Microsoft Technology Licensing, LLC

Laurent Visconti

Marcus Peinado

Luis Irun-Briz

Mark L Niehaus

Nikola J Livic

Aditya Bandi

Ishani Shailesh Parikh

Techniques are provided for ranking time-series including previously detected anomalous fact quantity changes over an associated time interval. Time-series are received, and for each time-series, a normalized fact quantity change is determined, and each time-series is ranked based in part on the normalized fact quantity change. A normalized fact quantity change may be determined by determining a normalization factor over the time interval, and then determining a product of the normalization factor and the absolute value of the fact quantity change of that time interval. Alternatively, a normalized fact quantity change may be the product of the normalization factor, a predetermined order factor, and the absolute value of the fact quantity change. The normalization factor is determined by analyzing the distribution of the fact quantity change over dimension values of the dimension(s) associated with the time-series to determine the number of values in which the fact quantity is concentrated.

Insight ranking based on detected time-series changes

Malware may be identified based on attempts to use tainted data in certain ways, such as by attempting to execute the tainted data, by attempting to modify execution control based on tainted data, or by attempting to apply an existing function to the tainted data. A data's taint is determined based on the location from which the data originates. When data from a tainted source is moved to an otherwise non-tainted destination, the taint may be propagated from the source to the destination, to indicate that the destination is now of unknown safety. A component may be used to observe the operation of a process, in order to determine what data is being moved with respect to the process, and how that data is being used.

Malware detection with taint tracking

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Bainbridge Island, WA, United States of America

Laurent Visconti