Intent-Driven Data Falsification Attack on Collaborative IoT-Edge Environments

Collaborative IoT-edge environments, although effective in hosting latency-sensitive applications, are fundamentally vulnerable to data falsification attacks that can potentially impact key system performance objectives. In this paper, we explore and propose an intent-driven energy data falsification attack model for collaborative IoT-edge environments and shed light on the attack's impact on system performance. Our primary contribution lies in developing key intuitions and systemization of threat landscape for attacks with selfish and malicious intents that target one or many key system performance objectives, viz., overall system energy-efficiency and end-to-end latency of hosted applications. The proposed attack model is evaluated, optimized, and validated through ‘testbed-in-the-loop’ simulations. The results demonstrate that depending on selfish and malicious intents, the proposed attack model can achieve upto 50% increase in energy savings for the compromised IoT devices, accelerate battery drainage of non-compromised devices, and ensure upto 61% success in violating application latency requirements.