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 I feel immense pleasure to extend my sincere gratitude to my supervisor, Professor 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 am expanding my sincere to our NGN Research Group Postdoctoral: Dr. Danish Sattar for his support, advice, and help throughout my study.I'm very thankful to my beloved wife and soulmate Fatma Arafah for her unconditional love, support, patience, and encouragement during tough times, for offering me the push and positive energy to continue when I am wretched, and for making me believe in myself.A big thanks to my kids, Layla and Ramsey, for their endless love and smile that boosts my power every time I play with them.My special thanks to my parents: Mohamed Maarouf
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".