Web Intrusion Detection with Machine Learning Algorithm

Web intrusion detection involves the identification and thwarting of unauthorized access or attacks targeting web applications. With the increasing use of the internet and the rise in cybercrime, web intrusion detection has become an essential aspect of cybersecurity. This report paper provides an overview of various web intrusion detection techniques, including signature-based, anomaly-based, and hybrid approaches. The paper also discusses the challenges and limitations of web intrusion detection and highlights the importance of using multiple detection techniques to improve accuracy and reduce false positives. Additionally, the paper covers the latest trends in web intrusion detection, such as machine learning-based approaches, and concludes with recommendations for improving web intrusion detection in the future.Web intrusion detection is also a crucial aspect of cybersecurity in modern times. As web applications become more prevalent, the likelihood of attacks also increases, making it necessary to have robust mechanisms in place to detect and prevent such attacks. In this report paper, we explore various techniques for web intrusion detection, including signature-based and anomaly based approaches.