MEMCAIN: a memory-enhanced hybrid CNN-attention model for network anomaly detection
Bibliographic record
Abstract
With increasing cybersecurity threats, effective intrusion detection has become critical for safeguarding networks. Although deep learning methods have advanced, two major issues persist: (1) class imbalance biases models toward normal traffic, increasing false negatives; (2) single-task frameworks limit feature representation and fail to leverage multi-task collaboration potential. To address these, we propose Memory Autoencoder with CNN-Attention Integration Network(MEMCAIN), a multi-task feature fusion deep learning method. First, MEMCAIN integrates CNN with attention mechanisms, constructing CCA Blocks through contrastive normalization to capture spatiotemporal features. These blocks are stacked to form CCANet, enabling comprehensive spatiotemporal feature extraction from traffic data. Second, a memory autoencoder is introduced to capture latent distribution features of traffic flows. Finally, an end-to-end collaborative training framework jointly optimizes CCANet (main task) and the memory autoencoder (auxiliary task). Experiments demonstrate MEMCAIN's significant superiority over baselines across multiple datasets, with ablation studies validating each module's efficacy for fine-grained intrusion detection in complex network environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".