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The SEI Blog continues to attract an ever-increasing number of readers interested in learning more about our work in agile metrics, high-performance computing, malware analysis, testing, and other topics. As we reach the mid-year point, this blog posting highlights our...

 The 2014 Year in Review: Top 10 Blog Posts

In 2014, the SEI blog has experienced unprecedented growth, with visitors in record numbers learning more about our work in big data, secure coding for Android, malware analysis, Heartbleed, and V Models for Testing. In 2014 (through December 21), the...

 The SEI Blog: A Two-Year Retrospective

In launching the SEI blog two years ago, one of our top priorities was to advance the scope and impact of SEI research and development projects, while increasing the visibility of the work by SEI technologists who staff these projects....

 Writing Effective YARA Signatures to Identify Malware

In previous blog posts, I have written about applying similarity measures to malicious code to identify related files and reduce analysis expense. Another way to observe similarity in malicious code is to leverage analyst insights by identifying files that possess...

 Modeling Malware with Suffix Trees

Through 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...

 A Summary of Key SEI R&D Accomplishments in 2011

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,...

 Fuzzy Hashing Against Different Types of Malware

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...

 Using Machine Learning to Detect Malware Similarity

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...

 Fuzzy Hashing Techniques in Applied Malware Analysis

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...



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