Development of an Intelligent Web Application Firewall (WAF) Using Machine Learning for Attack Detection and Prevention
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Abstract
This research Development of an Intelligent Web Application Firewall (WAF) Using Machine
Learning for Attack Detection and Prevention. The models used in this research consist of Machine
Learning techniques, including Random Forest and Support Vector Machine (SVM), and a Deep
Learning technique, namely Deep Neural Network (DNN). The system was trained and evaluated
using data from the CIC-IDS2017 Dataset and the OWASP Benchmark Dataset. The experimental
results indicate that the Deep Neural Network (DNN) model, which belongs to the Deep Learning
group, achieved the highest performance, with an average accuracy of 97.60% and an F1-Score of
96.65%, outperforming the Machine Learning models, namely Random Forest and SVM. The
developed WAF system is integrated with the trained model through a RESTful API and is
capable of detecting web attacks in real time, with an average response time of 120 milliseconds.
The system evaluation results show that the developed system achieved an average detection
accuracy of 96.10%. In addition, user satisfaction with the system was at the highest level
(mean score of 4.53 out of 5), particularly in terms of detection accuracy and response speed.
The findings demonstrate that the application of Deep Learning techniques, especially the Deep
Neural Network (DNN) model, can significantly enhance the effectiveness of web attack detection
compared to traditional Machine Learning techniques. Furthermore, the proposed system shows
strong potential as a cybersecurity tool capable of accurately detecting both known and previously
unseen web attacks.
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