ABSTRACT

We consider the application of a genetic algorithm (GA) to the problem of hiding information in program executables. In a nutshell, our approach is to re-order instructions in a program in such way that aims to maximize the amount of data that can be embedded while, at the same time, ensuring the functionality of the executable is not altered. In this work, we focus on the problem of identifying a large set of instructions which may be reordered, and some initial results on the IA-64 architecture are then presented that illustrate the potential benefit of such an approach.

BIO

Ryan Gabrys is a scientist at SPAWAR Systems Center Pacific. He received the B.S. degree in mathematics and computer science from the University of Illinois at Champaign-Urbana in 2005. In 2014, he received a Ph.D. in electrical engineering at the University of California at Los Angeles. His research interests broadly lie in the areas of theoretical computer science and electrical engineering, including bioinformatics, combinatorics, coding theory, and signal processing.