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Evolvable malware

Published: 08 July 2009 Publication History

Abstract

The concept of artificial evolution has been applied to numerous real world applications in different domains. In this paper, we use this concept in the domain of virology to evolve computer viruses. We call this domain as "Evolvable Malware". To this end, we propose an evolutionary framework that consists of three modules: (1) a code analyzer that generates a high-level genotype representation of a virus from its machine code, (2) a genetic algorithm that uses the standard selection, cross-over and mutation operators to evolve viruses, and (3) the code generator converts the genotype of a newly evolved virus to its machinelevel code. In this paper, we validate the notion of evolution in viruses on a well-known virus family, called Bagle. The results of our proof-of-concept study show that we have successfully evolved new viruses-previously unknown and known-variants of Bagle-starting from a random population of individuals. To the best of our knowledge, this is the first empirical work on evolution of computer viruses. In future, we want to improve this proof-of-concept framework into a full-blown virus evolution engine.

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cover image ACM Conferences
GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation
July 2009
2036 pages
ISBN:9781605583259
DOI:10.1145/1569901
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 08 July 2009

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Author Tags

  1. artificial evolution
  2. computer virus
  3. genetic algorithm

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GECCO09
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GECCO09: Genetic and Evolutionary Computation Conference
July 8 - 12, 2009
Québec, Montreal, Canada

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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  • (2023)YAMME: a YAra-byte-signatures Metamorphic Mutation EngineIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.329405918(4530-4545)Online publication date: 2023
  • (2023)A Survey of strategy-driven evasion methods for PE malware: Transformation, concealment, and attackComputers & Security10.1016/j.cose.2023.103595(103595)Online publication date: Nov-2023
  • (2023)Game-theoretic approach to epidemic modeling of countermeasures against future malware evolutionComputer Communications10.1016/j.comcom.2023.05.001206(160-171)Online publication date: Jun-2023
  • (2022)Adapting novelty towards generating antigens for antivirus systemsProceedings of the Genetic and Evolutionary Computation Conference10.1145/3512290.3528693(1254-1262)Online publication date: 8-Jul-2022
  • (2021)Malware Generation with Specific Behaviors to Improve Machine Learning-based Detection2021 IEEE International Conference on Big Data (Big Data)10.1109/BigData52589.2021.9671886(2160-2169)Online publication date: 15-Dec-2021
  • (2021)Evolutionary Computation in Social Propagation over Complex Networks: A SurveyInternational Journal of Automation and Computing10.1007/s11633-021-1302-318:4(503-520)Online publication date: 1-Aug-2021
  • (2020)Finding Effective Security Strategies through Reinforcement Learning and Self-Play2020 16th International Conference on Network and Service Management (CNSM)10.23919/CNSM50824.2020.9269092(1-9)Online publication date: 2-Nov-2020
  • (2020)A Conceptual Direction on Automatically Evolving Computer Malware using Genetic and Evolutionary Algorithms2020 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT48043.2020.9112509(226-229)Online publication date: Feb-2020
  • (2020)Neural Swarm VirusSwarm, Evolutionary, and Memetic Computing and Fuzzy and Neural Computing10.1007/978-3-030-37838-7_12(122-134)Online publication date: 3-Jan-2020
  • (2019)AIMED: Evolving Malware with Genetic Programming to Evade Detection2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)10.1109/TrustCom/BigDataSE.2019.00040(240-247)Online publication date: Aug-2019
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