Once again, I’ve attended the CERIAS Security Symposium held on the campus of Purdue University. This is one of several posts summarizing the talks I attended. This post will also appear on the CERIAS Blog.
With “Big Data” being a hot topic in the information technology industry at large, it should come as no surprise that it is being employed as a security tool. To discuss the collection and analysis of data, a panel was assembled from industry and academia. Alok Chaturvedi, Professor of Management, and Samuel Liles Associate Professor of Computer and Information Technology, both of Purdue Unversity, represented academia. Industry representatives were Andrew Hunt, Information Security Research at the MITRE Corporation, Mamani Older, Citigroup’s Senior Vice President for Information Security, and Vincent Urias, a Principle Member of Technical Staff at Sandia National Laboratories. The panel was moderated by Joel Rasmus, the Director of Strategic Relations at CERIAS.
Professor Chaturvedi made the first opening remarks. His research focus is on reputation risk: the potential damage to an organization’s reputation – particularly in the financial sector. Reputation damage arises from the failure to meet the reasonable expectations of stakeholders and has six major components: customer perception, cyber security, ethical practices, human capital, financial performance, and regulatory compliance. In order to model risk, “lots and lots of data” must be collected; reputation drivers are checked daily. An analysis of the data showed that malware incidents can be an early warning sign of increased reputation risk, allowing organizations an opportunity to mitigate reputation damage.
Mister Hunt gave brief introductory comments. The MITRE Corporation learned early that good data design is necessary from the very beginning in order to properly handle a large amount of often-unstructured data. They take what they learn from data analysis and re-incorporate it into their automated processes in order to reduce the effort required by security analysts.
Mister Urias presented a less optimistic picture. He opened his remarks with the assertion that Big Data has not fulfilled its promise. Many ingestion engines exist to collect data, but the analysis of the data remains difficult. This is due in part to the increasing importance of meta characteristics of data. The rate of data production is challenging as well. Making real-time assertions from data flow at line rates is a daunting problem.
Ms. Older noted that Citigroup gets DDoS attacks every day, though some groups stage attacks on a somewhat predictable schedule. As a result, Citigroup employs a strong perimeter defense. She noted, probably hyperbolically, that it takes 20 minutes to boot her laptop. Despite the large volume of data produced by the perimeter defense tools, they don’t necessarily have good data on internal networks.
Professor Liles focused on the wealth of metrics available and how most of them are not useful. “For every meaningless metric,” he said, “I’ve lost a hair follicle. My beard may be in trouble.” It is important to focus on the meaningful metrics.
The first question posed to the panel was “if you’re running an organization, do you focus on measuring and analyzing, or mitigating?” Older said that historically, Citigroup has focused on defending perimeters, not analysis. With the rise of mobile devices, they have recognized that mere mitigation is no longer sufficient. The issue was put rather succinctly by Chaturvedi: “you have to decide if you want to invest in security or invest in recovery.”
How do organizations know if they’re collecting the right data. Hunt suggested collecting everything, but that’s not always an option, especially in resource-starved organizations. Understanding the difference between trend data and incident data is important, according to Liles, and you have to understand how you want to use the data. Organizations with an international presence face unique challenges since legal restrictions and requirements can vary from jurisdiction-to-jurisdiction.
Along the same lines, the audience wondered how long data should be kept. Legal requirements sometimes dictate how long data should be kept (either at a minimum or maximum) and what kind of data may be stored. The MITRE corporation uses an algorithmic system for the retention and storage medium for data. Liles noted that some organizations are under long-term attack and sometimes the hardware refresh cycle is shorter than the duration of the attack. Awareness of what local log data is lost when a machine is discarded is important.
Because much of the discussion had focused on ways that Big Data has failed, the audience wanted to know of successes in data analytics. Hunt pointed to the automation of certain analysis tasks, freeing analysts to pursue more things faster. Sandia National Labs has been able to correlate events across systems and quantify sensitivity effects.
One audience member noted that as much as companies profess a love for Big Data, they often make minimal use of it. Older replied that it is industry-dependent. Where analysis drives revenue (e.g. in retail), it has seen heavier use. An increasing awareness of analysis in security will help drive future use.
Other posts from this event:
