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A2G2V: Automatic Attack Graph Generation and Visualization and Its Applications to Computer and SCADA Networks.
- Source :
-
IEEE Transactions on Systems, Man & Cybernetics. Systems . Oct2020, Vol. 50 Issue 10, p3488-3498. 11p. - Publication Year :
- 2020
-
Abstract
- Securing cyber-physical systems (CPS) and Internet of Things (IoT) systems requires the identification of how interdependence among existing atomic vulnerabilities may be exploited by an adversary to stitch together an attack that can compromise the system. Therefore, accurate attack graphs play a significant role in systems security. A manual construction of the attack graphs is tedious and error-prone, this paper proposes a model-checking-based automated attack graph generator and visualizer (A2G2V). The proposed A2G2V algorithm uses existing model-checking tools, an architecture description tool, and our own code to generate an attack graph that enumerates the set of all possible sequences in which atomic-level vulnerabilities can be exploited to compromise system security. The architecture description tool captures a formal representation of the networked system, its atomic vulnerabilities, their pre-and post-conditions, and security property of interest. A model-checker is employed to automatically identify an attack sequence in the form of a counterexample. Our own code integrated with the model-checker parses the counterexamples, encodes those for specification relaxation, and iterates until all attack sequences are revealed. Finally, a visualization tool has also been incorporated with A2G2V to generate a graphical representation of the generated attack graph. The results are illustrated through application to computer as well as control (SCADA) networks. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 21682216
- Volume :
- 50
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Systems, Man & Cybernetics. Systems
- Publication Type :
- Academic Journal
- Accession number :
- 146012239
- Full Text :
- https://doi.org/10.1109/TSMC.2019.2915940