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Journal of Information and Optimization Sciences cover
Open Access ·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

Enhanced DDoS attack detection using instance-based transfer learning with Q TRANSFER and Gaussian membership function

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pp. 1811–1819Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2014XML
Received:
03 Dec 2024
Published Online:
30 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-2014
Pages:
1811–1819

Abstract

DDoS attacks pose a significant threat to networks due to their ease of execution and the difficulty in mitigating them. Machine learning-based detection faces challenges due to limited accessible training data. This paper presents an instance-based transfer learning algorithm for DDoS detection, leveraging publicly available datasets for knowledge transfer. The approach filters and reconstructs instances using gain-based selection, TrAdaBoost, Q TRANSFER, and a Gaussian membership function to refine instance weighting. This enhances model generalizability to new attack types, ensuring robust detection. The proposed method improves adaptability to evolving threats, strengthening the reliability of DDoS detection technologies.

Keywords

Subject Classifications

68M1268T0768T05

References

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