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Full-Text Articles in Engineering
Malware Detection Using Electromagnetic Side-Channel Analysis, Matthew A. Bergstedt
Malware Detection Using Electromagnetic Side-Channel Analysis, Matthew A. Bergstedt
Theses and Dissertations
Many physical systems control or monitor important applications without the capacity to monitor for malware using on-device resources. Thus, it becomes valuable to explore malware detection methods for these systems utilizing external or off-device resources. This research investigates the viability of employing EM SCA to determine whether a performed operation is normal or malicious. A Raspberry Pi 3 was set up as a simulated motor controller with code paths for a normal or malicious operation. While the normal path only calculated the motor speed before updating the motor, the malicious path added a line of code to modify the calculated …
Cyber Data Anomaly Detection Using Autoencoder Neural Networks, Spencer A. Butt
Cyber Data Anomaly Detection Using Autoencoder Neural Networks, Spencer A. Butt
Theses and Dissertations
The Department of Defense requires a secure presence in the cyber domain to successfully execute its stated mission of deterring war and protecting the security of the United States. With potentially millions of logged network events occurring on defended networks daily, a limited staff of cyber analysts require the capability to identify novel network actions for security adjudication. The detection methodology proposed uses an autoencoder neural network optimized via design of experiments for the identification of anomalous network events. Once trained, each logged network event is analyzed by the neural network and assigned an outlier score. The network events with …
Preprocessing Techniques To Support Event Detection Data Fusion On Social Media Data, Brandon T. Davis
Preprocessing Techniques To Support Event Detection Data Fusion On Social Media Data, Brandon T. Davis
Theses and Dissertations
This thesis focuses on collection and preprocessing of streaming social media feeds for metadata as well as the visual and textual information. Today, news media has been the main source of immediate news events, large and small. However, the information conveyed on these news sources is delayed due to the lack of proximity and general knowledge of the event. Such news have started relying on social media sources for initial knowledge of these events. Previous works focused on captured textual data from social media as a data source to detect events. This preprocessing framework postures to facilitate the data fusion …
Autoencoded Reduced Clusters For Anomaly Detection Enrichment (Arcade) In Hyperspectral Imagery, Brenden A. Mclean
Autoencoded Reduced Clusters For Anomaly Detection Enrichment (Arcade) In Hyperspectral Imagery, Brenden A. Mclean
Theses and Dissertations
Anomaly detection in hyper-spectral imagery is a relatively recent and important research area. The shear amount of data available in a many hyper-spectral images makes the utilization of multivariate statistical methods and artificial neural networks ideal for this analysis. Using HYDICE sensor hyper-spectral images, we examine a variety of preprocessing techniques within a framework that allows for changing parameter settings and varying the methodological order of operations in order to enhance detection of anomalies within image data. By examining a variety of different options, we are able to gain significant insight into what makes anomaly detection viable for these images, …