Entries Tagged as 'Malware '

Modeling Malware with Suffix Trees

CERT , Malware No Comments »

By Will Casey
Senior Researcher
CERT

Will CaseyThrough our work in cyber security, we have amassed millions of pieces of malicious software in a large malware database called the CERT Artifact Catalog. Analyzing this code manually for potential similarities and to identify malware provenance is a painstaking process. This blog post follows up our earlier post to explore how to create effective and efficient tools that analysis can use to identify malware.

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A Summary of Key SEI R&D Accomplishments in 2011

Acquisition , Agile , Architecture Documentation , Binaries , Cyber-physical Systems , Fuzzy Hashing , Handheld Devices , Malware , Measurement & Analysis , Resilience Management Model (RMM) , Safety-Related Requirements , Security-Related Requirements , Software Cost Estimates , Team Software Process (TSP) , Technical Debt 1 Comment »

By Douglas C. Schmidt
Chief Technology Officer

Douglas C. SchmidtA 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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Fuzzy Hashing Against Different Types of Malware

CERT , Fuzzy Hashing , Malware No Comments »

By David French,
CERT Senior Researcher

David FrenchMalware, 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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Using Machine Learning to Detect Malware Similarity

Machine Learning , Malware 3 Comments »

By Sagar Chaki, Senior Member of the Technical Staff
Research, Technology, and System Solutions

Sagar Chaki Malware, which is short for “malicious software,” consists of programming aimed at disrupting or denying operation, gathering private information without consent, gaining unauthorized access to system resources, and other inappropriate behavior. Malware infestation is of increasing concern to government and commercial organizations. For example, according to the Global Threat Report from Cisco Security Intelligence Operations, there were 287,298 “unique malware encounters” in June 2011, double the number of incidents that occurred in March. To help mitigate the threat of malware, researchers at the SEI are investigating the origin of executable software binaries that often take the form of malware. This posting augments a previous posting describing our research on using classification (a form of machine learning) to detect “provenance similarities” in binaries, which means that they have been compiled from similar source code (e.g., differing by only minor revisions) and with similar compilers (e.g., different versions of Microsoft Visual C++ or different levels of optimization).

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Fuzzy Hashing Techniques in Applied Malware Analysis

CERT , Fuzzy Hashing , Malware 3 Comments »

By David French,
CERT Senior Researcher

David French 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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