Enhanced SVM model using PCA-autoencoder for DDoS-DNS attack detection in E-commerce networks
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Innovative Research and Scientific Studies
Abstract
E-commerce platforms are increasingly targeted by cyber-attacks, resulting in substantial financial losses and damage to
their reputation. Many traditional security methods are inadequate at detecting these sophisticated attacks, highlighting the
need for smarter solutions. This project addresses this issue by developing a machine learning system specifically designed
for detecting intrusions in e-commerce environments. Six model combinations were evaluated, utilizing data simplification
techniques such as Principal Component Analysis (PCA) and Autoencoders, alongside classification tools like Support
Vector Machine (SVM), XGBoost, and AdaBoost. The models were assessed using the CIC-IDS2017 dataset, which
simulates real network traffic scenarios. Their performance was measured based on accuracy, precision, recall, and F1
score. Among the tested models, the Autoencoder-XGBoost combination demonstrated the highest accuracy and the most
effective detection capabilities. This suggests that employing deep learning techniques for feature selection, combined with
robust ensemble methods, enhances intrusion detection performance. In conclusion, this project demonstrates that machine
learning can significantly improve the security of e-commerce platforms when integrated with data simplification and
ensemble learning strategies. The developed system offers a more accurate and efficient approach to identifying cyber
threats, laying the foundation for future research into more advanced and adaptable cybersecurity solutions.