Why the Cyber Security Problem Is Actually a Big Data Problem

The demands of continuous monitoring are leading federal agencies to shift their big data capabilities toward boosting their cyber posture.

July 2, 2014 - In 2013, the federal government was the target of 46,160 cyber attacks, according to an April report by the Government Accountability Office, a 33 percent jump from the year prior. While most Americans know the name Edward Snowden, fewer know that in June 2013 hackers stole personally identifiable information from the Department of Energy, costing taxpayers $3.7 million, or that in January, investigators uncovered a malware hosting service responsible for infecting millions of computers worldwide – including those belonging to NASA and other agencies.

As agencies look to strategies to detect and report unauthorized network access, one technique, continuous diagnostics and mitigation (CDM), has garnered the most attention. Initially developed as a risk management framework by NIST, CDM works by deploying automated sensors to give IT personnel a real-time picture of the threat landscape.

The hope is that by shifting cyber security operations toward a more dynamic posture, agencies will be better able to detect, quarantine, and report intrusions more quickly, thereby minimizing potential damage to critical networks and infrastructure. The positives of continuous monitoring are hard to overstate. The Center for Strategic and International Studies estimates that government-wide adoption of CDM could prevent up to 85 percent of the cyber attacks that agencies currently face.

However, federal officials are finding that perhaps the greatest challenge in adopting continuous monitoring is the sheer volume of data generated. For the average agency with a 10Gb/second Internet connection, this means sorting through roughly 100 Tb (terabytes!) per day. What was previously a security problem has evolved into a big data problem, placing serious demands on the federal government’s aging storage infrastructure.

Effective CDM requires not only raw storage capacity, but also tools to sift through massive data inflows and detect anomalies created by unauthorized users and applications. The high bandwidth needs of continuous monitoring frequently pose problems for security information and event management (SIEM) technologies – until recently a mainstay in the cyber professional's toolbox – since their limitations in handling large data streams can lead to security gaps.

As a result, agencies are increasingly turning to big data analytics as the solution. DISA, for instance, is in the process of adapting its cloud-based analytics platform, known as Acropolis, for CDM applications. Once complete, Acropolis will provide continuous monitoring, cyber attack analysis, and insider threat analysis for the whole of DoD, allowing security officials to conduct risk analyses every three days instead of every three years.

Progress in continuous monitoring isn’t limited solely to the Defense Department. In mid-July the General Services Administration will begin launching the second round of awards related to its CDM-as-a-service bulk contract, which will focus on raising the baseline monitoring capabilities of all civilian agencies. The first round, launched in January, gave agencies greater access to asset management and software assurance tools to secure endpoints against intrusion. Further, by early next year the Department of Homeland Security plans outfit each agency with a diagnostic dashboard capable of assessing and prioritizing trouble spots needing attention.

With cyber threats showing no signs of slowing down, more and more agencies will begin to see an operational role for big data analytics beyond performance management or fraud detection. Protecting the nation's critical networks and infrastructure will require the vigilance that only real-time data analysis can provide.


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