Distributed Detection of Large-Scale Internet of Things Botnets Based on Graph Partitioning
Bibliographic record
Abstract
With the rapid growth of IoT devices, the threat of botnets is becoming increasingly worrying. There are more and more intelligent detection solutions for botnets that have been proposed with the development of artificial intelligence. However, due to the current lack of computing power in IoT devices, these intelligent methods often cannot be well-applied to IoT devices. Based on the above situation, this paper proposes a distributed botnet detection method based on graph partitioning, efficiently detecting botnets using graph convolutional networks. In order to alleviate the wide range of IoT environments and the limited computing power of IoT devices, the algorithm named METIS is used to divide the network traffic structure graph into small graphs. To ensure robust information flow between nodes while preventing gradient explosion, diagonal enhancement is applied to refine the embedding representations at each layer, facilitating accurate botnet attack detection. Through comparative analysis with GATv2, GraphSAGE, and GCN across the C2, P2P, and Chord datasets, our method demonstrates superior performance in both accuracy and F1 score metrics. Moreover, an exploration into the effects of varying cluster numbers and depths revealed that six cluster levels yielded optimal results on the C2 dataset. This research significantly contributes to mitigating the IoT botnet threat, offering a scalable and effective solution for diverse IoT ecosystems.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".