AESMOTE: Adversarial Reinforcement Learning With SMOTE for Anomaly Detection
Why this work is in the frame
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Bibliographic record
Abstract
Intrusion Detection Systems (IDSs) play a vital role in securing today's Data-Centric Networks. In a dynamic environment such as the Internet of Things (IoT), which is vulnerable to various types of attacks, fast and robust solutions are in demand to handle fast-changing threats and thus the ever-increasing difficulty of detection. In this paper, we present a novel framework for the detection of anomalies, which, in particular, supports intrusion detection. The anomaly-detection framework we propose combines reinforcement learning with class-imbalance techniques. Our goal is not only to exploit the auto-learning ability of the reinforcement-learning loop but also to address the dataset imbalance problem, which is pervasive in existing learning-based solutions. We introduce an adapted SMOTE to address the class-imbalance problem while remodelling the behaviors of the environment agent for better performance. Experiments are conducted on NSL-KDD datasets. Comparative evaluations and their results are presented and analyzed. Using techniques such as SMOTE, ROS, NearMiss1 and NearMiss2, performance measures obtained from our simulations have led us to recognize specific performance trends. In particular, the proposed model AESMOTE outperforms AE-RL in several cases. Experiment results show an Accuracy greater than 0.82 and a F1 greater than 0.824.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it