SHAP TEXNOLOGIYASI ASOSIDA DDOS HUJUMLARINI ANIQLASH MODELINI OPTIMALLASHTIRISH

Авторы

  • Furqat Raxmatov Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti “Kompyuter tizimlari” kafedrasi dotsenti Автор
  • Muxammadi Toshtemirov Muhammad al-Xorazmiy nomidagi Toshkent axborot texnologiyalari universiteti 2-kurs magistranti Автор

DOI:

https://doi.org/10.65164/5tggpz92

Ключевые слова:

DDoS hujumlari, SHAP, feature selection, Random Forest, tarmoq xavfsizligi, model optimallashtirish, explainable AI.

Аннотация

Ushbu maqolada SHAP (SHapley Additive exPlanations) texnologiyasi
yordamida DDoS hujumlarini aniqlash modelini optimallashtirish usuli taqdim etiladi. Uchta xalqaro
benchmark dataset (CIC-DDoS2019, UNSW-NB15, Network Intrusion) ustida o'tkazilgan tajribalar
shuni ko'rsatdiki, SHAP asosida tanlangan 10 ta belgi bilan o'qitilgan model to'liq belgilar to'plamiga
nisbatan CIC-DDoS2019 da +0.82%, UNSW-NB15 da +2.04% yuqori aniqlik ko'rsatdi. Belgilar soni
esa 74–87% ga qisqardi. Natijalar SHAP texnologiyasining DDoS aniqlash tizimlarida samarali
feature selection usuli sifatida qo'llanilishi mumkinligini isbotlaydi.

Библиографические ссылки

[1]. Waqas M. va boshq., "A Systematic Review of DDoS Attack Detection Techniques". – IEEE

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[2]. Lundberg S.M., Lee S.I., "A Unified Approach to Interpreting Model Predictions". – NeurIPS,

2017, 4765–4774 b.

[3]. Sharafaldin I., Lashkari A.H., Ghorbani A.A., "Toward Generating a New Intrusion Detection

Dataset". – ICISSP, 2018, 108–116 b.

[4]. Moustafa N., Slay J., "UNSW-NB15: A Comprehensive Data Set for Network Intrusion

Detection Systems". – MilCIS, 2015 b.

[5]. Anand V. va boshq., "Network Intrusion Detection Using SHAP-based Explainable AI". –

IEEE ICCCNT, 2021, 1–6 b.

[6]. Farnaaz N., Jabbar M.A., "Random Forest Modeling for Network Intrusion Detection System".

– Procedia Computer Science, 2016, 89, 213–217 b.

Опубликован

2026-04-14