HCL: A Hybrid CNN-LSTM Framework for Intrusion Detection in SDN-IoT Networks
Bibliographic record
Abstract
The seamless integration of Software-Defined Networking within Internet of Things (IoT) infrastructures has introduced novel paradigms for efficient network resource management. Nevertheless, this integration has also exposure to various cyber threats. Addressing these threats necessitates advanced detection mechanisms capable of adapting to the dynamic security needs. This paper introduces a Hybrid CNN LSTM (HCL) deep learning-based framework, which integrates hybrid Convolutional Neural Networks and Long Short-Term Memory networks to enhance intrusion detection in SDN-IoT networks. The performance analysis, conducted using real-world SDN datasets, attests to the framework’s efficiency, exhibiting a high detection accuracy and less inference time, ensuring reliable security measures without compromising network performance. The HCL framework demonstrates above 90% accuracy in differentiating between benign and malicious traffic, with a particular focus on detecting DoS, DDoS, port scanning, and fuzzing attacks. Additionally, the framework’s scalability aligns seamlessly with varying number of devices, maintaining strong defense across diverse network topologies. These results demonstrate the framework’s effectiveness in defending against modern cyber threats in SDN-IoT networks.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".