Advances in Malware Detection and Evasion

The field of malware detection and evasion is rapidly evolving, with a focus on developing innovative methods to detect and classify malware, as well as techniques to evade detection. Dynamic analysis and deep learning are being increasingly used to improve malware detection accuracy and resilience against evasion strategies. Researchers are also exploring new approaches to generate highly obfuscated malicious code and to develop more effective attack strategies against behavioral malware detectors. Furthermore, there is a growing need for standardized benchmarks and frameworks to evaluate and compare the performance of different red teaming techniques. Overall, the field is moving towards more sophisticated and adaptive methods for both malware detection and evasion. Noteworthy papers include:

  • A dynamic malware categorization framework that uses CNNs and grayscale images to classify malware with high accuracy.
  • A reward-driven automated webshell malicious-code generator that produces diverse and highly obfuscated payloads.
  • An end-to-end adversarial framework that effectively evades behavioral malware detectors in both feature and problem spaces.
  • A lightweight benchmark for reinforcement fine-tuning-based red teaming that simplifies and standardizes the implementation and evaluation of RFT-based red teaming methods.

Sources

Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion

A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming

Tarallo: Evading Behavioral Malware Detectors in the Problem Space

RedRFT: A Light-Weight Benchmark for Reinforcement Fine-Tuning-Based Red Teaming

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