To detect malicious activities in a network, intrusion detection systems are used. Even though these solutions are widely deployed for this purpose they have one serious shortcoming which is the huge amount of false alarms that they are generating. Different measures are taken to tackle this problem such as manually changing the settings of the intrusion detection systems. However, this is an infeasible approach for organisations since a network is changing regularly and specialists that have good knowledge of both the environment and the solution are required. The existing unsupervised approaches cannot be implemented as fully automated solutions because of the need to tune hyper-parameters. Additionally,the implementation of the existing solutions is complicated since often multiple models and the constant updating thereof is required which is a computationally intensive process considering the selected algorithms. In this work, the possibilities to reduce the false alarms in an automated manner are investigated. This is done by applying unsupervised anomaly detection techniques on the resulting alert data to distinguish regular alarms from high priority ones. Real alert data is collected from a network of a large organisation and an additional synthetically generated data set is used to evaluate the proposed approach. Four unsupervised anomaly detection algorithms are chosen to model the regular alerts. These are Local Outlier Factor (LOF), Isolation Forest (IF), Histogram-based Outlier Score(HBOS) and Cluster-based Local Outlier Factor (CBLOF). We show that this approach can greatly reduce the false alarms in real environments. By adding noise to the data we evaluate the performance of the models and propose a method that can be used to determine when the model needs to be retrained. This is done by deriving a metric that is used to trigger the system to automatically retrain on the most recent historic data. This is necessary in order to make the system automated and adaptable to changes in the network.

During this project we were asked to explore the possibilities and limitations of creating a virtual therapist that can help people who suffer from a sleeping disorder. In order to answer this question, the goal of the project was to make such a therapist ourselves.

The first two weeks of the project were spent doing research. We looked into cognitive behavioural therapy and existing eHealth solutions for people with sleeping disorders. We also conducted research into existing chat bots, platforms and languages. At the end of the research phase we concluded that machine learning was the best way to make the agent smart.

During the design phase we stumbled upon the fact that we could not get access to a corpus of conversations between therapists and patients discussing sleep problems. This meant that we could not use machine learning on this corpus, which greatly influenced how smart the agent was going to be. We had to rethink our design choices concerning the artificial intelligence and decided to use a different platform. The personalized artificial intelligence was incorporated in our web application, which communicates with the code via our server.

The testing of the product is based on unit tests, where all modules and functions are tested individually after every new addition. The user interface was tested by setting up use cases. User tests on the final product were also performed. This gave us insight in how people with no knowledge about the system would interact with the system.

The main purpose of this project was to explore the possibilities and limitations for creating a virtual therapy that treats insomnia. Even though the solution we created cannot yet be used as a standalone therapy, it shows a promising glimpse of the many possibilities to conduct virtual therapy. A project group with more time, money and help from a psychologist could greatly improve the project by buying or generating a corpus which can then be used for machine learning and by letting the psychologist model the conversation. The future of virtual therapy has yet to be discovered, but this project already provides a bright outlook.