CLASSIFICATION OF MALWARE FAMILIES USING NAÏVE BAYES CLASSIFIER
Bibliographic record
Abstract
Dikarenakan peningkatan pengguna smartphone Android berbanding lurus dengan peningkatan pengembangan malware yang semakin pesat. Tidak jarang penelitian tentang malware setiap tahunnya yang membahas tentang malware families dengan berbagai macam pendekatan yang salah satunya machine learning. Dengan mendapatkan data malware yang kredibel, dapat memudahkan peneliti dalam menganalisa malware. Terdapat kumpulan data malware yang dibuat the Canadian Institute for Cybersecurity(CIC) yang dapat diakses secara publik. Data ini disebut CICInvestAndMal2019 yang berisi data malware. Dataset ini dibuat dengan melakukan analisa statis dan dinamis pada smartphone secara real time. Hasil dari analisa tersebut kemudian diproses dengan metode Random Forest yang menghasilkan precision 61.2% dan recall 57.7%. Berdasarkan penelitian tersebut, maka penulis akan mengklasifikasikan dataset CICInvestAndMal2019 menggunakan metode Naïve Bayes, dan hasil yang didapat dari klasifikasi Naïve Bayes adalah nilai recall dan precision sebesar 68% dan 66%.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".