A Hybrid Deep Learning Approach for Analyzing and Detecting the Malware in Software Defined Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rise of software-defined networking (SDN) has introduced new security challenges, particularly in detecting and mitigating malware threats within network infrastructures. Traditional malware detection techniques often struggle with the dynamic nature of modern cyber threats. This paper presents a hybrid deep learning-based approach for malware detection in SDN environments, leveraging Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Multi-Layer Perceptron (MLP). The proposed CNN-LSTM-MLP model integrates spatial, temporal, and fully connected feature extraction techniques to enhance classification accuracy. The study evaluates multiple LSTM architectures, including Bi-Directional-LSTM, Stacked-LSTM, and LSTM-MLP, demonstrating that the CNN-LSTM-MLP model achieves superior performance. The experimental results, conducted using datasets from the Canadian Institute for Cybersecurity, indicate that our model attains an accuracy of 98%, outperforming existing deep learning-based approaches. Additionally, the study integrates RYU and POX SDN controllers to simulate real-world network environments, ensuring practical applicability. The findings highlight the efficacy of hybrid deep learning models in securing SDN architectures against evolving malware threats.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it