Using Evolutionary Learning of Behavior to Find Weaknesses in Operating Systems

System security is an ongoing struggle between system designers and the hacking community. Human creativity within this community pushes software into areas never anticipated by the designers, thus revealing weaknesses. Evolutionary algorithms offer designers a new way to examine the viability of their code. Because of the use of randomness as well as direction based on evaluation, these algorithms help to simulate some aspects of the human creative process. In this work we show that already rather simple evolutionary searches allow us to find weaknesses in an operating system, a Linux version, resulting in a crash of the system and the necessity to reboot - a serious system flaw and security risk.

[1]  Helder Coelho,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms by David E. Goldberg , 2005, J. Artif. Soc. Soc. Simul..

[2]  David E. Goldberg,et al.  The Design of Innovation: Lessons from and for Competent Genetic Algorithms , 2002 .

[3]  David Brumley,et al.  Remote timing attacks are practical , 2003, Comput. Networks.

[4]  Joachim Wegener,et al.  Evolutionary Testing of Embedded Systems , 2003 .

[5]  Rajiv Lal,et al.  United States Army , 2004 .

[6]  Jürgen Branke,et al.  Evolutionary Optimization in Dynamic Environments , 2001, Genetic Algorithms and Evolutionary Computation.

[7]  Samuel J. Leffler,et al.  The design and implementation of the 4.3 BSD Unix operating system , 1991, Addison-Wesley series in computer science.

[8]  Stephanie Forrest,et al.  Architecture for an Artificial Immune System , 2000, Evolutionary Computation.

[9]  Steven G. Johnson,et al.  The Design and Implementation of FFTW3 , 2005, Proceedings of the IEEE.

[10]  H. V. Jagadish,et al.  Information warfare and security , 1998, SGMD.

[11]  K.A. De Jong,et al.  Adaptive testing of controllers for autonomous vehicles , 1992, Proceedings of the 1992 Symposium on Autonomous Underwater Vehicle Technology.

[12]  Jörg Denzinger,et al.  Evolutionary online learning of cooperative behavior with situation-action pairs , 2000, Proceedings Fourth International Conference on MultiAgent Systems.