ICES '96 Proceedings of the First International Conference on Evolvable Systems: From Biology to Hardware
Adaptive Reconfiguration Of Data Networks Using Genetic Algorithms
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Adaptive and Evolvable Software Systems: Techniques, Tools, and Applications
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences - Volume 09
Decision tree classifier for network intrusion detection with GA-based feature selection
Proceedings of the 43rd annual Southeast regional conference - Volume 2
Genetic algorithms for mentor-assisted evaluation function optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Multi-resistant radar jamming using genetic algorithms
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Computer viruses as artificial life
Artificial Life
Malware detection based on dependency graph using hybrid genetic algorithm
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Disguised malware script detection system using hybrid genetic algorithm
Proceedings of the 28th Annual ACM Symposium on Applied Computing
KameleonFuzz: evolutionary fuzzing for black-box XSS detection
Proceedings of the 4th ACM conference on Data and application security and privacy
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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.