Attack scenario recognition through heterogeneous event stream analysis

S. Mathew, S. Upadhyaya
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引用次数: 4

Abstract

Stealthy, goal-oriented multistage attacks are difficult to detect since they often consist of specific attack steps that do not cause significant variations in the statistical distributions of data streams. We present an approach for attack scenario detection and recognition that is based on analyzing data streams from multiple heterogeneous sensors. Events captured from these sensors are used to generate high-dimensional state vectors that characterize overall system-wide activity. Monitoring the time series of these state vectors through Principal Component Analysis forms the basis of an anomaly detection technique for real-time scenario detection. Data traffic from a real network that emulates a military intelligence network is used to test and validate this approach. Results indicate that our approach is both effective and has low computational requirements, making it a candidate for practical implementation.
通过异构事件流分析识别攻击场景
隐蔽的、面向目标的多阶段攻击很难被检测到,因为它们通常由特定的攻击步骤组成,这些攻击步骤不会导致数据流统计分布的显著变化。我们提出了一种基于分析来自多个异构传感器的数据流的攻击场景检测和识别方法。从这些传感器捕获的事件用于生成表征整个系统范围活动的高维状态向量。通过主成分分析监测这些状态向量的时间序列构成了实时场景检测异常检测技术的基础。来自模拟军事情报网的真实网络的数据流量用于测试和验证该方法。结果表明,我们的方法既有效又具有较低的计算需求,使其成为实际实现的候选方法。
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