海报：面向选择威胁适配的工业入侵检测系统
Poster: Towards Selecting Threat Appropriate Industrial Intrusion Detection Systems
- RWTH Aachen University（亚琛工业大学）
- Fraunhofer FKIE（弗劳恩霍夫FKIE研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对工业控制系统威胁动态变化，提出基于反威胁情报共享的检测器选择机制，通过攻击层面性能评估验证其能提升安全防护效果。
AI中文摘要：
由于针对工业控制系统的威胁态势是动态变化的，有效的安全防护需要能够及时应对这些不断演变的威胁状况的检测策略。为解决这一问题，我们提出了一种基于反威胁情报共享的机制构想，以根据当前环境选择适当的检测器。为突出该机制的潜力，我们对多种入侵检测系统进行了攻击层面的性能评估。结果表明，入侵检测性能因攻击场景而异，这强调了此类机制对工业控制系统安全的价值。
英文摘要：
As the threat landscape against industrial control systems is dynamic, effective security requires detection strategies capable of timely reactions to these evolving threat situations. To address this problem, we propose the idea of a counter-threat intelligence sharing based mechanism to select appropriate detectors for current circumstances. To highlight the potential of this mechanism, we conduct attack-level performance evaluations of various intrusion detection systems. Results show the variance of intrusion detection performance depending on the attack scenario, emphasizing the benefits of such a mechanism for industrial control system security
