By using MOREAL Threat Intelligence platform, organizations gain awareness with regard to the security incidents and threats that take place within their infrastructures.
To develop such threat awareness, MOREAL is based on big-data analytics principles, along with correlation of primal information brought out from logs provided by the underlying network and network security infrastructure.
More precisely, logs are initially analysed, correlated, and collated with Open Source and Crypteia Networks Security intelligence to generate secondary and trietary threat intelligence by the Threat Intelligence Engine of MOREAL platform.
Then our Engine augments threat knowledge by behavioural, and statistical analytics, as well as, reputation pattern matching. The MOREAL core reasoning process is found on computations on graph and meta-graph models that are generated from any internal and external connection that can be logged.
In particular, graphs and meta-graphs are processed with algorithms that compute efficiently plausible threat paths with a likelihood scoring approach based on observations of the protected infrastucture and Security Intelligence in terms of IP reputation, malware, and traffic patterns.
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