Towards understanding the applicability of runtime moving target defense for the internet of things and cyber physical systems

Moving Target Defense (MTD) enhances traditional security by dynamically altering system attributes to prevent attacks. While widely studied, its application in resource-constrained Internet of Things (IoT) and Cyber Physical Systems (CPS) devices remains limited. This paper examines the effectiveness of Address Space Layout Randomization (ASLR), an MTD technique that randomizes memory layouts to prevent memory corruption on 32-bit Raspberry Pi OS and OpenWRT both on ARMv7 compared to Kali Linux on x86_64. Using address distribution analysis, byte-level variation, and Chao-Shen entropy estimation, the study assesses ASLR randomness and validates results through a Return-Oriented Programming (ROP) attack. Findings show that ARM-based ASLR offers lower entropy than x86_64 but achieves comparable protection to 32-bit x86 systems, demonstrating its practicality for IoT and CPS platforms. The results provide insights on achieving security amid resource constraints by integrating ASLR with other built-in security mechanisms and complementary MTD techniques operating at different layers.