@inproceedings{16c4ea84d30c438fa0598bd51bbf4314,
title = "Recurrent Neural Network (RNN) Based Model for Enhanced Cyberattack Detection in Cloud Forensics",
abstract = "In this paper, we present a Recurrent Neural Network (RNN)-based model designed to improve the detection of cyberattacks within cloud forensic investigations. The presented model addressed the challenge of class imbalance in cyberattack datasets. The model employed the Synthetic Minority Over-sampling Technique (SMOTE) to balance the dataset. The proposed model used random forest model for the selection of optimal feature set. The proposed model gives an accuracy of 0.99. The proposed model showcases the potential of RNNs in the area of digital forensics and cybersecurity. The proposed model provides a reliable tool for the identification and classification of malicious activities in cloud environments.",
keywords = "Cloud Forensics, Cyberattack Detection, Deep Learning, Recurrent Neural Networks",
author = "Liang Zhou and Chui, \{Kwok Tai\} and Akshat Gaurav and Gupta, \{Brij B.\} and Varsha Arya",
note = "Publisher Copyright: {\textcopyright} 2026 IEEE.; 2026 IEEE International Conference on Consumer Electronics, ICCE 2026 ; Conference date: 03-02-2026 Through 05-02-2026",
year = "2026",
doi = "10.1109/ICCE67443.2026.11449689",
language = "English",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
booktitle = "2026 IEEE International Conference on Consumer Electronics, ICCE 2026",
}