Sophos Limited

Adarsh Dinesh Kyadige

Konstantin Berlin

Ben Uri Gelman

In example embodiments, techniques are provided to detect LOLBin attacks using a trained machine learning model that classifies command lines as benign or malicious. The machine learning model may be trained using a dataset of command line data that describes executed binary executable files, sourced from the log of events of compute instances. The dataset may be sampled using an approximate content-based logarithmic sampling algorithm (e.g., an algorithm that employs logarithmic sampling based on a locality sensitive hash, for example, a MinHash). The dataset may be labeled and featurized. The featurized labeled dataset may be used to train the machine learning model, which is then deployed to detect LOLBin attacks on a compute instance. In response to detection of a LOLBin attack, a remedial action may be performed on the compute instance.

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Thornton, CO, United States of America

Adarsh Dinesh Kyadige