AB-TCAD: An Access Behavior-Based Two-Stage Compromised Account Detection Framework
Bibliographic record
Abstract
In the fast-growing Internet, legitimate user accounts can be stolen by attackers, posing a serious threat to network security. Therefore, compromised account detection is an urgent problem. Most existing work is based on the behavior and content associated with posts. However, these approaches cannot detect anomalous behavior that attackers only collect information in the early stage, and often fail to consider temporal features. In this paper, we propose an access behavior-based two-stage compromised account detection framework, called AB-TCAD. In the first stage, we propose a novel feature called URL graph that depicts a user's website access pattern. To analyze abnormal changes in user access patterns, we design an AddEdge-GNN algorithm that detects similarities between URL graphs and obtains suspicious accounts. AddEdge-GNN predicts potential new user access behavior and reduces misjudgments that treat normal behavioral changes as anomalies. In the second stage, we propose an RVAE-based temporal detection. We construct temporal features of access behavior in multiple dimensions and use RVAE to detect anomalies, thereby identifying compromised accounts. We perform a real-world evaluation using data from a production network. The results show that AB-TCAD outperforms existing solutions in terms of both precision and recall metrics.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".