Margaret M. Knepper

SENTINEL: a multiple engine information retrieval and visualization system

We describe a prototype Information Retrieval system, SENTINEL, under development at Harris Corporations Information Systems Division. SENTINEL is a fusion of multiple information retrieval technologies, integrating n-grams, a vector space model, and a neural network training rule. One of the primary advantages of SENTINEL is its three-dimenstional visualization capability that is based fully upon the mathematical representation of information within SENTINEL. This three-dimensional visualization capability provides users with an intuitive understanding, with relevance feedback/query refinement techniques that can be better utilized, resulting in higher retrieval accuracy (precision).

A perceptually-relevant model-based cyber threat prediction method for enterprise mission assurance

Cyber attacks remain elusive and are increasingly effective. Information security professionals regularly monitor network resources and cyber security websites with an interest in understanding how such threats expose their enterprises vulnerabilities and dependencies. However, information must be persistently and purposefully examined from a multitude of resources in order to establish context and situational awareness. This in turn, enables organizations to perceive, anticipate and counteract threats before they occur and helps assure their ability to accomplish their missions. Global information must be transformed into timely and local actionable knowledge. To achieve this, cyber event data coupled with knowledge of the semantic interrelationships between other location, object, agent, and event entities need to be factored to facilitate a clearer understanding of the total cyber landscape. In this work, we introduce an ontology driven framework comprising of a dynamic knowledge base, a functional and self-updating mission model, and the associated information and complex event processing capabilities. We focus the description of the system on cyber mission information needs, whereby collection, processing, management and mission model updates are based on cyber-related information from a variety of resources including commercial news, blogs, wikis, and social media sources. The result is a dynamic capability for cyber mission management that provides proactive, on demand cyber information to analysts, professionals, policy makers, and support personnel.

A qualitative and quantitative method for predicting sentiment toward deployed U.S. forces

The ability to automatically predict likelihood of reaction to specific events and situational awareness is important to many military and commercial applications. Gauging population sentiment for targeted response areas and having the ability to predict or control sentiment within these areas is invaluable. Review of reception towards deployed forces must be analyzed, especially in areas vital for U.S. national interests. Predicting population behavior is critical for success and must include a qualitative as well as a quantitative solution. Additionally, a feedback mechanism is needed for periodically updating reception towards presence of U.S. Forces over time. We propose a method for predicting sentiment towards deployed U.S. Forces in near real time, to efficiently propitiate manpower resources, allocate equipment assets, and reduce cost of analyses. Sentiment prediction is becoming an increasingly important and feasible task based on social media, open source data, physical imagery and abundance of video data feeds. Predicting reaction to events can be time consuming. Locating the most likely affected areas is very tedious, requiring much human labor effort, and it is often difficult to obtain the best information on a timely basis. An efficient tool would be helpful to rapidly parse text that has been extracted from an intelligent algorithm in order to evaluate the population sentiment for the targeted area. Multiple data inputs and artificial intelligence (AI) algorithms are required in order to support sound decision making theory. The goal of our system, called GlobalSite, is to deliver trustworthy threat analysis systems and services that understand situations, while being a vital tool for continuing mission operations information.

A Prototype Search Toolkit

Information overload is now a reality. We no longer worry about obtaining a sufficient volume of data; we now are concerned with sifting and understanding the massive volumes of data available to us. To do so, we developed an integrated information processing toolkit that provides the user with a variety of ways to view their information. The views include keyword search results, a domain specific ranking system that allows for adaptively capturing topic vocabularies to customize and focus the search results, navigation pages for browsing, and a geospatial and temporal component to visualize results in time and space, and provide “what if” scenario playing. Integrating the information from different tools and sources gives the user additional information and another way to analyze the data. An example of the integration is illustrated on reports of the avian influenza (bird flu).