and cyber-physical systems, new research directions in modeling, monitoring, diagnosis, optimization, and control have emerged in recent years [1]
This paper extends the conference version [20] significantly by introducing the concept of the Hybrid Attack Graph (HAG), and a new developed tool for its automatic generation (AHAG)
One research implemented a tool that consisted of three main pieces: a model builder, an attack graph generator (SPIN), and a Graphical User Interface (GUI) for graphical presentation
Summary
As a result of the rapid advancement of complex engineering systems such as infrastructure, communications, energy systems, industrial automation, artificial intelligence, and cyber-physical systems, new research directions in modeling, monitoring, diagnosis, optimization, and control have emerged in recent years [1]. For fault diagnosis of complex industrial system to design a different kind of executable model They proposed to build grey-box models based on a state space neural network architecture derived from that structural information in the PC, which links measurements with equations, and with parameters related to faulty behavior. Extending the Marginal Distribution Adaptation (MDA) to Joint Distribution Adaptation (JDA), the proposed framework can exploit the discrimination structures associated with the labeled data in source domain to adapt the conditional distribution of unlabeled target data, and guarantee a more accurate distribution matching [12] They reviewed over 220 technical research programs in total, with more attention on the recent developments of the fault diagnosis approaches and their applications during the last decade.
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