L'utilisation massive des plateformes multi-cœurs et multi-processeurs a pour effet de favoriser la programmation parallèle à mémoire partagée. Néanmoins, exploiter efficacement et de manière correcte le parallélisme sur ces plateformes reste un problème de recherche ouvert. De plus, leur modèle d'exécution sous-jacent, et notamment les modèles de mémoire "relâchés", posent de nouveaux défis pour les outils d'analyse statiques et dynamiques. Dans cette thèse nous abordons deux aspects importants dans le cadre de la programmation sur plateformes multi-cœurs et multi-processeurs: l'optimisation de sections critiques implémentées selon l'approche pessimiste, et l'analyse dynamique de flots d'informations. Les sections critiques définissent un ensemble d'accès mémoire qui doivent être exécutées de façon atomique. Leur implémentation pessimiste repose sur l'acquisition et le relâchement de mécanismes de synchronisation, tels que les verrous, en début et en fin de sections critiques. Nous présentons un algorithme générique pour l'acquisition/relâchement des mécanismes de synchronisation, et nous définissons sur cet algorithme un ensemble de politiques particulier ayant pour objectif d'augmenter le parallélisme en réduisant le temps de possession des verrous par les différentes threads. Nous montrons alors la correction de ces politiques (respect de l'atomicité et absence de blocages), et nous validons expérimentalement leur intérêt. Le deuxième point abordé est l'analyse dynamique de flot d'information pour des exécutions parallèles. Dans ce type d'analyse, l'enjeu est de définir précisément l'ordre dans lequel les accès à des mémoires partagées peuvent avoir lieu à l'exécution. La plupart des travaux existant sur ce thème se basent sur une exécution sérialisée du programme cible. Ceci permet d'obtenir une sérialisation explicite des accès mémoire mais entraîne un surcoût en temps d'exécution et ignore l'effet des modèles mémoire relâchées. A contrario, la technique que nous proposons permet de prédire l'ensemble des sérialisations possibles vis-a-vis de ce modèle mémoire à partir d'une seule exécution parallèle ("runtime prediction"). Nous avons développé cette approche dans le cadre de l'analyse de teinte, qui est largement utilisée en détection de vulnérabilités. Pour améliorer la précision de cette analyse nous prenons également en compte la sémantique des primitives de synchronisation qui réduisent le nombre de sérialisations valides. Les travaux proposé ont été implémentés dans des outils prototype qui ont permit leur évaluation sur des exemples représentatifs. / The invasion of multi-core and multi-processor platforms on all aspects of computing makes shared memory parallel programming mainstream. Yet, the fundamental problems of exploiting parallelism efficiently and correctly have not been fully addressed. Moreover, the execution model of these platforms (notably the relaxed memory models they implement) introduces new challenges to static and dynamic program analysis. In this work we address 1) the optimization of pessimistic implementations of critical sections and 2) the dynamic information flow analysis for parallel executions of multi-threaded programs. Critical sections are excerpts of code that must appear as executed atomically. Their pessimistic implementation reposes on synchronization mechanisms, such as mutexes, and consists into obtaining and releasing them at the beginning and end of the critical section respectively. We present a general algorithm for the acquisition/release of synchronization mechanisms and define on top of it several policies aiming to reduce contention by minimizing the possession time of synchronization mechanisms. We demonstrate the correctness of these policies (i.e. they preserve atomicity and guarantee deadlock freedom) and evaluate them experimentally. The second issue tackled is dynamic information flow analysis of parallel executions. Precisely tracking information flow of a parallel execution is infeasible due to non-deterministic accesses to shared memory. Most existing solutions that address this problem enforce a serial execution of the target application. This allows to obtain an explicit serialization of memory accesses but incurs both an execution-time overhead and eliminates the effects of relaxed memory models. In contrast, the technique we propose allows to predict the plausible serializations of a parallel execution with respect to the memory model. We applied this approach in the context of taint analysis , a dynamic information flow analysis widely used in vulnerability detection. To improve precision of taint analysis we further take into account the semantics of synchronization mechanisms such as mutexes, which restricts the predicted serializations accordingly. The solutions proposed have been implemented in proof of concept tools which allowed their evaluation on some hand-crafted examples.

Context: Every day increasing number of malwares are spreading around the world and infecting not only end users but also large organizations. This results in massive security threat for private data and expensive computer resources. There is lot of research going on to cope up with this large amount of malicious software. Researchers and practitioners developed many new methods to deal with them. One of the most effective methods used to capture malicious software is dynamic malware analysis. Dynamic analysis methods used today are very time consuming and resource greedy. Normally it could take days or at least some hours to analyze a single instance of suspected software. This is not good enough especially if we look at amount of attacks occurring every day. Objective: To save time and expensive resources used to perform these analyses, AMA: an automated malware analysis system is developed to analyze large number of suspected software. Analysis of any software inside AMA, results in a detailed report of its behavior, which includes changes made to file system, registry, processes and network traffic consumed. Main focus of this study is to develop a model to automate the runtime analysis of software which provide detailed analysis report and evaluation of its effectiveness. Methods: A thorough background study is conducted to gain the knowledge about malicious software and their behavior. Further software analysis techniques are studied to come up with a model that will automate the runtime analysis of software. A prototype system is developed and quasi experiment performed on malicious and benign software to evaluate the accuracy of the newly developed system and generated reports are compared with Norman and Anubis. Results: Based on thorough background study an automated runtime analysis model is developed and quasi experiment performed using implemented prototype system on selected legitimate and benign software. The experiment results show AMA has captured more detailed software behavior then Norman and Anubis and it could be used to better classify software. Conclusions: We concluded that AMA could capture more detailed behavior of the software analyzed and it will give more accurate classification of the software. We also can see from experiment results that there is no concrete distinguishing factors between general behaviors of both types of software. However, by digging a bit deep into analysis report one could understand the intensions of the software. That means reports generated by AMA provide enough information about software behavior and can be used to draw correct conclusions. / +46 736 51 83 01

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