Securing the Web: A Survey of Web Intrusion Detection Methods
Keywords:
Anomaly detection, Artificial intelligence, Intrusion Detection Systems (IDS), Machine learning, Web intrusionAbstract
As the reliance on web-based applications and services continues to proliferate, the threats posed by malicious intrusions into these systems have become a paramount concern. This review paper explores the realm of web intrusion detection, providing a comprehensive overview of the state-of-the-art techniques, methodologies, and advancements in the field. Subsequently, the paper categorizes and examines various web intrusion detection approaches, ranging from signature-based methods to anomaly detection and machine learning-based techniques. Each approach’s strengths, limitations, and real-world applications are scrutinized to offer a well-rounded understanding of the current landscape. The paper delves into the evolution of intrusion detection systems, taking into account the dynamic nature of web attacks and the shifting attack vectors. It evaluates the role of big data analytics, artificial intelligence, and machine learning in enhancing the efficiency and accuracy of intrusion detection.
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