Will machine learning transform the security industry? The answer is a resounding yes. Organizations already use machine learning technologies to block spam email, malicious websites, and malware. However, organizations’ approach to breach detection has been much less cutting edge, with many relying on a combination of manual analysis and static tools to identify network intrusions, or an over-emphasis on finding malware instead of looking for malicious behaviors. These antiquated detection techniques help explain why it takes the typical organization 146 days¹ to detect a breach.
Machine learning is poised to change all of that; with machine learning, security tools can identify the mis-behaving needle in the proverbial haystack of network traffic. By learning good traffic and profiling user activity over time, analytics-based security tools can catch the attacks that evade legacy detection approaches.
Read this white paper to learn:
• Why signature-based security tools have reached their useful limits
• How profiling known good behavior can help distinguish attacks from normal activity
• Which inputs are required for effective behavior analytics tools
• How machine learning can reduce the time to detection and the cost of a data breach
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