Machine Learning in the SOC The odds may appear stacked against today’s security operations centers (SOCs): more data, more sophisticated attack vectors, fewer resources, and a complex ecosystem of security tools. Anomaly detection and unsupervised machine learning can fuel next-generation security operations by helping SOC teams reclaim productivity and improve threat detection. White Paper Securitypage T able of Contents Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Machine Learning and Anomaly Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Select the Right T ool for the Job . . . . . . . . . . . . . . . .

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