Year: 2024
Type: article
Abstract: The Edge Computing (EC) paradigm is gaining popularity among users due to its inherent characteristics and expeditious delivery approach. Users may get information from the network's edge thanks to this feature of network architecture. The security of this edge network design, however, is a major issue. Through the Internet and in a shared setting, users can access all EC services. Intrusion detection is a method of network security that searches for threats. It is ineffective to monitor real-time network data, and current detection techniques are unable to identify known dangers. To address this problem, a technique known as augmentation oversampling is proposed, which incorporates the minority classes in the dataset. Our Sort-Augment-Combine (SAC) approach divides the dataset into subsets of the class labels, from which synthetic data is generated for each group. The developed synthetic data was then used to oversample the minority classes. After the oversampling process was complete, the distinct classes were combined to provide improved training data for model fitting. When compared to the original dataset, the models trained using the enhanced datasets perform better in terms of accuracy, recall (sensitivity), and true positives (specificity). SAC fared best in a UNSW-NB15 dataset when compared to the Synthetic Minority Oversampling Technique (SMOTE) and Generative Adversarial Network-Data Augmentation (GAN-DA). Additionally, SAC points to improvements in general sensitivity, specificity, and accuracy. SMOTE, datasets with ROSE enhancements, and Random Over-Sampling Examples for process innovation.
Source: Journal of Applied Data Sciences
Author: M Batumalay
Institution: INTI International University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4402559331