When it comes to security, the industry often focuses its efforts on reactive analysis. To achieve future-proof security, you need real-time threat intelligence. And for real-time threat intelligence, machine learning is the key ingredient. But not all machine learning is the same. In this edition of the quarterly threat trends, you’ll see real-world examples of how machine learning analysis makes security products more accurate, and how it accelerates classification. You'll also get our inside perspective on threat predictions for 2019.
Machine Learning in Webroot’s DNA
Machine learning is more than marketing hype. It’s what makes real-time threat intelligence possible, and it’s what helps prevent false positive detections. Not all machine learning is created equal. Learn how to measure powerful machine learning, and how it creates equally powerful threat intelligence.
Webroot Predictions for 2019
Experts across the global Webroot team, from Threat Research to Legal to Communications and more, share their cybersecurity-related hopes and fears for the coming year. Learn more in our new blog entry: Cybersecurity Predictions for 2019.
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Machine Learning at Work
To stop emerging threats in real time, you need machine learning—period. But when you’re evaluating possible vendor partners, hearing a lot industry jargon and daunting numbers can leave you feeling frustrated and uncertain. Our infographic brings machine learning down to earth with a concrete example of our threat categorization process.