Botnets are networks formed with a number of machines infected by malware called bots. Detection of these malicious networks is a major concern as they pose a serious threat to network security. Most of the research on botnet detection is based on particular botnet characteristics which fail to detect other types of botnet. There exist several generic botnet detection methods that can detect varieties of botnets. But, these generic detection methods perform very poorly in real-life dataset as the methods are not developed based on a real-life botnet dataset. A crucial reason for those detection methods not being developed based on a real-life dataset is that there is a scarcity of large-scale real-life botnet dataset. Due to security and privacy concerns, organizations do not publish their real-life botnet dataset. Therefore, there is a dire need for a simulation methodology that generates a large-scale botnet dataset similar to the original real-life dataset while preserving the security and privacy of the network. In this dissertation, we develop a generic bot detection methodology that can detect a variety of bots and evaluate the methodology in a real-life, large, highly class-imbalanced dataset. Numerical results show that our methodology can detect bots more accurately than the existing methods. Realizing the need for real-life large-scale botnet dataset, we develop a simulation methodology to simulate a large-scale botnet dataset from a real-life botnet dataset. Our simulation methodology is based on Markov chain and role–mining process that can simulate the degree distributions along with triangles (community structures). To scale-up the original graph to large-scale graph, we also propose a scaling-up algorithm, Enterprise connection algorithm. We evaluate our simulated graph by comparing with the original graph as well as with the graph generated by Preferential attachment algorithm. Comparisons are done in the following three major categories: comparison of botnet subgraphs, comparison of overall graphs and comparison of scaled-up graphs. Result demonstrates that our methodology outperform Preferential attachment algorithm in simulating the triangle distributions and the botnet structure.

Ces dernières années ont connu un regain d'intérêt pour l'utilisation des graphes comme moyen fiable de représentation et de modélisation des données, et ce, dans divers domaines de l'informatique. En particulier, pour les grandes masses de données, les graphes apparaissent comme une alternative prometteuse aux bases de données relationnelles. Plus particulièrement, le recherche de sous-graphes s'avère être une tâche cruciale pour explorer ces grands jeux de données. Dans cette thèse, nous étudions deux problématiques principales. Dans un premier temps, nous abordons le problème de la détection de motifs dans les grands graphes. Ce problème vise à rechercher les k-meilleures correspondances (top-k) d'un graphe motif dans un graphe de données. Pour cette problématique, nous introduisons un nouveau modèle de détection de motifs de graphe nommé la Simulation Relaxée de Graphe (RGS), qui permet d’identifier des correspondances de graphes avec un certain écart (structurel) et ainsi éviter le problème de réponse vide. Ensuite, nous formalisons et étudions le problème de la recherche des k-meilleures réponses suivant deux critères, la pertinence (la meilleure similarité entre le motif et les réponses) et la diversité (la dissimilarité entre les réponses). Nous considérons également le problème des k-meilleures correspondances diversifiées et nous proposons une fonction de diversification pour équilibrer la pertinence et la diversité. En outre, nous développons des algorithmes efficaces basés sur des stratégies d’optimisation en respectant le modèle proposé. Notre approche est efficiente en terme de temps d’exécution et flexible en terme d'applicabilité. L’analyse de la complexité des algorithmes et les expérimentations menées sur des jeux de données réelles montrent l’efficacité des approches proposées. Dans un second temps, nous abordons le problème de recherche agrégative dans des documents XML. Pour un arbre requête, l'objectif est de trouver des motifs correspondants dans un ou plusieurs documents XML et de les agréger dans un seul agrégat. Dans un premier temps nous présentons la motivation derrière ce paradigme de recherche agrégative et nous expliquons les gains potentiels par rapport aux méthodes classiques de requêtage. Ensuite nous proposons une nouvelle approche qui a pour but de construire, dans la mesure du possible, une réponse cohérente et plus complète en agrégeant plusieurs résultats provenant de plusieurs sources de données. Les expérimentations réalisées sur plusieurs ensembles de données réelles montrent l’efficacité de cette approche en termes de pertinence et de qualité de résultat. / Recent years have witnessed a growing renewed interest in the use of graphs as a reliable means for representing and modeling data. Thereby, graphs enable to ensure efficiency in various fields of computer science, especially for massive data where graphs arise as a promising alternative to relational databases for big data modeling. In this regard, querying data graph proves to be a crucial task to explore the knowledge in these datasets. In this dissertation, we investigate two main problems. In the first part we address the problem of detecting patterns in larger graphs, called the top-k graph pattern matching problem. We introduce a new graph pattern matching model named Relaxed Graph Simulation (RGS), to identify significant matches and to avoid the empty-set answer problem. We formalize and study the top-k matching problem based on two classes of functions, relevance and diversity, for ranking the matches according to the RGS model. We also consider the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Moreover, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the proposed model. The proposed approach is optimal in terms of search time and flexible in terms of applicability. The analyze of the time complexity of the proposed algorithms and the extensive experiments on real-life datasets demonstrate both the effectiveness and the efficiency of these approaches. In the second part, we tackle the problem of graph querying using aggregated search paradigm. We consider this problem for particular types of graphs that are trees, and we deal with the query processing in XML documents. Firstly, we give the motivation behind the use of such a paradigm, and we explain the potential benefits compared to traditional querying approaches. Furthermore, we propose a new method for aggregated tree search, based on approximate tree matching algorithm on several tree fragments, that aims to build, the extent possible, a coherent and complete answer by combining several results. The proposed solutions are shown to be efficient in terms of relevance and quality on different real-life datasets

The electromagnetic machines like the dc, induction, synchronous motor/generator and the transformer have an energy flow framework that is similar. All these machines deal with electrical energy in the electrical domain that is interfaced with the magnetic domain. Except for the transformer, the other machines also have one more energy interface i.e. with the mechanical domain. In all these machines, the magnetic domain acts as the silent energy manager. The electrical and the mechanical domain energies will have to pass through the magnetic domain and appropriately get routed. In recognition of the commonality of this pattern of energy flow, this thesis proposes a generalised model of a multiphase electromagnetic device wherein the dc machine, induction machine, synchronous machine and the transformers are special cases of the proposed generalised model. This is derived using bond graphs that is based on the underlining principle of Energy Flow rooted in the concept of Conservation of Energy. A model is a set of mathematical equations representing a physical system. A model is as good as a modeller understanding of the physical system and the underlying approximation he makes while writing down the equations describing the models behaviour to the stimulus. A modelling language tool, which can cut down the approximations made by using the power of identified analogous characteristics across the physical domain, can help make a model more close to real life situation. Bond Graph is such a modelling language which is powerful enough to model the non-linear, multi-disciplinary, hybrid continuous-discrete phenomena encountered in a real life physical system. Bond graphs as a modelling tool was introduced by Professor H.Paynter at Massachusetts Institute of Technology in 1959. The Bond Graph methodology is based on consideration of energy flows between the ports of the components of an engineering system. Bond Graph methodology enables one to develop a graphical model that is consistent with the first principle of energy conservation without having the need to start with establishing and reformulating equations. The derivation of a mathematical model from the graphical description is automated by software tools. As a consequence, a modeller using this methodology can focus on modelling of the physical system. In the graphical representation of bond graph the vertices of a bond graph denote subsystems, system components or elements, while the edges, called power bonds, represent energy flows between them. The nodes of a bond graph have power ports where energy can enter or exit. Bond graph models are developed in a hierarchical top-down or bottom-up approach by using component models or elements from model libraries. An electromagnetic machine is a black box having an assemblage of windings in iron resulting in a combination of input/output ports on shaft and electrical terminals. Abstraction of an machine model by a modeller matching the vision of the observer above is an ideal goal. Bond graph methodology is an appropriate tool for trying to reach this goal as it is based on object oriented modelling techniques. There have been few attempts to model electric machine in bond graph earlier. A well established DC motor bond graph has been widely used in all bond graph literature. But AC rotating machine being a higher order nonlinear system poses a tougher challenge. Here too, there have been few attempts in modelling AC machines. It is observed that majority of AC machine bond graph models have been built up from their mathematical models. But as the bond graph modelling technique is based on the unifying theory of energy exchange, better insight into the system is achievable if the model is conceptualised from its physical structure. This thesis starts from the basic theory of energy port to conceptualise the generalised model from physical correspondence. In this thesis a Rotating Electrical Machine is studied as a physical system. The energy ports inside this physical system is identified. When a physical system receives the energy through its energy port in one energy cycle, it processes this energy in one of the three ways. The received energy is converted into useful work or it is dissipated or stored. The storage can further be classified into two ways, either as kinetic energy or as potential energy. For a rotating electric machine the input-output port for energy exchange are either in electrical or mechanical domain depending on the class of the machine. The magnetic domain across all class of electromagnetic device acts as the energy manager. In order to capture the features of the energy jumping across the air gap in a rotating electrical machine, wherein the magnetic fields from spatially distributed windings of the stator and rotor interplay, an Axis Rotator (AR) element -a mathematical commutator, is introduced in this thesis as a new bond graph element. In a multiphase device, the energy from the various phases and spatial axes are transferred through the axis rotator element. The Axis Rotator is a critical element which helps distinguish between the various classes of electromagnetic devices. The defining features of the Axis Rotator helps in deriving the various special electromagnetic devices (such as the dc machine, induction machine, synchronous machine and the transformer) from the generalised model. The Axis Rotator exists in the magnetic domain. It naturally inherits the characteristics of the magnetic domain. The Axis Rotator as a bond graph element is complex. In a specific case of 3φ Induction Motor an alternative bond graph model with all integral elements is developed. By one to one correspondence with the AR bond graph model, the inner component of ’AR’ can be identified. Another advantage of using this model is that saturable and non-saturable magnetic permeance can be separated out, a useful feature in the nonlinear model discussed next. One of the most distinguishing features of the magnetic domain is the existence of Magnetic Hysteresis. Magnetic Hysteresis is a well understood and studied subject. But this physical process is wilfully ignored by the modelling community at large. The main reason for this is the difficulty of modelling a nonlinear phenomena. The bond graph modelling naturally allows the inclusion of such non-idealities within its framework. This thesis proposes the generalised model along with the inclusion of magnetic non-linearities and non-idealities into the model of the system. This inherent strength of bond graph model flows from the fact that the models in bond graph are developed from the first principles of energy conversation and the mathematical equations are derived later from the evolved graph. The tools that are available for bond graph simulation are not adequate for power electronics systems. The existing tools do not address space vectors and frame transformations. As a consequence it is difficult to simulate the electromagnetic device models developed in this thesis. The need for a bond graph tool to address vectors and frame transformations, a common occurrence in electric machines dynamic model study was acutely felt. This necessitated a support for handling complex data class from the underlying mathematical engine of the software. MATLAB/Simulink is the commonly available mathematical tool which has a support for complex variables. Therefore during the course of this research work a new software tool box was developed which meets the need of electromagnetic machines in particular and other engineering domains in general. For developing the new bond graph simulation software, the language extender approach was chosen, as it combines the capabilities of existing popular mathematical engine with its tested graphical frontend and the flexibility of combining different modelling technique like bond graph, block diagram, equations etc. It also ensures portability as they are compiled by interpreted language compiler of the mathematical engine and are thus independent of the computer operating system. C-MEX S-function methodology was used to develop the software as it has access to lower level functions and methods of the underlying mathematical engine. This helps in speeding up the software execution time alongwith the flexibility in defining new complex elements like the Nonlinear Axis Rotator. In conclusion, this thesis makes the following contributions: (i) The Axis rotator concept to handle space vectors and frame transformations, (ii) generalised model of the electromagnetic device, (iii) introduction of the saturation and hysteresis non-linearity in the magnetic domain, (iv) development of the bond graph toolbox to handle vector and frame transformations.