The Quiet Victories and False Promises of Machine Learning in Security

Machine learning (ML) is a collection of statistical techniques that can unlock insights and scale when applied to narrowly scoped problems with large data sets, but it is not a one-size-fits-all solution, particularly in cybersecurity. In security, ML is best used for specific tasks like signatureless malware detection and network anomaly detection, where it complements traditional methods, though it can struggle with false positives and negatives due to its probabilistic nature. While ML can detect anomalies in predictable systems, its effectiveness depends heavily on the quality and quantity of data it is trained on, as well as the stability of the environment. Many organizations misuse the term ML, often conflating it with basic statistical methods, which underscores the importance of integrating ML tools thoughtfully into existing workflows to enhance rather than replace existing systems for optimal efficiency. As demonstrated by Sysdig's ML system for miner detection, ML can be powerful when combined with human analysis to verify and refine its results.