2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 19 , Issue 2 , PP: 392-401, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed F. Almukhtar 1 * , Noor D. AL-Shakarchy 2 , Mais Saad Safoq 3
Doi: https://doi.org/10.54216/FPA.190228
In the digital age, e-commerce platforms are critical components of the global economy, facilitating seamless transactions and interactions between businesses and consumers. The digital infrastructure of these institutions is frequently attacked, either to hack or disrupt online services, leading to significant financial losses and damage to reputation. The most famous of these attacks are DDoS attacks, which lead to an increase in the volume of traffic to the platform's website beyond the capacity of the servers, thus causing the platform to respond slowly and crash and customers to be unable to access it. The increase in these attacks causes significant material damage to institutions, whether in the loss of revenues or the cost of responding to attacks. This work presents a robust DDoS attacks early detection model that can be adopted on e-commerce platforms using a lightweight one-dimension Convolutional neural network. The proposed model leverages the efficiency of deep learning with the lightweight architecture to analyze network traffic in real time, identifying patterns indicative of an impending DDoS attack. The balance between high detection accuracy with computational efficiency makes it suitable for real-time implementation in diverse e-commerce environments. DNN is trained on a comprehensive dataset of network traffic, encompassing both normal and attack scenarios, to ensure it can distinguish between legitimate traffic spikes and malicious activity. DDoS Evaluation Dataset CIC-DDoS2019 and CICIDS2017 are used in the experimental and accuracy achieved 0.98 and 0.99 in these two datasets respectively.
Distributed Denial of Service (DDoS) , One Dimensional Convolutional Neural Network (1D CNN) , Convolution layer , Max pooling Layer , Dropout layer , Normalization
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