Projects

Modern computer network defense systems rely primarily on signature-based intrusion detection tools, which generate alerts when patterns that are pre-determined to be malicious are encountered in network data streams. Signatures are created reactively, and only after manual analysis of a network intrusion. There is no ability to detect intrusions that are new, or variants of an existing attack. There is no ability to adapt the detectors to the patterns unique to a network environment.

The Oak Ridge Cyber Analytics (ORCA) Attack Variant Detector (AVD) is a sensor that uses machine learning technology to analyze behaviors in channels of communication between individual computers. Using examples of attack and non-attack traffic in the target environment, the ORCA sensor is trained to recognize and discriminate between malicious and normal traffic types. The machine learning provides an insight that would be difficult for a human to explicitly code as a signature because it evaluates many interdependent metrics simultaneously.

Big data demands the need for intelligent, recommender agents that can enhance a person’s situational or domain awareness of their environment. The ability to have a keen awareness and availability of relevant information provides a critical competitive edge. Unfortunately, there is simply too much data streaming too quickly for a person to manually process, analyze, and take action within a reasonable amount of time. In an attempt to alleviate this challenge, many people subscribe to relevant Internet information. There may be forms of subscriptions with the most common being Really Simple Syndication (RSS), blogs, even Facebook and Twitter. The concept is simple, when new information is posted to the site; a subscriber sees a list of this new information. The subscriber then has the option of following a link to read more. This approach is a very useful and successful model for monitoring this data, but it does have some significant drawbacks. In practice, the feeds of new information become quite lengthy, and contain more information than can be practically read. Furthermore, there can be a significant number of items that have little interest to the subscriber. Thus, the ability to find new and relevant information proves critical. We have developed a content-based recommender system that addresses both of these problems. The flexibility of input allows the system to be adaptable to industry and government use cases and data sets such as news feeds, resumes, proposal requests, etc.

The Verification, Validation and Uncertainty Quantification (VVUQ) for machine learning project identified processes and techniques to conduct VVUQ on machine learning applications.

ORNL has played a key role in developing novel Big Data toolkits in the context of syndromic disease surveillance. Our platform, the Oak Ridge Bio-surveillance Toolkit (ORBiT) enables large-scale analysis of heterogeneous data sources, including environmental, climate/weather related data, prescriptions records and other novel data streams emerging from social media (e.g., Twitter, Instagram). ORBiT is targeted at developing novel statistical and machine learning tools instead of acting as a central data collection interface from these heterogeneous resources. Additionally, it also provides an application programming interface (API) that can be used by end-users to target specific bio-surveillance applications. Machine learning tools are tightly integrated with visualization tools in a web-based framework to aid the end users or analysts in exploring potential links between heterogeneous data sets, detecting patterns/correlations across multiple data streams, identifying emerging disease outbreaks, forecasting emerging epidemics, and monitoring control strategies. ORBiT is implemented as a component-based, plug-and-play toolkit that exploits existing distributed cloud-based analytics frameworks.

The goal of the proposed work is to bring powerful, flexible analytics to the analysts’ fingertips.

This project develops a multi-scale anomaly detection algorithm for time-varying graph data.

Entangled photons possess many strange properties which make them invaluable tools in physics. This project seeks to push their entanglement to fundamental limits for new applications in quantum information processing.

Nonlinear interferometers, which use parametric amplifiers in place of beam splitters, can improve the signal to noise ratio of interferometric sensors by a factor of twice the power gain. Recently ORNL has realized a novel, inherently stable, nonlinear interferometer using nonlinear rubidium (Rb) vapor. This approach reduces the complexity and the size, weight and power requirements (SWAP) of earlier demonstrations. However, it is still constructed using bulk, free-space optics on a lab table. This project seeks to realize a reduced SWAP further and perform measurements to quantify its performance relative to other approaches.

We propose an entirely new experimental photonic qubit interface which will enable quantum connections between common material qubits such as ions or atoms.

Scientific facilities and organizations constantly report and evaluate performance and impact based on publication metrics. One of the major problems with this process is that many facilities do not have an effective method of finding, tracking, or managing their publications. COBRA is a publication discovery and management system designed to automate the discovery of publications while providing a management solution and adding efficiency to the manual processes that are still necessary. The COBRA publication discovery and management system is the culmination of several years worth of work. It builds upon previous methods of publication discovery and adds a complete management system and infrastructure.