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Record W7015882386

Understanding Swizzor's Obfuscation Scheme - Pierre-Marc Bureau and Joan Calvet

2010· other· en· W7015882386 on OpenAlexaboutno aff

Bibliographic record

VenueBulletin of Miscellaneous Information (Royal Gardens Kew) · 2010
Typeother
Languageen
FieldEngineering
TopicManufacturing Process and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsObfuscationUnpackingMalwareProcess (computing)Malware analysisCryptovirologyReverse engineeringSet (abstract data type)UndoTimeline
DOInot available

Abstract

fetched live from OpenAlex

Swizzor is a malware family that was first seen on the Internet in 2002 and, since then, researchers have collected millions of different binary samples. The reason so many different files exist is that Swizzor uses strong server-side binary obfuscation to evade antivirus detection and slow down manual reverse engineering. In this talk, we will present a set of tools and techniques we have developed to understand and defeat Swizzor's binary protection. Upon execution, the custom packer goes through more than 40 million instructions before reaching any useful code. To deal with this, we created a tracing framework which builds a comprehensive timeline of the process execution, including memory modifications. We also created visualisation tools to quickly identify key elements of the unpacking process without having to read any assembly instruction. We have built an inference engine to automatically identify known patterns in memory such as decryption keys, useless values and control structures used by the packer. By taking into account the memory access and modification of the code, we were able to bypass its traditional syntactic obfuscation. We thus achieved a comprehensive understanding of the unpacking process and were able to reduce the need for manual analysis of new binaries. To the best of our knowledge, no one has deeply investigated the Swizzor malware family and its ties to shady advertisement companies. We will explain how Swizzor and its adware components are installed by affiliation programs to finance the development of well known applications. We will show the communication protocol used by Swizzor to fetch binary updates and how different packages are deployed depending on the affiliation program. Bio Pierre-Marc Bureau is senior researcher at antivirus company ESET. In his position, he is responsible of investigating trends in malware and finding effective techniques to counter these threats. Prior to joining ESET, Pierre-Marc Bureau worked for a network security company where he was senior security analyst. Pierre-Marc Bureau finished his Master degree in computer engineering at Ecole Polytechnique of Montreal in 2006. His studies focused mainly on the performance evaluation of malware. He has presented at various international conferences including Recon, Infosec, and Virus Bulletin. His main interests lie in reverse engineering, application and network security. Joan Calvet is a Ph.D. student at the High Security Lab in LORIA (Nancy, France) and the SecSI Lab at the Ecole Polytechnique of Montreal. His main interests lie in malware analysis, reverse engineering, and software security.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0870.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.187
Teacher spread0.174 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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