By John Pirc, David DeSanto, Iain Davison, Will Gragido
Drawing upon years of functional event and utilizing quite a few examples and illustrative case experiences, probability Forecasting: Leveraging titanic info for Predictive research discusses very important themes, together with the risk of utilizing old facts because the foundation for predicting destiny breaches, the right way to use safeguard intelligence as a device to improve possibility forecasting strategies, and the way to take advantage of probability info visualization innovations and chance simulation instruments. Readers will achieve worthy protection insights into unstructured gigantic info, in addition to strategies on the way to use the information to their virtue to minimize danger.
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Extra resources for Threat Forecasting Leveraging Big Data for Predictive Analysis
Example text
Models study historic and current behaviors and trends with the ability to apply those results to various models in order provide predictable future outcomes. In some of the aforementioned models one could argue that it is more of an art form or that it depends on luck instead of being a science with 100% accuracy. However, the current security controls offered in the market today are becoming smarter and utilizing behavior analytics and math-ematics in order to detect and prevent threats. This is a major step forward in solving the time-to-detection problem and insider threat but it is still a reactionary response if the threat is able to make its way to the end-point.
This is an important point because most of the security ven- dors will release mid-year and yearly trend reports that look back at the most critical vulnerabilities and malware that were preva- lent over said period of time. Although this information is interest-ing and provides a nice read, how are they using this information to better assess the future? The reports are informative about all the threats that you were likely to have been exposed to over the past year, but it honestly does nothing for your security posi-tion today and you are likely making buying decisions to protect your infrastructure from threats that have already expired.
The authors of Threat Forecasting apply this same principle to the value of intelligent threat exchange. The authors explore how your organization can benefit from intelligent analysis of real-time threat information. Just as Metcalfe’s law describes the benefit of the computer network, so too do the authors educate us about the benefit of leveraging external and internal sources of Indica-tors of Interest (IOI), Indicators of Attack (IOA) and Indicators of Compromise (IOC). 0 architectures and the inherent challenges of maintaining security and control of the network and its sensitive data.
Threat Forecasting Leveraging Big Data for Predictive Analysis by John Pirc, David DeSanto, Iain Davison, Will Gragido
by Michael
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