Anti-Eavesdropping Multicast GAN Steganography for Botnet Stealth Communication
Bibliographic record
Abstract
Botnets, networks of compromised devices controlled by malicious actors, pose a growing threat to cybersecurity by enabling large-scale DDoS attacks, data theft, and critical infrastructure disruption. To evade detection, modern botnets increasingly rely on covert communication channels. This paper introduces EAMS-GAN (Encrypted, Authenticated, and Multi-casting Steganography GAN), a novel botnet architecture that combines steganography, deep learning, and error correction to achieve highly stealthy communication. EAMS-GAN offers advanced capabilities, including anti-eavesdropping, sender authentication, and secure multicasting, illustrating the potential sophistication of next-generation botnet operations. Experimental results show that the system achieves 99% payload transmission accuracy and 100% command communication accuracy without degrading image quality. This work is intended solely for cybersecurity research and awareness, aiming to help defenders anticipate and counter evolving threats, not to promote malicious use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".