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Web Server Attack Detection using Machine Learning

Today, every single organization is utilizing web applications for extension of its business as a result of the high accessibility of web and simple access. With the expansion of web use, the danger of attacks has increased likewise. An efficient monitoring software which can detect these attacks timely is required. HTML, PHP and Java script are used for websites development and SQL is used for database management extensively. Most common web server attacks include SQL injection, DOS (Denial of Service) and XSS (Cross Site Scripting). Rule based intrusion detection systems work on keywords and patterns and are unable to detect unknown attacks. This paper proposes machine learning based model for intrusion detection using web server logs. This model detects whether a specific log is normal or an attack log and also specifies the type of attack. Web server logs are generated and collected by creating a private network using an Apache WAMP server.