KOMPYUTER VIRUSLARINI KLASSIFIKATSIYA QILISHDA MATEMATIK KLASTERLASH ALGORITMLARINING ROLI
Keywords:
Kalit so‘zlar: Kompyuter viruslari, klasterlash algoritmlari, K-means, DBSCAN, Hierarchical clustering, mashinaviy o‘rganish, ma’lumotlarni tahlil qilish, virus klassifikatsiyasi, kiberxavfsizlik, zararli dasturlarni aniqlash.Abstract
Annotatsiya: Ushbu ishda kompyuter viruslarini aniqlash va ularni turlarga
ajratishda matematik klasterlash algoritmlarining roli o‘rganiladi. Ma’lumotlar asosida
viruslarning o‘xshash xususiyatlarini aniqlash uchun “K-means”, “DBSCAN” va
“Hierarchical clustering” algoritmlari qo‘llaniladi. Tadqiqotning maqsadi — yangi
aniqlanmagan viruslarni ularning xatti-harakati yoki kod tuzilishiga qarab avtomatik
ravishda guruhlash orqali “tezkor javob berish tizimini” yaratishdir. Natijada,
klasterlash usullari yordamida zararli dasturlarni erta aniqlash, antivirus dasturlarining
samaradorligini oshirish va kiberxavfsizlikni mustahkamlash imkoniyati yaratiladi.
References
Foydalangan adabiyotlar ro‘yhati
1. “Online Clustering of Known and Emerging Malware Families” — maqolada k-
means, DBSCAN va onlayn klasterlash algoritmlarining kombinatsiyasi, oqim
ma’lumotlarda yuqori tasiqlik (purity) natijasi haqida ma’lumotlar keltirilgan.
2. https://arxiv.org/pdf/2405.03298
3. “Classification and Online Clustering of Zero-Day Malware” — yangi virus
namunalari uchun real-vaqt klasterlash va klassifikatsiya modelini taklif qiladi.
4. https://link.springer.com/article/10.1007/s11416-024-00513-5?utm_source
5. “Malware Classification based on Call Graph Clustering” — viruslarni chaqiruv
graflari orqali klasterlash usuli, graflar orasidagi similarlik va DBSCAN/k-medoids
qo‘llanilishi.
6. https://arxiv.org/abs/1008.4365?utm_source
7. “Clustering Analysis for Malware Behavior Detection using Registry Data” —
Windows reyestri ma’lumotlaridan foydalanib K-means yordamida zararli va oddiy
jarayonlarni guruhlash bo‘yicha tadqiqot.
8. https://thesai.org/Downloads/Volume10No12/Paper_13-
Clustering_Analysis_for_Malware_Behavior_Detection.pdf?utm_source
9. “Automatic Malware Categorization Based on K-Means Clustering Technique” —
Android muhitida malware’larni K-means yordamida avtomatik kategoriyalash
usuli.
10. https://www.researchgate.net/publication/355217172_Automatic_Malware_Categ
orization_Based_on_K-Means_Clustering_Technique?utm_source
11. “Partitional Clustering of Malware Using K-Means” — malware xatti-harakat
ma’lumotlarini bo‘lish (partition) orqali K-means klasterlash modeli.
12. https://www.researchgate.net/publication/263129107_Partitional_Clustering_of_
Malware_Using_K-Means
13. “Clustering for malware classification” — klasterlash usullarining malware
klassifikatsiyasiga tatbiqi umumiy sharhi.
14. https://www.researchgate.net/publication/292185741_Clustering_for_malware_cl
assification
15. “Static Malware Family Clustering via Structural and Functional” — static tahlil
atributlari bo‘yicha struktura va funksiya ma’lumotlari asosida klasterlash.
16. https://scholar.smu.edu/cgi/viewcontent.cgi?article=1248&context=datasciencerev
iew&utm_source
17. “Machine Learning Aided Static Malware Analysis: A Survey and Tutorial” —
statik tahlil va mashinaviy o‘rganish usullari, shu jumladan klasterlash usullari
bo‘yicha ko‘p manbali sharh.
18. https://arxiv.org/abs/1808.01201?utm_source
19. “A Survey on Malware Detection with Graph Representation Learning” —
malware’ni graflar ko‘rinishida ifodalash va graf asosli usullar (shu jumladan
klasterlash / GNN) bo‘yicha tendensiyalar va misollar.
