Deep Learning for Big Data Analytics in High-Performance Computing Environments

Deep Learning (DL) has been showing huge success for analysis the big data problem. However, this large scale implementation of deep learning algorithms for Big Data analytics requires huge computing resources, leading to a high power requirement and communication overhead. Recently, IBM has developed a new non von Neumann architecture called TrueNorth Cognitive System which allows for a new direction of research of in the neuromorphic computing. We have implemented deep learning approach with different optimizer on the IBM’s TrueNorth system using Caffe, Tea and Corelet Programming Environment (CPE-2.1) which is experimented on MNIST dataset. The experimental results are analyzed for different optimization functions. In addition, we also implemented Intrusion detection for cyber security which being considered another big data problem. The experimental results show promising recognition accuracy for anomaly detection and classification.https://ecommons.udayton.edu/stander_posters/2075/thumbnail.jp

The Department of Electrical and Computer Engineering Newsletter

Summer 2017 News and notes for University of Dayton\u27s Department of Electrical and Computer Engineering.https://ecommons.udayton.edu/ece_newsletter/1010/thumbnail.jp

Machine Learning for Cyberattack Detection

Machine learning has become rapidly utilized in cybersecurity, rising from almost non-existent to currently over half of cybersecurity techniques utilized commercially. Machine learning is advancing at a rapid rate, and the application of new learning techniques to cybersecurity have not been investigate yet. Current technology trends have led to an abundance of household items containing microprocessors all connected within a private network. Thus, network intrusion detection is essential for keeping these networks secure. However, network intrusion detection can be extremely taxing on battery operated devices. The presented work presents a cyberattack detection system based on a multilayer perceptron neural network algorithm. To show that this system can operate at low power, the algorithm was executed on two commercially available minicomputer systems including the Raspberry PI 3 and the Asus Tinkerboard. An analysis of accuracy, power, energy, and timing was performed to study the tradeoffs necessary when executing these algorithms at low power. Our results show that these low power implementations are feasible, and a scan rate of more than 226,000 packets per second can be achieved from a system that requires approximately 5W to operate with greater than 99% accuracy

A Study on Sequential Classifiers Combination using Supervised Machine Learning Approach for Intrusion Detection System

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