Abstract: Internet and Web servers become the core infrastructure for companies and institutes. Simultaneously, Web servers also become a popular target for attackers. However, misuse Intrusion Detection Systems (IDSs) are only effective in detecting known attacks and it is difficult to keep up with the daily exploitation of novel and Web-related vulnerabilities; anomaly IDSs often produce a high false alarm rate. To get over the limitations of misuse and anomaly IDSs, this paper inspired by immune principles presents a novel anomaly detection approach to detect unknown Web attacks. In our proposed approach, which is referred to the immune principles Inspired Approach to Detection of Web attacks (IADW), mathematical formulas of self, non-self, antigen, library of antibody genes, immunocyte, and etc., are given, and immune-learning algorithm is described. Experiment results show that our approach can detect unknown attacks with lower false alarm rate, missing alarm rate, and higher detection rate and identification rate than the technique based on neural network and ID3. Thus, it provides an effective novel solution to detection of Web attacks.