A learning-based anomaly detection model of SQL attacks
With the rapid development of Internet, more and more enterprises, research and finance institutions connect their databases to the Internet for resource sharing. However, due to developers technical may be uneven, or they does not take security considerations into account, web applications become vulnerable to the attacks, thus the network databases will face the threats. Many e-service providers are reported to have leaked customers information through their websites. This paper presents a learning-based anomaly detection model of SQL attacks deployed between web server and database server; it creates a legitimate library while learning, and detects the threats using the library. This model recovers the fault of signature-based model which can not detect new types of attacks. Compared to the traditional anomaly detection technology, it is more flexible and can eliminate the complicated steps of establish the legal library manually.
SQL attacks learning-based anomaly detection database protection
Xu Ruzhi Deng Liwu Guo jian
Control and Computer Engineering North China Electric Power University Beijing, China
国际会议
北京
英文
1-4
2010-06-25（万方平台首次上网日期，不代表论文的发表时间）