Web applications vulnerability analysis and intrusion detection systems assessment With the increasing development of Internet, Web applications have become increasingly vulnerable and exposed to malicious attacks that could affect essential properties such as confidentiality, integrity or availability of information systems. To cope with these threats, it is necessary to develop efficient security protection mechanisms and testing techniques (firewall, intrusion detection system, Web scanner, etc..). The question that arises is how to evaluate the effectiveness of such mechanisms and what means can be implemented to analyze their ability to correctly detect attacks against Web applications. This thesis presents a new methodology, based on web pages clustering, that is aimed at identifying the vulnerabilities of a Web application following a black box analysis of the target application. Each identified vulnerability is actually exploited to ensure that the identified vulnerability does not correspond to a false positive. The proposed approach can also highlight different potential attack scenarios including the exploitation of several successive vulnerabilities, taking into account explicitly the dependencies between these vulnerabilities. We have focused in particular on code injection vulnerabilities, such as SQL injections. The proposed method led to the development of a new Web vulnerability scanner and has been validated experimentally based on various vulnerable applications. We have also developed an experimental platform integrating the new web vulnerability scanner, that is aimed at assessing the effectiveness ofWeb applications intrusion detection systems, in a context that is representative of the threats that such applications face in operation. This platform integrates several tools that are designed to automate as much as possible the evaluation campaigns. It has been used in particular to evaluate the effectiveness of two intrusion detection techniques that have been developed by our partners of the collaborative project DALI, funded by the ANR, the French National Research Agency.
