Bibliographic record
Abstract
Distributed Denial of Service Attack (DDoS) adalah serangan dengan mengkompilasi beberapa sistem di internet dengan zombie/agen yang terinfeksi dan membentuk jaringan botnet. Serangan DDoS mengakibatkan kerugian finansial, hilangnya produktivitas, kerusakan merek, penurunan peringkat kredit dan asuransi serta terganggunya hubungan pelanggan, dan pemasok. Selain itu, teknologi IoT juga rentan terhadap serangan DDoS berskala besar. Untuk mencegah terjadinya serangan DDoS maka dibutuhkan model yang dapat mendeteksi adanya serangan DDoS. Pada penelitian ini, kami mengusulkan Deep Q-Network (DQN) untuk mendeteksi serangan DDoS. DQN merupakan algoritme reinforcement learning yang menggabungkan deep learning dan q-learning. Penerapan DQN digunakan untuk meningkatkan akurasi deteksi serangan pada dataset. Pada penelitian ini, dataset yang digunakan untuk mendeteksi adanya serangan DDoS atau tidak adalah CICDDoS2019 dataset yang disediakan oleh Canadian Institute for Cybersecurity. Berdasarkan perbandingan metode yang dilakukan didapatkan hasil metode DQN yang diusulkan dapat mendeteksi 11 serangan DDoS dan benign/normal data dengan nilai akurasi yang lebih baik dibandingan metode LR dan SVR. Hasil penelitian menunjukkan model yang diusulkan memiliki nilai akurasi 96% dan lebih baik dibandingkan metode LR dan SVR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".