An Information Theoretic Analysis of Ghost Modulation

TL;DR

This work analyzes Ghost Modulation (GM), a cross-technology side channel that encodes a low-rate message by deliberately altering packet timing within an existing network. It develops a BCEC-based model capturing asymmetric delays and packet drops, derives capacity considerations, and proposes two simplified ML detectors along with a practical, joint signal acquisition and time synchronization scheme supported by simulations. It further evaluates the impact of short-block-length forward error correction codes on GM performance, illustrating the feasibility and trade-offs of GM as a covert, low-rate channel on existing networks. The results provide a theoretical and methodological foundation for GM design and scrutinize its operational viability in realistic network settings.

Abstract

Side channels have become an essential component of many modern information-theoretic schemes. The emerging field of cross technology communications (CTC) provides practical methods for creating intentional side channels between existing communications technologies. This paper describes a theoretical foundation for one such, recently proposed, CTC scheme: Ghost Modulation (GM). Designed to modulate a low-data-rate message atop an existing network stream, GM is particularly suited for transmitting identification or covert information. The implementation only requires firmware updates to existing hardware, making it a cost-effective solution. However, GM provides an interesting technical challenge due to a highly asymmetric binary crossover erasure channel (BCEC) that results from packet drops and network delays. In this work, we provide a mathematical description of the signal and channel models for GM. A heuristic decision rule based on maximum-likelihood principles for simplified channel models is proposed. We describe an algorithm for GM packet acquisition and timing synchronization, supported by simulation results. Several well known short block codes are applied, and bit error rate (BER) results are presented.

Figures (5)

• Figure 1: Ghost modulation symbol structure.
• Figure 2: Binary crossover erasure channel with fully-asymmetric transition probabilities
• Figure 3: Common errors in Ghost Modulation that demonstrate the asymmetry present in the discrete, memoryless channel model.
• Figure 4: Mean squared error of the proposed detection algorithm in number of time bins away from the true signal start location for varying levels of normalized mean delay.
• Figure 5: Coding results for GM and the BCEC channel. Here, Rm and Hamm refer to Reed-Muller and Hamming codes respectively. The first argument is the dimension, $k$, and the second is the block length, $n$.