Entries by 'Sagar Chaki'

Semantic Comparison of Malware Functions

Binaries , Malware No Comments »

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

Sagar ChakiA malicious program disrupts computer operations, gains access to private computational resources, or collects sensitive information. In February 2012, nearly 300 million malicious programs were detected, according to a report compiled by SECURELIST. To help organizations protect against malware, I and other researchers at the SEI have focused our efforts on trying to determine the origin of the malware. In particular, I’ve recently worked with my colleagues—Arie Gurfinkel, who works with me in the SEI’s Research, Technology, & System Solutions Program, and Cory Cohen, a malware analyst with the CERT Program—to use the semantics of programming languages to determine the origin of malware. This blog post describes our exploratory research to derive precise and timely actionable intelligence to understand and respond to malware.

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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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Learning a Portfolio-Based Checker for Provenance-Similarity of Binaries

Binaries , Malware No Comments »

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

Sagar Chaki As software becomes an ever-increasing part of our daily lives, organizations find themselves relying on software that originates from unknown and untrusted sources. The vast majority of such software is available only as executables, known as “binaries.” Many binaries—such as malware or different versions and builds of a software package—are simply minor variants of old programs (or in some cases exact copies) that have been run through a different compiler. This blog post explains how the ability to detect similarities among binaries is an important tool in malware detection and a growing area of research.

 

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