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- W2186670802 abstract "Stealthy, goal-oriented multistage attacks are difficult to detect since they often consist of spe cific attack steps that do not cause significant varia tions in the statistical distributions ofdata streams. We present an approach for attack scenario de tection and recognition that is based on analyzing data streams from multiple heterogeneous sensors. Events captured from these sensors are used to gen erate high-dimensional state vectors that charac terize overall system-wide activity. Monitoring the time series ofthese state vectors through Principal Component Analysis forms the basisofan anomaly detection technique for real-time scenario detec tion. Data traffic from a real network that emu lates a military intelligence network is used to test and validate this approach. Results indicate that our approach is both effective and has low com putational requirements, making it a candidate for practical implementation." @default.
- W2186670802 created "2016-06-24" @default.
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- W2186670802 date "2009-01-01" @default.
- W2186670802 modified "2023-09-26" @default.
- W2186670802 title "ATTACK SCENARIORECOGNITIONTHROUGHHETEROGENEOUS EVENT STREAM ANALYSIS" @default.
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