<ul><li>Machine learning (ML) plays a pivotal role in detecting malicious software.</li><li>Inflated results in malware detection are due to spatial and temporal biases in experimental design.</li><li>TESSERACT introduces constraints for fair experiment design and proposes a new metric, AUT, for classifier robustness.</li><li>Performance enhancements are possible through periodic tuning and mitigation strategies.</li></ul>

TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)

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