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Using algebraic models of programs for detecting metamorphic malwares

Journal of Mathematical Sciences 2011年第5期172卷 740-750页

作者： Podlovchenko, R.I. Kuzyurin, N.N. Shcherbina, V.S. Zakharov, V.A. Moscow State University Moscow Russian Federation Institute for System Programming Russian Academy of Sciences Moscow Russian Federation

Polymorphic and metamorphic viruses are the most sophisticated malicious programs that give a lot of trouble to virus scanners. Each time when these viruses infect new executables or replicate themselves, they completely modify (obfuscate) their signature to avoid being detected. This contrivance poses a serious threat to antivirus software that relies on classical virus-detection techniques: such viruses do not have any stable specific sequence of instructions that one looks for. In the ultimate case, the only characteristic that remains invariable for all generations of the same virus is their functionality (semantics). To all appearance, the only way to detect for sure a metamorphic malicious code is to look for a pattern that has the same semantics as (i.e., equivalent to) some representative sample of the virus. Thus, metamorphic virus detection is closely related to the equivalence-checking problem for programs. In this paper, we outline some new automata-theoretic framework for the designing of virus detectors. Our approach is based on the equivalence-checking techniques in algebraic models of sequential programs. An algebraic model of programs is an abstract model of computation, where programs are viewed as finite automata operating on Kripke structures. Models of this kind make it possible to focus on those properties of program instructions that are widely used in obfuscating transformations. We give a survey (including the latest results) on the complexity of equivalence-checking problem in various algebraic models of programs and estimate thus the resilience of some obfuscating transformation commonly employed by metamorphic viruses. © 2011 Springer Science+Business Media, Inc.

关键词： MALWARE (Computer software) COMPUTER software COMPUTER viruses MATHEMATICAL models ALGEBRA SCANNING systems

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Python Fuzzing for Trustworthy Machine Learning Frameworks

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Journal of Mathematical Sciences (United States) 2024年第2期285卷 180-188页

作者： Yegorov, I. Kobrin, E. Parygina, D. Vishnyakov, A. Fedotov, A. Ivannikov Institute for System Programming of the RAS Moscow Russian Federation Lomonosov Moscow State University Moscow Russian Federation

Ensuring the security and reliability of machine learning frameworks is crucial for building trustworthy AI-based systems. Fuzzing, a popular technique in the secure software development lifecycle (SSDLC), can be used to develop secure and robust software. Popular machine learning frameworks such as PyTorch and TensorFlow are complex and written in multiple programming languages including C/C++ and Python. We propose a dynamic analysis pipeline for Python projects using the Sydr-Fuzz toolset. Our pipeline includes fuzzing, corpus minimization, crash triaging, and coverage collection. Crash triaging and severity estimation are important steps to ensure that the most critical vulnerabilities are addressed promptly. Furthermore, the proposed pipeline is integrated in GitLab CI. To identify the most vulnerable parts of the machine learning frameworks, we analyze their potential attack surfaces and develop fuzz targets for PyTorch, TensorFlow, and related projects such as h5py. Applying our dynamic analysis pipeline to these targets, we were able to discover 3 new bugs and propose fixes for them. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

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ISPRAS@FinTOC-2022 Shared Task: Two-stage TOC Generation Model 4

ISPRAS@FinTOC-2022 Shared Task: Two-stage TOC Generation Mod...

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4th Financial Narrative Processing Workshop, FNP 2022

作者： Bogatenkova, Anastasiia Belyaeva, Oksana Perminov, Andrew Kozlov, Ilya Ivannikov Institute for System Programming of the RAS 25 Alexander Solzhenitsyn Str. Moscow109004 Russia

ISBN: (纸本)9791095546740

This work is connected with participation in FinTOC-2022 Shared Task: "Financial Document Structure Extraction". The competition contains two subtasks: title detection and TOC generation. We describe an approach for solving these tasks and propose the pipeline, consisting of extraction of document lines and existing TOC, feature matrix forming and classification. Classification model consists of two classifiers: the first binary classifier separates title lines from non-title, the second one determines the title level. In the title detection task, we got 0.900, 0.778 and 0.558 F1 measure, in the TOC generation task we got 63.1, 41.5 and 40.79 the harmonic mean of Inex F1 score and Inex level accuracy for English, French and Spanish documents respectively. With these results, our approach took first place among English and French submissions and second place among Spanish submissions. As a team, we took first place in the competition in English and French categories and second place in the competition in Spanish. © European Language Resources Association (ELRA)

关键词： Machine learning

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Viktor Ivannikov's four generations of operating systems 5

Viktor Ivannikov's four generations of operating systems

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5th International Scientific Conference: History of Computers and Informatics in the Former Soviet Union, Russian Federation and in the Countries of Mutual Economic Assistance Council, SoRuCom 2020

作者： Tomilin, Alexander N. Russian Academy of Sciences Ivannikov Institute for System Programming Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9781665403184

Since 1963, under Viktor Ivannikov's leadership and with his personal involvement, the institute of Precise Mechanics and Computer Engineering of the USSR Academy of Sciences (IPMCE), the Delta Research institute of the USSR Ministry of Electronic Industry and the institute for Cybernetics Problems of the Russian Academy of Sciences developed large software systems of domestic high-performance computers and computing complexes. Since the institute for system programming of the Russian Academy of Sciences was established (January 25, 1994), Viktor Ivannikov supervised the work of his team in a wide range of relevant areas of system programming. Here, we list his great creative contributions to the development of ideology and technology of creation and functioning of operating systems of computers and computing complexes. © 2020 IEEE.

关键词： Computer operating systems

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Axel Berg's Legacy in Cybernetics and Education. From the Council on Cybernetics to Axel Berg institute 5

Axel Berg's Legacy in Cybernetics and Education. From the Co...

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5th International Scientific Conference: History of Computers and Informatics in the Former Soviet Union, Russian Federation and in the Countries of Mutual Economic Assistance Council, SoRuCom 2020

作者： Semenov, Alexei Vardanyan, Valery Vishnyakov, Yury Gukasov, Ivan Rudchenko, Tatiana Uvarov, Alexander Axel Berg Institute Frc Csc of the Ras Moscow Russia Ivannikov Institute for System Programming of the Ras Moscow Russia

ISBN: (纸本)9781665403184

The history of the Scientific Council on Complex Problem 'Cybernetics' of the Academy of Sciences of the USSR created at the initiative of academician and navy admiral, Axel Ivanovich Berg, is covered. Axel Berg's brilliant predictions, relating to the cybernetics development and digital transformation in education are described. The current continuation of Berg's traditions is highlighted. © 2020 IEEE.

关键词： E-learning

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Finetuning BERT on Partially Annotated NER Corpora

Finetuning BERT on Partially Annotated NER Corpora

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2022 Ivannikov Open Conference, ISPRAS 2022

作者： Scherbakov, Viktor Mayorov, Vladimir Ivannikov Institute for System Programming of the Russian Academy of Sciences Russia Lomonosov Moscow State University Russia

ISBN: (纸本)9798350398533

Most Named Entity Recognition (NER) models operate under the assumption that training datasets are fully labelled. While it is valid for established datasets like CoNLL 2003 and OntoNotes, sometimes it is not feasible to obtain the complete dataset annotation. These situations may occur, for instance, after selective annotation of entities for cost reduction. This work presents an approach to finetuning BERT on such partially labelled datasets using self-supervision and label preprocessing. Our approach outperforms the previous LSTM-based label preprocessing baseline, significantly improving the performance on poorly labelled datasets. We demonstrate that following our approach while finetuning RoBERTa on CoNLL 2003 dataset with only 10% of total entities labelled is enough to reach the performance of the baseline trained on the same dataset with 50% of the entities labelled. © 2022 IEEE.

关键词： Cost reduction

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Docreader labeling system for line type classifier 4

Docreader labeling system for line type classifier

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4th Scientific-Practical Workshop Information Technologies: Algorithms, Models, systems, ITAMS 2021

作者： Kozlov, Ilya S. Ivannikov Institute for System Programming of the RAS Alexander Solzhenitsyn st. 25 Moscow109004 Russia

We develop the document analysis system, which is able to extract text and text metadata (such as font size and style), and restore the document structure. Some parts of the pipeline are based on machine learning thus requiring training and the labeled dataset, creating a training dataset is based on manual labeling. In this article, we describe an approach to the creation of a labeling system in the task of multiclass classification of document lines (paragraphs). The pipeline consists of several stages ranged from getting the source documents to getting a ready-to-learn dataset. An approach to the analysis of scanned documents and documents in docx and txt format is considered. In our work, we focus on intra-team labeling, thus we do not consider some problems, common for the crowdsourcing approach (such as unscrupulous annotators). © 2021 Copyright for this paper by its authors.

关键词： Pipelines

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Static Analysis for Go: Build Interception

Static Analysis for Go: Build Interception

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2023 Ivannikov ISPRAS Open Conference, ISPRAS 2023

作者： Dvortsova, Varvara Izbyshev, Alexey Borodin, Alexey Belevantsev, Andrey Ivannikov Institute for System Programming of the Ras Moscow Russia Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9798350349993

This paper presents a method for build interception of Go projects and utilizing the intercepted information for building an intermediate representation used in a static analyzer. The paper discusses two approaches: handling invocations of the higher-level go build command and handling invocations of the lower-level compile tool. The second solution is applicable not only to the built-in Go build system but also to other build systems that directly utilize the low-level compile tool, such as Bazel, Please, Pants. It also covers compilations involving CGO. © 2023 IEEE.

关键词： Static analysis

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LibAFL-DiFuzz: Advanced Architecture Enabling Directed Fuzzing

LibAFL-DiFuzz: Advanced Architecture Enabling Directed Fuzzi...

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2024 Ivannikov Open Conference, ISPRAS 2024

作者： Parygina, Darya Mezhuev, Timofey Kuts, Daniil Ivannikov Institute for System Programming of the RAS Moscow Russia Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9798331526023

Directed fuzzing performs best for targeted program testing via estimating the impact of each input in reaching predefined program points. But due to insufficient analysis of the program structure and lack of flexibility and configurability it can lose efficiency. In this paper, we enhance directed fuzzing with context weights for graph nodes and resolve indirect edges during call graph construction. We construct flexible tool for directed fuzzing with components able to be easily combined with other techniques. We implement proposed method in three separate modules: DiFuzzLLVM library for graph construction and indirect calls resolving, DiFuzz static analysis tool for processing program graphs and computing proximity metrics, and LibAFL-DiFuzz directed fuzzer based on LibAFL fuzzing library. We create additional LibAFL modules for enabling custom power scheduling and static instrumentation. We evaluate indirect calls resolving and get increase in directed fuzzing efficiency for reaching deeper target points. We evaluate context weights contribution and get benefits in TTE and scheduling iterations number. We evaluate our fuzzer in comparison with AFLGo and BEACON, and reveal speedup in time to exposure on several benchmarks. Furthermore, our tool implements some important usability features that are not available in mentioned tools: target points detection, multiple target points support, etc. © 2024 IEEE.

关键词： Directed graphs

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User Defined Checkers for Static Analysis with Symbolic Execution

User Defined Checkers for Static Analysis with Symbolic Exec...

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2024 Ivannikov Memorial Workshop, IVMEM 2024

作者： Galustov, Artemiy Borodin, Alexey Ivannikov Institute for System Programming of the RAS Moscow Russia Lomonosov Moscow State University Moscow Russia

ISBN: (纸本)9798331504663

Many modern static analysis tools provide toolkits that allow creation of custom checkers by end users. The majority of existing solutions choose AST-based approaches to provide a simpler declarative API. Solutions based on dataflow analysis or symbolic execution use complex explicit APIs that require much more expertise to achieve decent results compared to AST-based checkers. Even though the latter approach is much more powerful simplicity of the former made it much more *** this paper we introduce SvaceAPI a tool to write checkers based on symbolic execution in a declarative way. We describe a simple pattern-based API that leverages symbolic execution and attributes provided by Svace. Testing the API on a couple of checkers and open source programs across the variety of languages showed the same quality results as for built-in Svace checkers without noticeable performance regressions. © 2024 IEEE.

关键词： Open source software

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