Big Data Analytics at Oklahoma State University
Oklahoma State University
Computer Science Department
Title: Attacks on Privacy-Enhancing Technologies
Description: Even though encryption protects the contents of the communication between a client and a server on the Internet, an adversary can still collect metadata information such as the size of packets and the number of packets sent and received. This type of attack is called a website fingerprinting attack. Participants in this project will collect real web data, parse them using standard statistical tools, and analyze the accuracy of such an attack using machine learning and other statistical techniques.
Title: Adversarial Machine Learning
Description: Big data analytics usually involves machine learning for prediction or analysis. Machine learning requires training data before a model can be built. It is often assumed that this training data is representative of this model. In this project, participants will explore the effect of adversarial/malicious data in the training set on the prediction accuracy of the model built. Possible countermeasures will also be analyzed.
Title: Well Drilling Speed Prediction
Description: A large dataset of drilling speed and drilling attributes (such as rock formation, weight, torque, direction, etc...) are available for analysis. The goal is to accurately predict the drilling speed based on the conditions.
Title: Topic Mining using Microblog Text
Description: Microblogs such as Twitter are platforms widely used by the public. Due to the popularity of the use of these platforms, they have become a platform for dissemination of various types of ideas, opinions, etc. The contents of these platforms are used by researchers in various areas. The objective of our research is to uncover topics and their importance by analyzing texts extracted. Various known tools and algorithms will be used to develop an approach for topic mining and validation. Modules: The research will consist of several steps - data collection, relevance filtering, clustering, topic identification, determining significance. Public domain tools will be used, where possible.
Title: Intelligent scene understanding from imagery and video big data
Description: A computer system's ability to interpret and understand context in visual data is growing rapidly, with the advent of deep learning and access to large-scale imagery sources and repositories. Such an ability is vital for machines that operate in the real world, such as autonomous vehicles. Participants in this project will develop machine learning techniques to recognize objects, actions and relationships in image data.
Questions? Contact: Dr. Christopher Crick (email@example.com)