The arms race: Adversarial search defeats entropy used to detect malware

•We expose the two sides of the malware arms race: detection and evasion.•We propose EnTS, a novel and scalable malware detection technique.•EnTS improves the accuracy of its competitors, being up to 3000 times faster.•To defeat EnTS, we create EEE, an evasion technique with learning abilities.•EEE defeats EnTS and SoA detectors, pushing their false negatives up to a 90%. Malware creators have been getting their way for too long now. String-based similarity measures can leverage ground truth in a scalable way and can operate at a level of abstraction that is difficult to combat from the code level. At the string level, information theory and, specifically, entropy play an important role related to detecting patterns altered by concealment strategies, such as polymorphism or encryption. Controlling the entropy levels in different parts of a disk resident executable allows an analyst to detect malware or a black hat to evade the detection. This paper shows these two perspectives into two scalable entropy-based tools: EnTS and EEE. EnTS, the detection tool, shows the effectiveness of detecting entropy patterns, achieving 100% precision with 82% accuracy. It outperforms VirusTotal for accuracy on combined Kaggle and VirusShare malware. EEE, the evasion tool, shows the effectiveness of entropy as a concealment strategy, attacking binary-based state of the art detectors. It learns their detection patterns in up to 8 generations of its search process, and increments their false negative rate from range 0–9%, up to the range 90–98.7%.