Visible to the public Biblio

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Li, Ming, Hawrylak, Peter, Hale, John.  2019.  Concurrency Strategies for Attack Graph Generation. 2019 2nd International Conference on Data Intelligence and Security (ICDIS). :174-179.

The network attack graph is a powerful tool for analyzing network security, but the generation of a large-scale graph is non-trivial. The main challenge is from the explosion of network state space, which greatly increases time and storage costs. In this paper, three parallel algorithms are proposed to generate scalable attack graphs. An OpenMP-based programming implementation is used to test their performance. Compared with the serial algorithm, the best performance from the proposed algorithms provides a 10X speedup.

Hill, Zachary, Chen, Samuel, Wall, Donald, Papa, Mauricio, Hale, John, Hawrylak, Peter.  2017.  Simulation and Analysis Framework for Cyber-Physical Systems. Proceedings of the 12th Annual Conference on Cyber and Information Security Research. :7:1–7:4.

This paper describes a unified framework for the simulation and analysis of cyber physical systems (CPSs). The framework relies on the FreeBSD-based IMUNES network simulator. Components of the CPS are modeled as nodes within the IMUNES network simulator; nodes that communicate using real TCP/IP traffic. Furthermore, the simulated system can be exposed to other networks and the Internet to make it look like a real SCADA system. The frame-work has been used to simulate a TRIGA nuclear reactor. This is accomplished by creating nodes within the IMUNES network capable of running system modules simulating different CPS components. Nodes communicate using MODBUS/TCP, a widely used process control protocol. A goal of this work is to eventually integrate the simulator with a honeynet. This allows researchers to not only simulate a digital control system using real TCP/IP traffic to test control strategies and network topologies, but also to explore possible cyber attacks and mitigation strategies.

Cook, Kyle, Shaw, Thomas, Hawrylak, Peter, Hale, John.  2016.  Scalable Attack Graph Generation. Proceedings of the 11th Annual Cyber and Information Security Research Conference. :21:1–21:4.

Attack graphs are a powerful modeling technique with which to explore the attack surface of a system. However, they can be difficult to generate due to the exponential growth of the state space, often times making exhaustive search impractical. This paper discusses an approach for generating large attack graphs with an emphasis on scalable generation over a distributed system. First, a serial algorithm is presented, highlighting bottlenecks and opportunities to exploit inherent concurrency in the generation process. Then a strategy to parallelize this process is presented. Finally, we discuss plans for future work to implement the parallel algorithm using a hybrid distributed/shared memory programming model on a heterogeneous compute node cluster.