ntvНаучно-технический вестник информационных технологий, механики и оптикиScientific and Technical Journal of Information Technologies, Mechanics and Optics2226-14942500-0373Университет ИТМО10.17586/2226-1494-2025-25-1-114-127ntv-427Research ArticleКОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИCOMPUTER SCIENCEУлучшение и расширение CatBoost для точного обнаружения и классификации подтипов DoS и DDoS атак в сетевом трафикеEnhancing and extending CatBoost for accurate detection and classification of DoS and DDoS attack subtypes in network traffichttps://orcid.org/0000-0002-8256-6790ХажжузА.HajjouzA.

Хажжуз Абдулкадер — аспирант

Санкт-Петербург, 197101

Abdulkader Hajjouz — PhD Student

Saint Petersburg, 197101

hajjouz@itmo.ruhttps://orcid.org/0000-0001-5000-4868АвксентьеваЕ. Ю.AvksentievaE. Yu.

Авксентьева Елена Юрьевна — кандидат педагогических наук, доцент, доцент

Санкт-Петербург, 197101

Elena Yu. Avksentieva — PhD (Education Science), Associate Professor, Associate Professor

Saint Petersburg, 197101

eavksenteva@itmo.ruУниверситет ИТМОРоссияITMO UniversityRussian Federation202527022025251114127Copyright © Хажжуз А., Авксентьева Е.Ю., 20252025Хажжуз А., Авксентьева Е.Ю.Hajjouz A., Avksentieva E.Y.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ntv.elpub.ru/jour/article/view/427

В постоянно меняющемся цифровом мире рост сложных киберугроз, особенно атак DoS (отказ в обслуживании) и DDoS (распределенный отказ в обслуживании), представляет собой серьезную проблему для информационной безопасности. В работе рассматривается задача классификации вредоносного и безопасного сетевого трафика с использованием применением классификатора CatBoost — алгоритма машинного обучения, оптимизированного для категориальных данных и несбалансированных наборов данных. Использованы наборы данных CIC[1]IDS2017 и CSE-CIC-IDS2018, которые имитируют различные сценарии кибератак. оптимизация классификатора CatBoost для распознавания конкретных подтипов атак DoS и DDoS, включая Hulk, SlowHTTPTest, GoldenEye, Slowloris, HOIC, LOIC-UDP-HTTP, LOIT. Разработана методика работы CatBoost для подготовки данных, отбора признаков и настройки модели, нормализации выбросов, корректировки отрицательных значений и улучшения структуры наборов данных. Стратифицированная выборка обеспечила сбалансированное представление классов в обучающих, валидационных и тестовых наборах. Разработанная модель CatBoost продемонстрировала отличные результаты с общей точностью 0,999922, высокой полнотой и значениями F1-меры по всем категориям и способностью обрабатывать более 3,4 млн образцов в секунду. Эти результаты показывают, что модель является надежной и подходит для обнаружения вторжений в реальном времени. Классификация конкретных типов атак улучшает точность системы обнаружения вторжений (Intrusion Detection Systems, IDS) и позволяет целенаправленно реагировать на различные угрозы. Существенное повышение точности обнаружения решает проблему несбалансированных наборов данных и необходимость детектирования различных типов атак. CatBoost рекомендуется к использованию в передовых рамках информационной безопасности для критической инфраструктуры, облачных сервисов и корпоративных сетей для защиты от цифровых угроз. Данная работа предлагает быстрое, точное и масштабируемое решение для сетевой IDS и подчеркивает важность использования кастомизированных моделей машинного обучения в информационной безопасности. В дальнейшем предполагается изучить применение CatBoost на большем количестве наборов данных и его интеграцию с другими методами машинного обучения для повышения устойчивости и точности обнаружения.

In the ever changing digital world, the rise of sophisticated cyber threats, especially DoS and DDoS attacks, is a big challenge to Information Security. This paper addresses the problem of classifying malicious from benign network traffic using CatBoost classifier, a machine learning algorithm optimized for categorical data and imbalanced datasets. We used CIC-IDS2017 and CSE-CIC-IDS2018 datasets which simulate various cyberattack scenarios, our research optimized CatBoost to identify specific subtypes of DoS and DDoS attacks including Hulk, SlowHTTPTest, GoldenEye, Slowloris, HOIC, LOIC-UDP-HTTP, LOIT. The methodology involved data preparation, feature selection and model configuration, normalizing outliers, correcting negative values, and refining dataset structures. Stratified sampling ensured a balanced representation of classes in training, validation, and testing sets. The CatBoost model performed well with overall accuracy of 0.999922, high precision, recall, and F1-scores across all categories, and it can process over 3.4 million samples per second. These results show the model is robust and reliable for real-time intrusion detection. By classifying specific attack types, our model improves the precision of the Intrusion Detection Systems (IDS) and allows for targeted response to different threats. The big gain in detection accuracy solves the problem of imbalanced datasets and the need for granular attack types detection. Use CatBoost in advanced Information Security frameworks for critical infrastructure, cloud services, and enterprise networks to defend against digital threats. This paper provides a fast, accurate and scalable solution for network IDS and shows the importance of custom machine learning models in Information Security. Future work should explore CatBoost on more datasets and integrate it with other machine learning techniques to improve robustness and detection.

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The authors declare that there are no conflicts of interest present.