Detecting Stealthy Anomalies with Autoencoders Using Windowed Variance-Aware Loss Functions
Bibliographic record
Abstract
Autoencoders have had wide research appeal in cybersecurity and intrusion detection due to their cutting edge pattern recognition capabilities and their lack of reliance on labeled data. However, autoencoders often struggle to detect stealthy anomalies that do not result in a significantly elevated L1 reconstruction loss. In this paper, we introduce novel reconstruction loss functions—Windowed Standard Deviation Reconstruction Loss (WSR) and Windowed Inverse Standard Deviation Reconstruction Loss (WISR)—designed to improve intrusion detection accuracy by capturing micro-patterns of instability that traditional L1 loss fails to identify. We also present an autoencoder model integrated with these loss functions and evaluate its performance using the CIC-IDS2017 (Canadian Institute for Cybersecurity – Intrusion Detection System) dataset. Experimental results show that the proposed approach improves intrusion detection accuracy by up to 44% for different types of attacks compared to a baseline autoencoder based on standard L1 loss.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".