INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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.
"DDoS ATTACK CLASSIFICATION WITH MACHINE LEARNING", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.e420-e424, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403453.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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