Towards Proactive Cybersecurity in Smart Grids: Behavioral Advanced Persistent Threat Detection via Adversarial and Autoencoder Architectures
Bibliographic record
Abstract
Smart grids are confronted with growing cybersecurity threats by Advanced Persistent Threats (APTs) targeting vulnerabilities of cyber-physical systems with stealthy, multi-stage attacks. Conventional signature-based rule-driven detection mechanisms cannot detect these advanced threats. This paper presents an active behavior detection system using Generative Adversarial Networks and Autoencoders for benign network behavior modeling and anomaly detection characteristic of APTs. Tested on actual smart grid data, our hybrid solution is 96.5 % accurate and has an$\text{F 1}$-score of$\text{96.59 \%}$, surpassing baseline MLPs and state-of-the-art techniques. The main innovations are adversarial training for generating attack patterns and Autoencoder reconstruction for anomaly detection. Experiments show the framework's robustness to stealthy APTs with few false positives. This research propels adaptive defense technologies for critical infrastructure, bridging the gaps in scalability and dynamic threat modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".