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Full-Text Articles in Engineering
Improved Study Of Side-Channel Attacks Using Recurrent Neural Networks, Muhammad Abu Naser Rony Chowdhury
Improved Study Of Side-Channel Attacks Using Recurrent Neural Networks, Muhammad Abu Naser Rony Chowdhury
Boise State University Theses and Dissertations
Differential power analysis attacks are special kinds of side-channel attacks where power traces are considered as the side-channel information to launch the attack. These attacks are threatening and significant security issues for modern cryptographic devices such as smart cards, and Point of Sale (POS) machine; because after careful analysis of the power traces, the attacker can break any secured encryption algorithm and can steal sensitive information.
In our work, we study differential power analysis attack using two popular neural networks: Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN). Our work seeks to answer three research questions(RQs):
RQ1: Is it …
Investigating Semantic Properties Of Images Generated From Natural Language Using Neural Networks, Samuel Ward Schrader
Investigating Semantic Properties Of Images Generated From Natural Language Using Neural Networks, Samuel Ward Schrader
Boise State University Theses and Dissertations
This work explores the attributes, properties, and potential uses of generative neural networks within the realm of encoding semantics. It works toward answering the questions of: If one uses generative neural networks to create a picture based on natural language, does the resultant picture encode the text's semantics in a way a computer system can process? Could such a system be more precise than current solutions at detecting, measuring, or comparing semantic properties of generated images, and thus their source text, or their source semantics?
This work is undertaken in the hope that detecting previously unknown properties, or better understanding …
Feature Extraction Using Spiking Convolutional Neural Networks, Ruthvik Vaila, John Chiasson, Vishal Saxena
Feature Extraction Using Spiking Convolutional Neural Networks, Ruthvik Vaila, John Chiasson, Vishal Saxena
Electrical and Computer Engineering Faculty Publications and Presentations
Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. Training deep convolutional neural networks is a memory and power intensive job. Spiking networks could potentially help in reducing the power usage. There is a large pool of tools for one to chose to train artificial neural networks of any size, on the other hand all the available tools to simulate spiking neural networks are geared towards computational neuroscience applications and they are not suitable for real …