Title: CLMO-XAI-SVDM: cannibalistic lead with long short-term memory hybridised explainable artificial intelligence for DDoS attack detection

Authors: Komal Jakotiya; Vishal Shirsath; Sharanbasawa Inamadar

Addresses: Computer Engineering, School of Engineering, ADYPU, DY Patil Knowledge City Road Via Lohgaon, Airport Rd, Charholi Budruk, Pune, Maharashtra, 412105, India ' Computer Engineering, School of Engineering, ADYPU, DY Patil Knowledge City Road Via Lohgaon, Airport Rd, Charholi Budruk, Pune, Maharashtra, 412105, India ' Computer Engineering, School of Engineering, ADYPU, DY Patil Knowledge City Road Via Lohgaon, Airport Rd, Charholi Budruk, Pune, Maharashtra, 412105, India

Abstract: Distributed denial of service (DDoS) attack detection is necessitated as the security of the network data in recent decades is highly demandable. Several researches exist with numerous advantages but still contain certain challenges. To deal with the limitations and to perform significant attack detection, cannibalistic lead with long short-term memory hybridised explainable artificial intelligence (CLMO-XAI-SDBM) is proposed in the research. Further, the incorporation of the CLMO algorithm enhances the efficiency of the detection model as it remains the combination of characteristics of the bio-inspired algorithms. The classifier model included in the proposed research provides the advantages of handling the high-dimensional data and learning the multi-scaled features at different time series. The experimental results demonstrated that the proposed model attains high efficiency, which is evaluated with metrics such as precision, recall, and F1-score attaining 95.72%, 95.78%, and 95.71% respectively.

Keywords: attack detection; DDoS; distributed denial of service; network traffic; deep learning; explainable artificial intelligence.

DOI: 10.1504/IJNVO.2025.153489

International Journal of Networking and Virtual Organisations, 2025 Vol.33 No.4, pp.279 - 308

Received: 22 Oct 2024
Accepted: 03 Nov 2025
Published online: 11 May 2026 *

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