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Anomaly based intrusion detection systems rely on the build of a normal behavior model. When a deviation from this normal behavior is detected, an alert is raised. This anomaly approach, unlike the misuse approach, is able to detect unknown attacks. A basic technique to build such a model for a program is to use the system call sequences of the process. To improve the accuracy and completeness of this detection model, we can add information related to the system call, such as its arguments or its execution context. But even then, attacks that target non-control-data may be missed and attacks on control-data may be adapted to bypass the detection mechanism using evasion techniques.We propose in this article an approach that focuses on the detection of non-control-data attacks. Our approach aims at exploiting the internal state of a program to detect a memory corruption on non-control-data that could lead to an illegal system call. To achieve this, we propose to build a dataoriented detection model by statically analyzing a program source code. This model is used to instrument the program by adding reasonableness checks that verify the consistent state of the data items the system calls depend on. We thus argue that it is possible to detect a program misuse issued by a non-control-data attack inside the program during its execution. While keeping a low overhead, this approach allows to detect non-control-data attacks.

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The use of diversity and redundancy in the security domain is an interesting approach to prevent or detect intrusions. Many researchers have proposed architectures based on those concepts where diversity is either natural or artificial. These architectures are based on the architecture of N-version programming and were often instantiated for web servers without taking into account the web application(s) running on those. In this article, we present a solution to protect the web applications running on this kind of architectures in order to detect and tolerate code injection intrusions. Our solution consists in creating diversity in the web application scripts by randomizing the language understood by the interpreter so that an injected code can not be executed by all the servers. We also present the issues related to the automatization of our solution and present some solutions to tackle these issues.

The most common anomaly detection mechanisms at application level consist in detecting a deviation of the control-flow of a program. A popular method to detect such anomaly is the use of application sequences of system calls. However, such methods do not detect mimicry attacks or attacks against the integrity of the system call parameters. To enhance such detection mechanisms, we propose an approach to detect in the application the corruption of data items that have an influence on the system calls. This approach consists in building automatically a data-oriented behaviour model of an application by static analysis of its source code. The proposed approach is illustrated on various examples, and an injection method is experimented to obtain an approximation of the detection coverage of the generated mechanisms.