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Towards An Adaptable System-based Classification Design for Cyber Identity


Mary (Kay) Michel, Michael King, IEEE Cyber Science/SA UK 2018, Scotland, UK

Abstract— As cybercrime activity continues to increase with significant data growth and the Internet of Things (IoT’s), this research introduces a new proactive methodically designed approach vs. current reactive and specialized methods.  A novel holistic identity classification scheme and information architecture is proposed. This approach has an adaptive, common cybernetic trait design to support a changing technological landscape and human behavior. Common cyber identity base trait dimensions for context, physical, cyber, and human aspects allow for systematic analysis of temporal evidence to help resolve a physical person’s identity in a cybercrime. This research platform supports both broad and targeted identity analytics utilizing advanced machine learning methods with mixed media visualizations to facilitate Cyber Situational Awareness (SA). Early PhD experimentation with real-world use cases shows promise with regard to providing salient attributes and patterns of cyber activity that are unique to a person.

Cyber Identity: Salient Trait Ontology and Computational Framework to Aid in Solving Cybercrime


Mary (Kay) Michel, Marco Carvalho, Heather Crawford, Albert C. Esterline, IEEE TrustCom 2018.

Abstract— Cyber forensics is challenging due to the lack of defined holistic features with a ground truth identity core, and scalable systematic methods to credibly link a person’s physical and cyber attributes in a complex networked environment. Cybercrime continues to grow as humans conduct more online activities that generate sensitive data while connected to anyone around the world.  In this work, we propose a new classification-based ontology and computational framework for resolving an identity based on cyber activities. Our ontology and framework extend legal case situational theory research to temporally map cyber and physical categorical traits. Initial experimentation based on real-world legal cases reveals contextual salient traits that are most effective in linking evidence to a person’s profile or unique identity. As a result, these multi-dimensional traits support innovative visualizations that depict a person’s linkable identity core, digital artifacts, security, and technology. The impact of our ontology and framework design is to support solving cybercrime by aiding in identity resolution.

Categorization of Discoverable Cyber Attributes for Identity Protection, Privacy, and Analytics


Mary (Kay) Michel, Michael King, IEEE SoutheastCon, April 19, 2018.


Abstract— The Internet has become a major source of data that many regard as personally identifiable, and the ease of accessibility may be considered as an invasion of privacy.  While there are certainly benign uses of this data, it has also facilitated increases in identity theft and identity fraud. This paper presents classes of cyber identity attributes that can aid in analysis and protection of a person’s sensitive data in a complex, changing environment. Our research is motivated by the need to understand and organize identity attributes in such a way as to inform the general public of what Personally Identifiable Information (PII) is available and how it may be better protected. In this paper, we outline and discuss five major categories of identity attributes that are discoverable online. The categories discussed relate to data that is biographic, behavioral, relationship, biometric, and physiological data which are all part of a holistic representational model for identity analytics.

Cyber Biometrics: The Face and Text Profiler 


Kay Michel, Chris Moffatt, and Liam Mayron, IEEE MILCOM 2012 Classified session, October 29, 2012.

Abstract— This paper presents a novel neural network text gender classification method. Also included are evaluations of existing facial recognition algorithms that can be used to provide a higher probability of correct gender identification when coupled with our text profiler algorithm. Test results are provided showing experimental trials of our neural network technique to identify if the author of an online text quote was male or female based on the most effective researched psycholinguistic text attributes.

Cognitive Cyber Situational Awareness Using Virtual Worlds


Kay Michel, Nathan Helmick, and Liam Mayron, IEEE CogSIMA, February 22, 2011.

Abstract—The use of network data visualization tools for cyber security is particularly challenging when large amounts of diverse data are displayed and continuously updated. These real-time changes can be used for context-sensitive decision making, but are impeded by a lack of expressive visualization techniques.  In this work, we propose a new method for the visualization of network traffic - virtual worlds. These three-dimensional, immersive environments allow the representation of data and metrics within an expressive environment intuitive to many users. Furthermore, they provide a unique medium for users to collaborate in identifying and isolating security vulnerabilities. We provide a description of the system for situational awareness and proposed experiment involving cognitive processes of human vision, perception and action.