AI-Based Detection of Abnormal Traffic Patterns in Web Applications
DOI:
https://doi.org/10.63282/3050-922X.ICRCEDA25-112Keywords:
AI-based detection, abnormal traffic patterns, web applications, machine learning, cybersecurity, anomaly detection, deep learning, network security, intrusion detection, real-time monitoringAbstract
With the rapid growth of web applications, the need for robust security mechanisms to protect against malicious activities has become paramount. Cyber threats such as Distributed Denial of Service (DDoS) attacks, SQL injection, and credential stuffing have evolved, making traditional rule-based security mechanisms inadequate. AI-based detection techniques, particularly those leveraging machine learning and deep learning, have emerged as effective solutions to detect abnormal traffic patterns in web applications. This paper explores the implementation of AI-based anomaly detection for identifying malicious web traffic. We discuss the significance of data collection, feature selection, and model training to enhance detection accuracy. The study employs supervised and unsupervised learning techniques such as Support Vector Machines (SVM), Decision Trees, and neural networks to classify traffic as normal or abnormal. Additionally, we investigate real-time traffic monitoring and adaptive learning mechanisms to detect new and evolving threats. The experimental results demonstrate that AI-driven models outperform traditional security mechanisms in detecting anomalies with high accuracy and minimal false positives. The paper also presents a comparative analysis of different AI techniques, challenges in deploying AI-based solutions, and future research directions. This research highlights the potential of AI-based approaches in improving cybersecurity resilience and mitigating threats in web applications
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