Using RNA sequence data for predicting patient properties is fairly common by now. In this paper, Variational Auto-Encoders (VAEs) are used to assist in this process. VAEs are a type of neural network seeking to encode data into a smaller dimension called latent space. These latent features are then used to do downstream task analysis such as cancer types, survival time and cancer stages, with the help of a MLP classifier. Furthermore, the training process itself is also analyzed with the usage of UMaps. The purpose of this paper is to compare different VAE models on their effectiveness in providing training data used for the predictions. The predictions mostly consist of guessing when using any of the latent spaces, constructed by the VAE models, as input data for the MLP classifier. The NoVAE model is the only model with slightly better performance when it comes to mean accuracy and standard deviation. The guessing issue is further analyzed with the help of UMaps. The VAEs are able to classify the input data during the training process, but when faced with new data, this end up not being the case. Both the learning rate and β term yield interesting results regarding the modification of the input data and variational property respectively. A lower learning rate leads to better classification, but this is due it deviation less from the original input data. When using a small β term with the β-VAE, the output is similar to that of the VanillaVAE. Meaning the VanillaVAE does not perform better than a regular autoencoder.

CNAME (Canonical Name) cloaking has emerged as a deceptive technique employed by website operators to obfuscate the true origin of their content. This master thesis aims to comprehensively examine the utilization and prevalence of CNAME cloaking across the web. To achieve this, a custom program was developed to crawl websites and gather valuable insights such as cookies and embedded objects. DNS resolutions are performed to identify domains in the resolution chain that exhibit characteristics of cloaking, as per the defined parameters. The thesis leverages diverse datasets to analyze different segments of the web, providing a holistic view of the ecosystem. This study focuses on several key aspects. Firstly, it investigates the most common types of cloakers encountered, shedding light on their prevalence and distribution within the web. Furthermore, the coexistence of Content Delivery Networks (CDNs), trackers, and cloakers is analyzed, providing a comprehensive understanding of their interplay and potential implications. Additionally, the Time-to-Live (TTL) values of cloakers are examined to gain insights into their temporal dynamics and potential strategies employed by operators. By examining the prevalence and dynamics of CNAME cloaking, this research contributes to the broader understanding of this deceptive practice and its implications for privacy, security, and user experience. The findings of this thesis provide valuable insights for policymakers, web administrators, and security professionals to devise effective countermeasures against CNAME cloaking. According to our findings, cloaking tends to occur more frequently on popular websites, indicating a correlation between website popularity and the likelihood of encountering cloaking behavior. Additionally, our analysis reveals that each cloaker tends to target specific types of websites, suggesting a degree of specialization or targeting within the cloaking ecosystem. Moreover, we will delve into the origins and implications of both cookies and embedded objects in the context of cloaking. By examining the relationship between cloaking and these elements, we aim to gain a deeper understanding of the mechanisms and techniques employed by cloakers in their tracking practices.d