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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Managerial perspectives : Utilizing MLP-CNN for predicting and classifying DDoS attacks

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pp. 2071–2079Vol. 45Issue 8November 2024DOI: 10.47974/JIOS-1651XML
Received:
13 Feb 2024
Published Online:
18 Dec 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1651
Pages:
2071–2079

Abstract

In today’s interconnected internet landscape, monitoring for misuse by malicious users is crucial. DDoS attacks are a highly prevalent malicious technique; it is a transaction that attempts to flood a server’s normal traffic, network, or services. The last few years have witnessed a decrease in network availability with higher detection rates and complexity. Recently, ML-based detection methods have emerged as one of the most prominent approaches. This paper introduces a Multilayer Perceptron and Convolutional Neural Network (MLP-CNN) for classifying and predicting DDoS attacks. By injecting various DDoS attacks into regular data, an extensive dataset is compiled. The proposed model combines CNN with MLP, and its performance is assessed using a confusion matrix. Experimental results demonstrate the model’s efficiency with 97% accuracy, 96% precision, and 96.5% recall. The detection time for 41 attributes is 2 seconds, and for 9 attributes, it is 0.2 seconds. 

Keywords

Subject Classifications

68T0168T07

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