Evolving Assembly Code in an Adversarial Environment
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916280994365440 |
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| author | Maliukov, Irina Weiss, Gera Margalit, Oded Elyasaf, Achiya |
| author_facet | Maliukov, Irina Weiss, Gera Margalit, Oded Elyasaf, Achiya |
| contents | In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor -- an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the Assembly language and synthesize the code from scratch using Genetic Programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and utilized them. To push evolution further, we implemented memetic operators that utilize machine learning to explore the solution space effectively. This work has important applications for cyber-security as we utilize evolution to detect weaknesses in survivors. The Assembly BNF is domain-independent; thus, by modifying the fitness function, it can detect code weaknesses and help fix them. Finally, the CodeGuru competition offers a novel platform for analyzing GP and code evolution in adversarial environments. To support further research in this direction, we provide a thorough qualitative analysis of the evolved survivors and the weaknesses found. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_19489 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evolving Assembly Code in an Adversarial Environment Maliukov, Irina Weiss, Gera Margalit, Oded Elyasaf, Achiya Neural and Evolutionary Computing In this work, we evolve Assembly code for the CodeGuru competition. The goal is to create a survivor -- an Assembly program that runs the longest in shared memory, by resisting attacks from adversary survivors and finding their weaknesses. For evolving top-notch solvers, we specify a Backus Normal Form (BNF) for the Assembly language and synthesize the code from scratch using Genetic Programming (GP). We evaluate the survivors by running CodeGuru games against human-written winning survivors. Our evolved programs found weaknesses in the programs they were trained against and utilized them. To push evolution further, we implemented memetic operators that utilize machine learning to explore the solution space effectively. This work has important applications for cyber-security as we utilize evolution to detect weaknesses in survivors. The Assembly BNF is domain-independent; thus, by modifying the fitness function, it can detect code weaknesses and help fix them. Finally, the CodeGuru competition offers a novel platform for analyzing GP and code evolution in adversarial environments. To support further research in this direction, we provide a thorough qualitative analysis of the evolved survivors and the weaknesses found. |
| title | Evolving Assembly Code in an Adversarial Environment |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2403.19489 |