CAPTURE: Cyberattack Forecasting Using Non-Stationary Features with Time Lags
作者： Ahmet Okutan , Shanchieh Jay Yang , Katie McConky , Gordon Werner
关键词: Computer science 、 Concept drift 、 Volume (computing) 、 Baseline model 、 Variety (cybernetics) 、 Range (statistics) 、 Data mining 、 Early signs 、 Task (project management) 、 Third party
摘要: Forecasting cyberattacks before they occur is an important yet challenging task, as exploring early signs of attack from a large volume data not trivial. This paper describes the design and evaluation novel automated system, CAPTURE, which uses broad range unconventional signals derived various open sources to forecast towards target organization anonymized CorpX. It includes approaches select relevant significant, but redundant, lagged treat non-stationary relationships between cyberattack occurrences. Using cyber incidents recorded by third party 146 variety sources, this demonstrates that CAPTURE performs significantly better than baseline model with configurations. Furthermore, offers insights human analysts on how specific contributed forecasts.
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