Malware detection system attack prevention
AssigneeIntel Corporation
InventorLi Chen
Life of this patent
Estimating the term…
Abstract
Systems and methods may be used to prevent attacks on a malware detection system. A method may include modeling a time series of directed graphs using incoming binary files during training of a machine learning system and detecting, during a time-window of the time series, an anomaly based on a directed graph of the time series of directed graphs. The method may include providing an alert that the anomaly has corrupted the machine learning system. The method may include preventing or remedying corruption of the machine learning system.
Claims25
(canceled)
A method for preventing attacks on a malware detection system, the method comprising:
- during training of a machine learning system to detect malware attacks based on incoming binary files, detecting an anomaly in the incoming binary files;
- and
- providing an alert that the anomaly has corrupted the training of the machine learning system.
A malware detection system for preventing poison attacks, the malware detection system comprising:
- a processor of an anomaly detection system;
- and
- memory, the memory including instructions, which when executed by the processor, cause the processor to:
- during training of a machine learning system to detect malware attacks based on incoming binary files, detect an anomaly in the incoming binary files;
- and
- provide an alert that the anomaly has corrupted the training of the machine learning system.
At least one non-transitory machine-readable medium including instructions for preventing attacks on a malware detection system, which when executed by a machine, cause the machine to:
- during training of a machine learning system to detect malware attacks based on incoming binary files, detect an anomaly in the incoming binary files;
- and
- provide an alert that the anomaly has corrupted the training of the machine learning system.
Dependencies are inferred from the claim text.