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Tehnički vjesnik, Vol. 31 No. 2, 2024.

Izvorni znanstveni članak

https://doi.org/10.17559/TV-20230613000728

Detecting and Mitigating Low-Rate DoS and DDoS Attacks: Multimodal Fusion of Time- Frequency Analysis and Deep Learning model

Thangavel Yuvaraja orcid id orcid.org/0000-0003-4108-1209 ; Department of ECE Kongunadu College of Engineering and Technology, Thottiyam, India *
Winston Gnanathika Rajan Salem Jeyaseelan ; Department of IT PSNACollege of Engineering and Technology, Dindigul, India
S Rengasamy Ashokkumar ; Department of Computer and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, India
Magudeeswaran Premkumar orcid id orcid.org/0000-0003-0517-1055 ; Department of ECE SSM Institute of Engineering and Technology, Dindigul, India

* Dopisni autor.

Puni tekst: engleski pdf 1.021 Kb

str. 495-501

preuzimanja: 403

citiraj

Sažetak

This paper outlines a method for identifying and counteracting distributed denial of service (DDoS) and low-rate denial of service (DoS) attacks. These impair significant threats to network security and can disrupt the accessibility and efficacy of systems under attack. The proposed method combines Time-Frequency Analysis (TFA) using Short-Time Fourier Transform (STFT) and a Deep Learning model (DLM), namely Recurrent Neural Network (RNN), to enhance network security. By leveraging the strengths of STFT and RNN, the approach achieves improved detection capabilities and enables timely response and effective mitigation. The CICDDoS2019 dataset has been employed to conduct the evaluation, which provides a diverse set of realistic attack traffic scenarios. The results show that the proposed approach is effective, with an impressive accuracy rate of 99.1%. Compared to traditional methods, the integrated achieves higher accuracy and lower false positive rates. This research highlights the potential of Multimodal Fusion method, for addressing the growing need for advanced defense mechanisms in today's evolving threat landscape.

Ključne riječi

DDoS; Deep Learning; DoS; network security; RNN; STFT; TFA

Hrčak ID:

314839

URI

https://hrcak.srce.hr/314839

Datum izdavanja:

29.2.2024.

Posjeta: 995 *