US Patent 11714903 Sample traffic based self-learning malware detection

Techniques for sample traffic based self-learning malware detection are disclosed. In some embodiments, a system/process/computer program product for sample traffic based self-learning malware detection includes receiving a plurality of samples for malware detection analysis using a sandbox; executing each of the plurality of samples in the sandbox and monitoring network traffic during execution of each of the plurality of samples in the sandbox; detecting that one or more of the plurality of samples is malware based on automated analysis of the monitored network traffic using a command and control (C2) machine learning (ML) model if there is not a prior match with an intrusion prevention system (IPS) signature; and performing an action in response to detecting that the one or more of the plurality of samples is malware based on the automated analysis of the monitored network traffic using the C2 ML model. In some embodiments, the IPS signatures and C2 ML model are automatically generated and trained.