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The first detected computer virus commenced in the 1970s. Since then, malware infections have grown exponentially along with rapid increases within the digital environment. Malware detection is a challenging task due to the relentless growth in complexity and volume. That is why the need for automated detection arises. Applying machine learning to malware detection is not a new trend, and researchers have been experimenting with since the 1990s. This thesis aims to evaluate classification algorithms to discover malicious Portable Executables by looking at their static features. Six machine learning models were built and tested based on 20,000 malicious and benign files. Random Forest scored the highest cross-validation score of 99.3% amongst the models with 15 features. Selecting the number of features was based on research of previous studies. This thesis confirms that it is possible to use machine learning for static malware detection. It can also help for future automated malware analysis research. / Det första datorviruset upptäcktes på 1970-talet. Sedan dess, har antalet attacker ökat i och med den skenande digitala utvecklingen. Att finna skadlig kod är en utmanade uppgift då de ökar i komplexitet och volym. Därför finns det ett behov att automatisera spårningen. Att använda maskininlärning för upptäckt av skadlig kod är inte en ny trend och forskare har experimenterat med det sedan år 1990. Syftet med denna avhandling är att utvärdera klassificeringsalgortimer för att upptäckta skadlig kod i Portable Executables genom att använda statiska prediktorer. Sex stycken maskininlärnings modeller skapades och testades baserat på 20.000 skadliga och legitima filer. Random Forest uppnådde det högsta korsvalderingsvärdet på 99.3% av dessa modeller med 15 prediktorer. Att använda 15 prediktorer var inspirerat av forskning av tidigare studier. Denna avhandling bevisar att det är möjligt att använda maskininlärning för statisk spårning av skadlig kod. Det kan också användas för framtida automatiserade forskningsstudier om skadlig kod.

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Traditional workflow systems have several drawbacks, e.g. in their inabilities to rapidly react to changes, to construct workflow automatically (or with user involvement) and to improve performance autonomously (or with user involvement) in an incremental manner according to specified goals. Overcoming these limitations would be highly beneficial for complex domains where such adversities are exhibited. Video processing is one such domain that increasingly requires attention as larger amounts of images and videos are becoming available to persons who are not technically adept in modelling the processes that are involved in constructing complex video processing workflows. Conventional video and image processing systems, on the other hand, are developed by programmers possessing image processing expertise. These systems are tailored to produce highly specialised hand-crafted solutions for very specific tasks, making them rigid and non-modular. The knowledge-based vision community have attempted to produce more modular solutions by incorporating ontologies. However, they have not been maximally utilised to encompass aspects such as application context descriptions (e.g. lighting and clearness effects) and qualitative measures. This thesis aims to tackle some of the research gaps yet to be addressed by the workflow and knowledge-based image processing communities by proposing a novel workflow composition and execution approach within an integrated framework. This framework distinguishes three levels of abstraction via the design, workflow and processing layers. The core technologies that drive the workflow composition mechanism are ontologies and planning. Video processing problems provide a fitting domain for investigating the effectiveness of this integratedmethod as tackling such problems have not been fully explored by the workflow, planning and ontological communities despite their combined beneficial traits to confront this known hard problem. In addition, the pervasiveness of video data has proliferated the need for more automated assistance for image processing-naive users, but no adequate support has been provided as of yet. A video and image processing ontology that comprises three sub-ontologies was constructed to capture the goals, video descriptions and capabilities (video and image processing tools). The sub-ontologies are used for representation and inference. In particular, they are used in conjunction with an enhanced Hierarchical Task Network (HTN) domain independent planner to help with performance-based selection of solution steps based on preconditions, effects and postconditions. The planner, in turn, makes use of process models contained in a process library when deliberating on the steps and then consults the capability ontology to retrieve a suitable tool at each step. Two key features of the planner are the ability to support workflow execution (interleaves planning with execution) and can perform in automatic or semi-automatic (interactive) mode. The first feature is highly desirable for video processing problems because execution of image processing steps yield visual results that are intuitive and verifiable by the human user, as automatic validation is non trivial. In the semiautomaticmode, the planner is interactive and prompts the user tomake a tool selection when there is more than one tool available to perform a task. The user makes the tool selection based on the recommended descriptions provided by the workflow system. Once planning is complete, the result of applying the tool of their choice is presented to the user textually and visually for verification. This plays a pivotal role in providing the user with control and the ability to make informed decisions. Hence, the planner extends the capabilities of typical planners by guiding the user to construct more optimal solutions. Video processing problems can also be solved in more modular, reusable and adaptable ways as compared to conventional image processing systems. The integrated approach was evaluated on a test set consisting of videos originating from open sea environment of varying quality. Experiments to evaluate the efficiency, adaptability to user’s changing needs and user learnability of this approach were conducted on users who did not possess image processing expertise. The findings indicate that using this integrated workflow composition and execution method: 1) provides a speed up of over 90% in execution time for video classification tasks using full automatic processing compared to manual methods without loss of accuracy; 2) is more flexible and adaptable in response to changes in user requests (be it in the task, constraints to the task or descriptions of the video) than modifying existing image processing programs when the domain descriptions are altered; 3) assists the user in selecting optimal solutions by providing recommended descriptions.

Master's thesis describes imported functions detection in PE executables, which are from static libraries. Main reason is process automatization and analysis simplification. Detection is solved by searching prepared patterns with missmatch tolerance. Missmatch are caused by changing address during building application. Resulting application supports compiler detection and it contains patterns for MinGW32, Visual studio 2005 and C++ Builder 6.

The document describes developing graphical application with limited size. It describes suitable techniques for a polygonal mesh's compression. The second part is focused on practical usage of this techniques for developing scene in 3D modeling environment and also describes how to transfer this model to the executable file. The work attends to optimalizations of source code compilation and executables compression at the final chapters.