摘要： While ML can dramatically enhance an organization's security posture, it is critical to understand some of its challenges when designing security strategies.
The growth of machine learning and its ability to provide deep insights using big data continues to be a hot topic. Many C-level executives are developing deliberate ML initiatives to see how their companies can benefit, and cybersecurity is no exception. Most information security vendors have adopted some form of ML, however it’s clear that it isn’t the silver bullet some have made it out to be.
While ML solutions for cybersecurity can and will provide a significant return on investment, they do face some challenges today. Organizations should be aware of a few potential setbacks and set realistic goals to realize ML’s full potential.
False positives and alert fatigue
The greatest criticism of ML-detection software is the “impossible” number of alerts it generates -- think millions of alerts per day, effectively delivering a denial-of-service attack against analysts. This is particularly true of “static analysis” approaches that rely heavily on how threats look.
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