Protocol-Agnostic and Packet-Based Intrusion Detection Using a Multi-Layer Deep-Learning Architecture at the Network Edge
Bibliographic record
Abstract
Intrusion Detection (ID) faces multiple challenges, including the diversity of intrusion types and the risk of false positives and negatives. In an edge computing context, resource constraints further complicate the process, particularly during the training phase, which is computationally intensive. This paper presents a novel approach to ID in network traffic within edge computing environments using a Neural Network (NN) model. The proposed model is designed to align with the layered structure of network packets and has been trained and evaluated on the widely used CIC-IDS2017 cybersecurity dataset. Its protocol-agnostic design and customized preprocessing method enable it to efficiently detect network attacks across multiple protocols while preserving the original packet structure. Unlike existing approaches that transform packets into alternative representations such as images or NLP-based techniques, which introduce additional overhead, our method processes packets directly, eliminating the need for complex components like Recurrent Neural Networks (RNNs) or convolutional layers. Our model is optimized for edge computing by employing a centralized training approach that minimizes resource consumption while allowing flexible deployment on edge devices. Experimental results demonstrate that our approach outperforms existing methods in terms of accuracy, F1-score, recall, and precision when evaluated on a real-world dataset. This work highlights the potential of deep learning in enhancing network security while respecting edge computing constraints.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".