Intrusion Detection in IoT Network using Few-Shot Class Incremental Learning
Bibliographic record
Abstract
The Internet of Things (IoT) is one of the most rapidly evolving technologies, impacting various industrial sectors.With its immense potential, IoT comes with crucial security concerns, and we face high-volume and diverse attacks that must be addressed in short periods, emphasizing the importance of utilizing intrusion detection solutions in IoT networks.In the initial stage of an intrusion detection system, when there are sufficient samples available from the known attack classes, classic network intrusion detection methods can deliver good performance.However, the learned knowledge is no longer suitable for new types of attacks with just a few samples.On the other hand, due to the limited computing ability of edge devices in distributed IoT, only a small scale of data can be used for model training.Therefore, designing a lightweight learning scheme targeting small-scale training data is essential to train or update the model more effectively in resource-constrained devices.We propose a novel model based on Few-Shot Class Incremental Learning (FSCIL) for network intrusion detection in IoT networks.This model has been used in incremental image classification tasks, and to the best of our knowledge, this is the first time that this model has been used in network intrusion detection.We compare the proposed method with some state-of-the-art methods, and experimental analysis shows that our model outperforms others.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".