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  • 1.
    Diyar, Jamal
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Post-Pruning of Random Forests2018Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Abstract 

    Context. In machine learning, ensemble methods continue to receive increased attention. Since machine learning approaches that generate a single classifier or predictor have shown limited capabilities in some contexts, ensemble methods are used to yield better predictive performance. One of the most interesting and effective ensemble algorithms that have been introduced in recent years is Random Forests. A common approach to ensure that Random Forests can achieve a high predictive accuracy is to use a large number of trees. If the predictive accuracy is to be increased with a higher number of trees, this will result in a more complex model, which may be more difficult to interpret or analyse. In addition, the generation of an increased number of trees results in higher computational power and memory requirements. 

    Objectives. This thesis explores automatic simplification of Random Forest models via post-pruning as a means to reduce the size of the model and increase interpretability while retaining or increasing predictive accuracy. The aim of the thesis is twofold. First, it compares and empirically evaluates a set of state-of-the-art post-pruning techniques on the simplification task. Second, it investigates the trade-off between predictive accuracy and model interpretability. 

    Methods. The primary research method used to conduct this study and to address the research questions is experimentation. All post-pruning techniques are implemented in Python. The Random Forest models are trained, evaluated, and validated on five selected datasets with varying characteristics. 

    Results. There is no significant difference in predictive performance between the compared techniques and none of the studied post-pruning techniques outperforms the other on all included datasets. The experimental results also show that model interpretability is proportional to model accuracy, at least for the studied settings. That is, a positive change in model interpretability is accompanied by a negative change in model accuracy. 

    Conclusions. It is possible to reduce the size of a complex Random Forest model while retaining or improving the predictive accuracy. Moreover, the suitability of a particular post-pruning technique depends on the application area and the amount of training data available. Significantly simplified models may be less accurate than the original model but tend to be perceived as more comprehensible. 

  • 2. Johansson, Fredrik
    Attacking the Manufacturing Execution System: Leveraging a Programmable Logic Controller on the Shop Floor2019Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Background. Automation in production has become a necessity for producing companies to keep up with the demand created by their customers. One way to automate a process is to use a piece of hardware called a programmable logic controller (PLC). A PLC is a small computer capable of being programmed to process a set of inputs, from e.g. sensors, and create outputs, to e.g. actuators, from that. This eliminates the risk of human errors while at the same time speeding up the production rate of the now near identical products. To improve the automation process on the shop floor and the production process in general a special software system is used. This system is known as the manufacturing execution system (MES), and it is connected to the PLCs and other devices on the shop floor. The MES have different functionalities and one of these is that it can manage instructions. Theses instructions can be aimed to both employees and devices such as the PLCs. Would the MES suffer from an error, e.g. in the instructions sent to the shop floor, the company could suffer from a negative impact both economical and in reputation. Since the PLC is a computer and it is connected to the MES it might be possible to attack the system using the PLC as leverage. Objectives. Examine if it is possible to attack the MES using a PLC as the attack origin. Methods. A literature study was performed to see what types of attacks and vulnerabilities that has been disclosed related to PLCs between 2010 and 2018. Secondly a practical experiment was done, trying to perform attacks targeting the MES. Results. The results are that there are many different types of attacks and vulnerabilities that has been found related to PLCs and the attacks done in the practical experiment failed to induce negative effects in the MES used. Conclusions. The conclusion of the thesis is that two identified PLC attack techniques seems likely to be used to attack the MES layer. The methodology that was used to attack the MES layer in the practical experiment failed to affect the MES in a negative way. However, it was possible to affect the log file of the MES in one of the test cases. So, it does not rule out that other MES types are not vulnerable or that the two PLC attacks identified will not work to affect the MES.

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