Crowdstrike, Inc.

Cristian Viorel Popa

Stefan-Bogdan Cocea

Alexandru Dinu

Marian Radu

Mihaela-Petruta Gaman

Paul Sumedrea

Damian Monea

Mihaela Petruta Garan

A command line anomaly detection system can generate anomaly scores associated with command line entries, such that command line entries associated with the highest anomaly scores can be identified. The command line anomaly detection system can include a transformer model trained, via unsupervised machine learning, to determine meanings of components of individual command line entries. The command line anomaly detection system can also include an anomaly detection model trained, via unsupervised machine learning, to determine anomaly scores based on the meanings of components of individual command line entries determined by the transformer model.

Anomalous command line entry detection

Methods and systems for applying a diffusion model to adversarial purification and generating adversarial samples in malware detection are disclosed. According to an example, a malware file is inputted to a diffusion model to obtain an adversarial sample by altering content of the malware file. The adversarial sample is further tested by a malware detector. In some examples, the content of an input file may be encoded prior to be processed by the diffusion model. If the malware detector can identify the adversarial sample as a malware file, the diffusion model is updated to further alter the content until the adversarial sample successfully deceives the malware detector. According to another example, an executable file is purified using a diffusion model prior to be inputted to a malware detector. The diffusion model may remove potential malware content from the executable file, thus improving the performance of the malware detector.

Applying diffusion models in adversarial purification and adversarial sample generation in cybersecurity

The present disclosure provides an approach of providing, to an artificial intelligence (AI) model, a malicious script that includes a malicious behavior. The AI model is configured to modify software code of the malicious script to produce modified software code that obfuscates the malicious behavior. The approach produces, by a processing device using the AI model, an adversarial script that includes the modified software code that obfuscates the malicious behavior. In turn, the approach initiates a malware detector to test the adversarial script.

Adversarial script generation using artificial intelligence models

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Bucharest, Romania

Cristian Viorel Popa