The objective of this work is to explore the intrusion detection prob- lem and create simple rules for detecting specific intrusions. The intrusions are explored in the realistic CSE-CIC-IDS2018 dataset. First, the dataset is analyzed by computing appropriate statistics and visualizing the data. In the data visu- alization various dimensionality reduction methods are tested. After analyzing the dataset the data are normalized and prepared for the training. The training process focuses on feature selection and finding the best model for the intrusion detection problem. The feature selection is also used for creating rules. The rules are extracted from an ensemble of Decision Trees. At the end of this work, the rules are compared to the best model. The experiments demonstrate that the simple rules are able to achieve similar results as the best model and can be used in a rule-based intrusion detection system or be deployed as a simple model. 1

Recent advancements in generative models have resulted in the improvement of hyper- realistic synthetic images or "deepfakes" at high resolutions, making them almost indistin- guishable from real images from cameras. While exciting, this technology introduces room for abuse. Deepfakes have already been misused to produce pornography, political propaganda, and misinformation. The ability to produce fully synthetic content that can cause such mis- information demands for robust deepfake detection frameworks. Most deepfake detection methods are trained in a supervised manner, and fail to generalize to deepfakes produced by newer and superior generative models. More importantly, such detection methods are usually focused on detecting deepfakes having a specific type of content, e.g., face deepfakes. How- ever, other types of deepfakes are starting to emerge, e.g., deepfakes of biomedical images, satellite imagery, people, and objects shown in different settings. Taking these challenges into account, we propose NoiseLearner, an unsupervised and content-agnostic deepfake im- age detection method. NoiseLearner aims to detect any deepfake image regardless of the generative model of origin or the content of the image. We perform a comprehensive evalu- ation by testing on multiple deepfake datasets composed of different generative models and different content groups, such as faces, satellite images, landscapes, and animals. Further- more, we include more recent state-of-the-art generative models in our evaluation, such as StyleGAN3 and probabilistic denoising diffusion models (DDPM). We observe that Noise- Learner performs well on multiple datasets, achieving 96% accuracy on both StyleGAN and StyleGAN2 datasets. / Master of Science / Images synthesized by artificial intelligence, commonly known as deepfakes, are starting to become indistinguishable from real images. While these technological advances are exciting with regards to what a computer can do, it is important to understand that such technol- ogy is currently being used with ill intent. Thus, identifying these images is becoming a growing necessity, especially as deepfake technology grows to perfectly mimic the nature of real images. Current deepfake detection approaches fail to detect deepfakes of other content, such as sattelite imagery or X-rays, and cannot generalize to deepfakes synthesized by new artificial intelligence. Taking these concerns into account, we propose NoiseLearner, a deep- fake detection method that can detect any deepfake regardless of the content and artificial intelligence model used to synthesize it. The key idea behind NoiseLearner is that it does not require any deepfakes to train. Instead, NoiseLearner learns the key features of real images and uses them to differentiate between deepfakes and real images – without ever looking at a single deepfake. Even with this strong constraint, NoiseLearner shows promise by detecting deepfakes of diverse contents and models used to generate them. We also explore different ways to improve NoiseLearner.