ANALISIS KOMPARATIF UKURAN DAN INTENSITAS SERANGAN DDoS_ STUDI KASUS PADA UDPLag, LDAP, DAN PORTMAP MENGGUNAKAN CIC-DDoS2019
Bibliographic record
Abstract
Serangan Distributed Denial of Service (DDoS) telah menjadi ancaman yang terus berlanjut terhadap infrastruktur digital, yang membutuhkan strategi deteksi dan mitigasi yang efisien. Studi ini menyajikan analisis komparatif dari tiga jenis serangan DDoS, yaitu UDPLag, LDAP, dan Portmap, menggunakan dataset CIC-DDoS2019 dari Canadian Institute for Cybersecurity. Tujuan penelitian ini adalah untuk memeriksa pola dan dampak dari setiap serangan berdasarkan distribusi ukuran paket, karakteristik temporal, dan tingkat risiko industri berdasarkan klasifikasi CVE. Metodologi yang digunakan meliputi statistik deskriptif, visualisasi (histogram, boxplot, heatmap, pairplot), serta teknik reduksi dimensi t-SNE. Hasil menunjukkan bahwa serangan LDAP memiliki ukuran dan volume paket terbesar, sementara Portmap memiliki tingkat risiko teknis tertinggi berdasarkan pemetaan CVE. UDPLag menunjukkan intensitas sedang dengan frekuensi tinggi. Penelitian ini menyimpulkan bahwa strategi mitigasi DDoS harus disesuaikan dengan karakteristik teknis dan risiko spesifik dari tiap jenis serangan. Pendekatan analitik visual yang dikombinasikan dengan referensi CVE memberikan wawasan penting dalam menetapkan prioritas mitigasi secara kontekstual.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".