Security information and event management

To detect advanced attacks, multiple data types such as network packet, log, endpoint, and cloud data need to be combined. These data sources provide the ability for RSA NetWitness Logs and Packets to discover attacks missed by log-centric SIEM and signature-based tools with the only solution that can correlate all of the needed data sources and apply advanced behavioral techniques and data science models. Using these advanced techniques in combination can provide security teams with speedier detection and all the visibility required to respond to advanced, but common attack tools that employ covert channels with C2 threats.

The use of detective analytics is now a central piece of security architectures, as security professionals are increasingly encountering a needle-in-the-haystack problem. Security tools – especially rule based ones – as well as systems, applications, and infrastructure, create so much data that it’s tough to uncover the signal of a real attack. Behavior analytics tools help make sense of the vast amount of data that these systems generate. While many vendors claim to use behavioral techniques, they fail to integrate them with other data science models to deal with today’s nation state or hacktivist-type attacker. There are some real use cases where using behavioral techniques for data analysis can identify some of the most real and pressing detection issues security teams face, and that conventional analytic tools alone fail to spot. One among many of these use cases is trying to spot compromised hosts, both internal and external. Real threats don’t openly advertise themselves. They hide their activity among all the other things that are happening in today’s typically complex IT environments. These threats rely on the assumption that today’s security teams have neither the tools nor the time to investigate deeply enough to distinguish between their activity and those of employees, customers or partners. Today’s sophisticated attackers use ways to get information in and out of the organization that evade detection, leveraging what are known as “covert channels” that enable command and control (C2) of resources. Many successful recent public attacks have covert channels communicating with C2 servers that can fully compromise systems.
For example:

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Phishing scams typically use covert channels to deliver malware to victims, making it difficult to spot that initial “click” on the offending link.

After compromise, today’s threats often use covert channels to effect “command and control” of victim endpoints, hiding communication traffic amongst normal web traffic. In addition to behavior analytics to detect C2 activity, using data science techniques to spot the use of covert channels means that security teams can spot these sophisticated threats quicker, and reduce the likelihood that an attack harms the organization.

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