2025
doi: 10.12720/jait.16.1.109-120
Computing Environments: Employing Recurrent Neural Networks and ELM for Advanced Analysis in Investigation Scenarios
T. Srinivasa Reddy1,*
,
T. Kalaichelvi2 ,
Yousef A. Baker El-Ebiary3 ,
V. Rajmohan4 ,
Janjhyam Venkata Naga Ramesh5,6
- 1Department of Computer Science and Engineering, Potti Sriramulu Chaluvidi Mallikarjun Rao College of Engineering and Technology, Vijayawada, India
- 2Department of Artificial Intelligence and Data Science, Panimalar Engineering College, Chennai, India
- 3Faculty of Informatics and Computing, UniSZA University, Kuala Nerus, Malaysia
- 4Department of Computer Science and Engineering, Saveetha School of Engineering (SIMATS), Chennai, India
- 5Department of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India
- 6Department of Computer Science and Engineering, Graphic Era Deemed To Be University, Dehradun, India
- Manuscript receivedDecember 18, 2023
- revisedFebruary 1, 2024
- acceptedApril 9, 2024
- publishedJanuary 23, 2025
Abstract
Forensic crime investigation in Cloud Computing Environments (CCE) includes meticulously examining digital evidence within cloud infrastructures in order to recognize and reduce cyber risks and criminal activity. The rising reliance on cloud technology exposes enterprises to advanced cybercrime, necessitating the need for and significance of forensic investigation in CCE. Existing approaches to forensic crime investigation frequently encounter scalability, efficiency, and adaptability issues due to the dynamic nature of cloud infrastructures. These constraints impede reliable and timely detection of cyber threats, stressing the need for novel techniques. To overcome these issues, this study provides a unique approach for forensic evidence recognition and classification in CCE using a hybrid Recurrent Neural Network (RNN) and Extreme Learning Machine (ELM). The methodology includes pre-processing based on Z-Score Normalization, data collecting, and cloud forensics evidence detection. A hybrid RNN-ELM model is put into practice, specifically designed for sequence modelling in cloud-based cybercrime data. By optimizing feature selection and boosting overall efficiency, Grey Wolf Optimization (GWO) helps to even more enhance the model’s performance. The practical usefulness of the proposed approach was demonstrated by the implementation of the study’s results in Python software. The proposed RNN-ELM method exhibits an average accuracy increase of 3.18% compared to existing methods, surpassing Deep Neural Network-Shuffled Frog Leap Optimization (DNN-SFLO) and Deep Learning Modified Neural Network (DLMNN) with accuracy percentages of 99.4%, 99.09%, and 96.25%, respectively. The created model offers a viable option for tackling the changing issues in cybercrime investigations within cloud environments. It demonstrates improved scalability, efficiency, and precision in managing forensic evidence within cloud computing scenarios.
Keywords
- forensic investigation
- cloud computing
- recurrent neural network
- extreme learning machine
- grey wolf optimization
How to Cite
T. Srinivasa Reddy, T. Kalaichelvi, Yousef A. Baker El-Ebiary, V. Rajmohan, and Janjhyam Venkata Naga Ramesh, "Computing Environments: Employing Recurrent Neural Networks and ELM for Advanced Analysis in Investigation Scenarios," Journal of Advances in Information Technology, vol. 16, no. 1, pp. 109-120, 2025. https://doi.org/10.12720/jait.16.1.109-120
Copyright & License
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
