TY - GEN
T1 - Evolving Assembly Code in an Adversarial Environment
AU - Maliukov, Irina
AU - Weiss, Gera
AU - Margalit, Oded
AU - Elyasaf, Achiya
N1 - Publisher Copyright:
© 2024 held by the owner/author(s).
PY - 2024/7/14
Y1 - 2024/7/14
N2 - We evolve survivors for the CodeGuru competition - - assembly programs that run 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. 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.
AB - We evolve survivors for the CodeGuru competition - - assembly programs that run 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. 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.
KW - assembly
KW - code generation
KW - codeguru xtreme
KW - genetic programming
UR - http://www.scopus.com/inward/record.url?scp=85201961313&partnerID=8YFLogxK
U2 - 10.1145/3638530.3654209
DO - 10.1145/3638530.3654209
M3 - Conference contribution
AN - SCOPUS:85201961313
T3 - GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
SP - 723
EP - 726
BT - GECCO 2024 Companion - Proceedings of the 2024 Genetic and Evolutionary Computation Conference Companion
PB - Association for Computing Machinery, Inc
T2 - 2024 Genetic and Evolutionary Computation Conference Companion, GECCO 2024 Companion
Y2 - 14 July 2024 through 18 July 2024
ER -