Big Data Analytics for Security

06 Sep UEBA Should Provide Conclusions, Not Just Anomalies

As with all technologies, UEBA has evolved over time. We at Fortscale take a great deal of pride in working closely with both our large and small customers to understand their specific needs and to respond to those needs. Although UEBA has always provided more information and context then just SIEM or event logs, after listening carefully to our customers, we...

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Fort Friday Security Clips

06 May Fort Friday Security Clips— Fortscale, Locky & DDoS

Fortscale is growing: We recently added three key members to our expanding leadership team. Joining Fortscale are David Somerville as Senior Vice President, Worldwide Sales, Sathvik Krishnamurthy to Fortscale’s Board of Directors, and Patrick Heim to the Fortscale Advisory Board. All three offer deep security and executive experience, and will help guide Fortscale as it enters its next phase of growth. “Fortscale’s...

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Gambling with Sloppy Security Practices

05 May Gambling with Sloppy Security Practices

  This week I’ve been studying a number of recent, very large data breaches. The victims all had one thing in common—sloppy security practices. Unfortunately these organizations are not alone in their casual attitudes about security. Everywhere we look I see evidence that far too many companies are playing a very high stakes game of gambling with sloppy security practices. Here are...

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31 Dec HOW CAN WE DEFINE SECURITY ANALYTICS?

Big Data Analytics for Security refers to a process of analyzing massive amounts of structured and unstructured data from hundreds of sources – including system logs, network devices, IP addresses, emails, conclusive information derived from other attack investigations, third party research and more – in order to recognize patterns or anomalies, analyze trends, verify alerts and security events, and ultimately...

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31 Dec DISCOVERY OF UNDER THE RADAR THREATS CAN BE PRODUCED ONLY BY ANALYZING HISTORICAL DATA

Big Data’s analytical scope isn’t limited to current data or real-time monitoring. It also looks at an organization’s historical data from months and years ago, and scans it against an increasing database of user behavioral profiles, and other data analytical factors, in order to find real or potential threats, and fortify your network from targeted attacks....

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31 Dec THE INSIGHTS ARE DERIVED FROM THE DATA, USING MACHINE LEARNING ALGORITHMS

Crunching vast amounts of historical data from multiple sources requires not only a robust Big Data Analytics platform, but also machine learning algorithms. These advanced algorithms can pinpoint the best-hidden needle in your big data haystack, with no predefined rules or heuristics. These algorithms can automatically classify alerts and entity behavior as "normal" or "suspicious", based solely on learning historical...

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