Simulation Driven Policy Recommendations for Code Diversity

Abstract

Periodic randomization of a computer program’s binary code is an attractive technique for defending against several classes of advanced threats. In this paper we describe a model of attacker-defender interaction in which the defender employs such a technique against an attacker who is actively constructing an exploit using Return Oriented Programming (ROP). In order to successfully build a working exploit, the attacker must guess the locations of several small chunks of program code, known as gadgets, in the defended program’s memory space. The defender thwarts the attacker’s efforts by periodically re-randomizing his code. Randomization incurs some performance cost, therefore an ideal strategy strikes an acceptable balance between utility degradation (cost) and security (benefit). We present risk aware and risk agnostic policy recommendations that were generated using simulation techniques. We found that policies that create low volatility environments are ideal for risk sensitive actors while policies that favor high system performance are more suitable for higher risk appetites.