1 Department of Computer Science, Nasarawa State University, Keffi, Nasarawa State-Nigeria.
2 Department of Computer Science, Federal University of Technology, Owerri, Nigeria.
Research Article
World Journal of Advanced Research and Reviews, 2023, 19(03), 1007-1019
Article DOI: 10.30574/wjarr.2023.19.3.1334
Received on 08 August 2023; revised on 17 September 2023; accepted on 19 September 2023
This study developed a novel intrusion detection system (IDS) for cloud computing using artificial neural networks (ANNs) and machine learning techniques. The proposed IDS uses an adaptive architecture capable of detecting malicious activities within a cloud computing environment. To process and optimize the data, Adam optimization techniques were employed, and MiniMaxScaler was used to normalize the data for training. The model was designed using the TensorFlow framework for ANNs, and the LSD methodology was employed in the development. The training was conducted using the University of New Brunswick Intrusion Detection Systems dataset, which had been preprocessed. Results indicate that the proposed architecture was highly effective in detecting various attacks, with low false-positive and false-negative rates. The training and validation accuracies were 99.7% and 99.9%, respectively, using this method. This approach can automatically detect the nature of attacks, saving time and resources.
(ABS) Artificial Neural Network; Intrusion Detection System; Deep Learning; Model and Cloud Computing
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Wampana Asua Paul, Binyamin Adeniyi Ajayi, Rashidah Funke Olanrewaju and Muhammad Umar Abdullahi. Analysis of intrusion detection system in cloud computing environment using artificial neural network. World Journal of Advanced Research and Reviews, 2023, 19(3), 1007-1019. Article DOI: https://doi.org/10.30574/wjarr.2023.19.3.1334