Active Adversaries from an Information-Theoretic Perspective: Data Modification Attacks
This work addresses security in communication networks against data tampering, providing theoretical bounds for adversarial scenarios, but it is incremental as it builds on existing information-theoretic frameworks.
The paper tackles the problem of reliable communication with active adversaries that can modify transmitted data over multiple paths, proposing two adversary models (memoryless and foreseer) and deriving capacity for memoryless adversaries and an achievable rate with an upper bound for foreseer adversaries.
We investigate the problem of reliable communication in the presence of active adversaries that can tamper with the transmitted data. We consider a legitimate transmitter-receiver pair connected over multiple communication paths (routes). We propose two new models of adversary, a "memoryless" and a "foreseer" adversary. For both models, the adversaries are placing themselves arbitrarily on the routes, keeping their placement fixed throughout the transmission block. This placement may or may not be known to the transmitter. The adversaries can choose their best modification strategy to increase the error at the legitimate receiver, subject to a maximum distortion constraint. We investigate the communication rates that can be achieved in the presence of the two types of adversaries and the channel (benign) stochastic behavior. For memoryless adversaries, the capacity is derived. Our method is to use the typical set of the anticipated received signal for all possible adversarial strategies (including their best one) in a compound channel that also captures adversarial placement. For the foreseer adversaries, which have enhanced observation capabilities compared to the memoryless ones, we propose a new coding scheme to guarantee resilience, i.e., recovery of the codeword independently of the adversarial (best) choice. We derive an achievable rate and we propose an upper bound on the capacity. We evaluate our general results for specific cases (e.g., binary symbol replacement or erasing attacks), to gain insights.