Evaluating Adversarial Learning Resilience on Various Encrypted Traffic Classifications
Bibliographic record
Abstract
Machine learning and deep learning algorithms are applied to reduce false alarm rates, improve accuracy in classifying encrypted network applications, and improve resilience against multiple types of network attacks. Due to the fast growth of learning techniques, various types of adversarial attacks are increased to reveal ML and DL vulnerabilities and add more challenges in classifying encrypted traffic. In this research, we focus on investigating and evaluating the effectiveness of different adversarial attacks and see how resilient ML and DL algorithms are in classifying encrypted traffic applications, i.e., C4.5 Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). We train our models in the adversarial-free environment on ISCX Ashraf Matrawy, the lead of the Next Generation Networks (NGN) research group at Carleton University for providing me with valuable support, guidance and encouragement through this research. It was a great privileged and honor to work and study under his guidance that assisted me in extending my knowledge and professional experience while directing me on the right path to pursue my goal. I
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".