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Full-Text Articles in Theory and Algorithms
Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai
Semi-Supervised Spatial-Temporal Feature Learning On Anomaly-Based Network Intrusion Detection, Huy Mai
Computer Science and Computer Engineering Undergraduate Honors Theses
Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn't fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent years, …
Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri
Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri
Computer Science and Computer Engineering Undergraduate Honors Theses
In the modern age of social media and networks, graph representations of real-world phenomena have become incredibly crucial. Often, we are interested in understanding how entities in a graph are interconnected. Graph Neural Networks (GNNs) have proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been …