By Douglas C. Schmidt
Chief Technology Officer

A key mission of the SEI is to advance the practice of software engineering and cyber security through research and technology transition to ensure the development and operation of software-reliant Department of Defense (DoD) systems with predictable and improved quality, schedule, and cost. To achieve this mission, the SEI conducts research and development (R&D) activities involving the DoD, federal agencies, industry, and academia. One of my initial blog postings summarized the new and upcoming R&D activities we had planned for 2011. Now that the year is nearly over, this blog posting presents some of the many R&D accomplishments we completed in 2011.

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By David French,
CERT Senior Researcher

Malware, which is short for “malicious software,” is a growing problem for government and commercial organizations since it disrupts or denies important operations, gathers private information without consent, gains unauthorized access to system resources, and other inappropriate behaviors. A previous blog post described the use of  “fuzzy hashing” to determine whether two files suspected of being malware are similar, which helps analysts potentially save time by identifying opportunities to leverage previous analysis of malware when confronted with a new attack.  This posting continues our coverage of fuzzy hashing by discussing types of malware against which similarity measures of any kind (including fuzzy hashing) may be applied.

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By David French,
CERT Senior Researcher

Malware—generically defined as software designed to access a computer system without the owner’s informed consent—is a growing problem for government and commercial organizations.  In recent years, research into malware focused on similarity metrics to decide whether two suspected malicious files are similar to one another. Analysts use these metrics to determine whether a suspected malicious file bears any resemblance to already verified malicious files. Using these metrics allows analysts to potentially save time, by identifying opportunities to leverage previous analysis. This post will describe our efforts to develop a technique (known as fuzzy hashing) to help analysts determine whether two pieces of suspected malware are similar.

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