DDoS ATTACK CLASSIFICATION WITH MACHINE LEARNING

Registration ID: IJNRD_216154

Published ID: IJNRD2403453

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Manideep Belide, Prof. Shivangi Gandhi

DDoS attacks, Machine learning, Classification, Network Traffic, Feature engineering.

Distributed Denial of Service (DDoS) attacks pose a significant threat to the availability and reliability of networked systems. Detecting and mitigating these attacks in real-time is crucial for ensuring the uninterrupted operation of critical services. In this paper, we propose a machine learning-based approach for the classification of DDoS attacks using network traffic features. Our study leverages a dataset of network traffic data collected from diverse sources, encompassing various types of DDoS attacks and normal network behavior. We employ feature engineering techniques to extract relevant features and train several machine learning models, including Random Forest, Support Vector Machine (SVM), and Gradient Boosting, to classify network traffic instances as either DDoS attacks or normal traffic. Performance evaluation using cross-validation and independent test datasets demonstrates the effectiveness of our approach in accurately identifying DDoS attacks with high precision and recall. Furthermore, we analyze the interpretability of the trained models and discuss the insights gained from the feature importance analysis. Our findings underscore the potential of machine learning techniques in enhancing DDoS attack detection capabilities and contribute to the ongoing efforts in cybersecurity research. Traditional methods for detecting DDoS attacks often rely on rule-based or signature-based approaches, which may struggle to adapt to evolving attack strategies and variations in network traffic patterns. In recent years, machine learning techniques have emerged as promising alternatives for DDoS attack detection, leveraging the power of data-driven algorithms to identify anomalous behavior and classify network traffic instances as either malicious or benign. By analyzing various features extracted from network traffic data, machine learning models can learn to distinguish between normal traffic and DDoS attack traffic, enabling proactive defense measures and timely response actions. In this paper, we present a comprehensive study on DDoS attack classification using machine learning methods. Our study aims to investigate the efficacy of different machine learning algorithms in accurately detecting and classifying DDoS attacks based on network traffic characteristics. We leverage a diverse dataset of network traffic samples, encompassing various types of DDoS attacks and legitimate traffic patterns, to train and evaluate multiple machine learning models. Furthermore, we explore the interpretability of the trained models and analyze the importance of different features in distinguishing between DDoS attacks and normal traffic. The remainder of this paper is organized as follows: Section 2 provides a review of existing literature and research related to DDoS attack detection and machine learning techniques. Section 3 describes the methodology employed in our study, including data preprocessing, feature engineering, and model training. Section 4 presents the experimental results and performance evaluation of the machine learning models. Section 5 discusses the findings and implications of our study, including insights into model interpretability and feature importance analysis. Finally, Section 6 concludes the paper with a summary of key findings and suggestions for future research directions.

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Manideep Belide & Prof. Shivangi Gandhi (March-2024). DDoS ATTACK CLASSIFICATION WITH MACHINE LEARNING. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(3), e420-e424. https://ijnrd.org/papers/IJNRD2403453.pdf

Volume 9 Issue 3, March-2024

Pages : e420-e424

Paper Reg. ID: IJNRD_216154

Published Paper Id: IJNRD2403453

Research Area: Computer Science & Technology 

Author Type: Indian Author

Country: Jangaon, Telangana, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2403453.pdf

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