Elena Peterson's guest appearances

Elena Peterson

CERIAS Weekly Security Seminar - Purdue University

Ryan Henry

Cory Doctorow

Doug Smith

Larry Ponemon

George Heron

Laura Thomas

Eli Lilly

Stephen Elliott

Tony Sager

David Carroll

Jack Daniel

Joe Judge

Chris Reed

Richard III

John Richardson

OwlTail

Cyber security data  in many ways mimics the behavior of organic systems. Individuals or groups compete for limited resources using a variety of strategies, the most effective of which are re-used and refined in later ‘generations'. Traditionally this behavior has made detection of malware very difficult because 1) recognition systems are often built on exact matching to a pattern that can only be ‘learned' after a malicious entity reveals itself and 2) the enormous volume and variation in benign code is an overwhelming source of previously unseen entities that often confound detectors.  In addition, the enormous volume of malware artifacts is overwhelming anyone trying to categorize and characterize new additions to the many malware repositories as so much of the processing is done by hand.To turn the tables of complexity on the attackers, we have developed a method for mapping the sequence of behaviors that make up a malicious artifact to strings of text and analyze these strings using modified bioinformatics algorithms. Bioinformatics algorithms optimize the alignment between text strings even in the presence of mismatches, insertions or deletions and do not require an a priori definition of the patterns one is seeking. Nor do they require any type of exact matching. This allows the data itself to suggest meaningful patterns that are conserved between binaries. These patterns can be used to identify zero-day malware and can help to automate the curation and characterization of large quantities of suspected malware.  I will talk about our MLSTONES capabilities as an innovative and effective way of detecting and characterizing most types of malware artifacts.  I'll also discuss how these capabilities can be used on other types of cyber security data.  About the speaker: Elena Peterson --Ms Peterson joined PNNL in 1990 after getting her BS in Computer and Information Sciences from the University of Oregon.  She is currently a Senior Cyber Security Researcher in the Computation and Analytics Division.  Ms. Peterson has led the research, development, and management of multiple cross-disciplinary, multi-laboratory projects focused in the fundamental sciences and national security sectors.  Her work has included research and development of integrated computational environments for bioinformatics, physics, computational chemistry, and cyber security.  She is currently the principal investigator for the MLSTONES and mMutant projects, which applies algorithms and tools from the biological sciences to create new and innovative solutions to relevant cyber security problems thus merging two of her main interests.

<p>Cyber security data  in many ways mimics the behavior of organic systems. Individuals or groups compete for limited resources using a variety of strategies, the most effective of which are re-used and refined in later ‘generations'. Traditionally this behavior has made detection of malware very difficult because 1) recognition systems are often built on exact matching to a pattern that can only be ‘learned' after a malicious entity reveals itself and 2) the enormous volume and variation in benign code is an overwhelming source of previously unseen entities that often confound detectors.  In addition, the enormous volume of malware artifacts is overwhelming anyone trying to categorize and characterize new additions to the many malware repositories as so much of the processing is done by <a href="http://hand.To" rel="nofollow" target="_blank">hand.To</a> turn the tables of complexity on the attackers, we have developed a method for mapping the sequence of behaviors that make up a malicious artifact to strings of text and analyze these strings using modified bioinformatics algorithms. Bioinformatics algorithms optimize the alignment between text strings even in the presence of mismatches, insertions or deletions and do not require an a priori definition of the patterns one is seeking. Nor do they require any type of exact matching. This allows the data itself to suggest meaningful patterns that are conserved between binaries. These patterns can be used to identify zero-day malware and can help to automate the curation and characterization of large quantities of suspected malware.  I will talk about our MLSTONES capabilities as an innovative and effective way of detecting and characterizing most types of malware artifacts.  I'll also discuss how these capabilities can be used on other types of cyber security data.  About the speaker: Elena Peterson --Ms Peterson joined PNNL in 1990 after getting her BS in Computer and Information Sciences from the University of Oregon.  She is currently a Senior Cyber Security Researcher in the Computation and Analytics Division.  Ms. Peterson has led the research, development, and management of multiple cross-disciplinary, multi-laboratory projects focused in the fundamental sciences and national security sectors.  Her work has included research and development of integrated computational environments for bioinformatics, physics, computational chemistry, and cyber security.  She is currently the principal investigator for the MLSTONES and mMutant projects, which applies algorithms and tools from the biological sciences to create new and innovative solutions to relevant cyber security problems thus merging two of her main interests.</p>

Elena Peterson, Flexible and Adaptive Malware Identification Using Techniques from Biology