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Volume 13 | Issue 6 | Year 2026 | Article Id. IJECE-V13I6P118 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I6P118

ApR-FM2TEDL: Enhanced Tri-Ensemble Optimized Deep Learning Framework for Attack Detection and Mitigation in Cloud Networks

Yogesh B. Sanap, Pushpalata G. Aher

Received Revised Accepted Published
20 Mar 2026 19 Apr 2026 18 May 2026 27 Jun 2026

Citation :

Yogesh B. Sanap, Pushpalata G. Aher, "ApR-FM2TEDL: Enhanced Tri-Ensemble Optimized Deep Learning Framework for Attack Detection and Mitigation in Cloud Networks," International Journal of Electronics and Communication Engineering, vol. 13, no. 6, pp. 218-247, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I6P118

Abstract

The usage of web services has increased the volume of data across cloud computing systems with a larger range of users. Cloud networks provide convenient access to users with storage facilities for evolving circumstances. However, the distributed nature of cloud networks, as well as the increasing data volume, leads to various attacks that affect the integrity of the stored information. Several methods have been presented for the detection of attack networks; however, they are vulnerable to limitations, including lower accuracy, longer training time, overfitting issues, and higher complexities. Consequently, the research overcomes these limitations with the Proposed Apis Random movement optimized Fuzzy Min-Max Enabled Tri-Ensemble Deep Learning (ApR-FM2TEDL) framework that exhibits accurate detection. The incorporation of the Apis Random Movement Optimized Synthetic Minority (ApRO-SyM) sampling method produced synthetic samples over the minority class that prevent the overfitting issues. Specifically, the Apis Random movement Optimization (ApRMO) Algorithm carries out hyperparameter tuning and improves the data generation by choosing better neighbor sets. The effectiveness of the model is computed using training percentage, which shows improvements in the sensitivity of 96.20%, accuracy of 97.74%, and specificity of 98.59% for the BOT-IoT dataset. Further, the proposed ApR-FM2TEDL framework achieves a superior accuracy of 97.13%, sensitivity of 98.56%, and specificity of 96.72% with 80% of training using the CIC-DDoS2019 dataset, outperforming the other baseline models.

Keywords

Attack Detection, Deep Learning, Synthetic Sampling, Cloud Computing, Traffic Flow.

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