Implementing an Attack Graph Generator in CUDA

Li, Ming; Hawrylak, Peter J.; Hale, John

doi:10.1109/ipdpsw50202.2020.00128

Cited by 3 publications

(1 citation statement)

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“…Even with pruning criteria, attack graph generation can be computationally intensive. Research has been performed on accelerating the computation through algorithmic and hardware improvements (Li et al, 2020) (Li et al, 2021). However, performance can also be improved by restricting the state-transition model.…”

Section: Attack Graphsmentioning

confidence: 99%

Decolonizing Information Technology Design: A Framework for Integrating Indigenous Knowledge in Design Science Research

Chung,

Shedlock,

Corbett

2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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Many self-adaptive, autonomous systems rely on component technologies to report anomalies to planning processes that can choose adaptations. What if the analysis technologies themselves need to be adapted? We consider an intrusion detection system (IDS) supported by two component technologies that assist its decision making: a neural network that finds security anomalies and an attack graph that informs the IDS about system states of interest. The IDS purpose is to send alerts regarding security anomalies. Planning processes respond to alerts by selecting mitigation strategies. Mitigations are imposed system-wide and can result in adaptations to the analysis technology, such as the IDS. Without adaptation the IDS can become stagnate in its detection quality. In this paper, we describe an architectural design for an adaptive layer that works directly with an IDS. We examine two use cases involving different mitigation strategies and their impacts on the IDS's supporting components.

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Section: Attack Graphsmentioning

confidence: 99%