Statistical analysis of identity risk of exposure and cost using the ecosystem of identity attributes

Personally Identifiable Information (PII) is often called the "currency of the Internet" as identity assets are collected, shared, sold, and used for almost every transaction on the Internet. PII is used for all types of applications from access control to credit score calculations to targeted advertising. Every market sector relies on PII to know and authenticate their customers and their employees. With so many businesses and government agencies relying on PII to make important decisions and so many people being asked to share personal data, it is critical to better understand the fundamentals of identity to protect it and responsibly use it. Previously developed comprehensive Identity Ecosystem utilizes graphs to model PII assets and their relationships and is powered by empirical data from almost 6,000 real-world identity theft and fraud news reports to populate the UT CID Identity Ecosystem. We analyze UT CID Identity Ecosystem using graph theory and report numerous novel statistics using identity asset content, structure, value, accessibility, and impact. Our work sheds light on how identity is used and paves the way for improving identity protection.Electrical and Computer Engineerin

Exploring Cyberterrorism, Topic Models and Social Networks of Jihadists Dark Web Forums: A Computational Social Science Approach

This three-article dissertation focuses on cyber-related topics on terrorist groups, specifically Jihadists’ use of technology, the application of natural language processing, and social networks in analyzing text data derived from terrorists\u27 Dark Web forums. The first article explores cybercrime and cyberterrorism. As technology progresses, it facilitates new forms of behavior, including tech-related crimes known as cybercrime and cyberterrorism. In this article, I provide an analysis of the problems of cybercrime and cyberterrorism within the field of criminology by reviewing existing literature focusing on (a) the issues in defining terrorism, cybercrime, and cyberterrorism, (b) ways that cybercriminals commit a crime in cyberspace, and (c) ways that cyberterrorists attack critical infrastructure, including computer systems, data, websites, and servers. The second article is a methodological study examining the application of natural language processing computational techniques, specifically latent Dirichlet allocation (LDA) topic models and topic network analysis of text data. I demonstrate the potential of topic models by inductively analyzing large-scale textual data of Jihadist groups and supporters from three Dark Web forums to uncover underlying topics. The Dark Web forums are dedicated to Islam and the Islamic world discussions. Some members of these forums sympathize with and support terrorist organizations. Results indicate that topic modeling can be applied to analyze text data automatically; the most prevalent topic in all forums was religion. Forum members also discussed terrorism and terrorist attacks, supporting the Mujahideen fighters. A few of the discussions were related to relationships and marriages, advice, seeking help, health, food, selling electronics, and identity cards. LDA topic modeling is significant for finding topics from larger corpora such as the Dark Web forums. Implications for counterterrorism include the use of topic modeling in real-time classification and removal of online terrorist content and the monitoring of religious forums, as terrorist groups use religion to justify their goals and recruit in such forums for supporters. The third article builds on the second article, exploring the network structures of terrorist groups on the Dark Web forums. The two Dark Web forums\u27 interaction networks were created, and network properties were measured using social network analysis. A member is considered connected and interacting with other forum members when they post in the same threads forming an interaction network. Results reveal that the network structure is decentralized, sparse, and divided based on topics (religion, terrorism, current events, and relationships) and the members\u27 interests in participating in the threads. As participation in forums is an active process, users tend to select platforms most compatible with their views, forming a subgroup or community. However, some members are essential and influential in the information and resources flow within the networks. The key members frequently posted about religion, terrorism, and relationships in multiple threads. Identifying key members is significant for counterterrorism, as mapping network structures and key users are essential for removing and destabilizing terrorist networks. Taken together, this dissertation applies a computational social science approach to the analysis of cyberterrorism and the use of Dark Web forums by jihadists

Smartphones usage at workplace: Assessing information security risks from accessibility perspective

Innovations in technology have created opportunities for employees to be increasingly efficient, productive and always connected to both internal and external customers as they go about their everyday lives using consumer IT tools and resources. This leads to increasingly employee's use of such resources at hand while performing their routine activities at workplaces due to inherent features of connectivity that allow ease of access to information assets. Building on the significance of effort expectancy (ease of use) in earlier research on smartphone adoption at workplace, this study seeks to examine from the aspect of accessibility (ease of access) as a key feature of smart phone usage. It adapts key constructs of Routine Activity Theory (RAT) in the premises of information systems security, viewing the construct of accessibility (ease of copying/transfer data) as a risk associated with the smartphone usage at workplace. That is, focusing on the probability of convenience (opportunity) as a motivation to commit crime. Through analysis of extant literature and theoretical assertions, it presents a theoretical model that can help identify the relationship between smartphone usage and occurrence of insider fraud incidents in the presence of certain situational stimuli. This study assumes that there are possible implications at workplace in terms of ease of access which a smartphone device provides to an employee allowing them to copy/transfer sensitive information assets conveniently, the practice that may actually increase the occurrence of detrimental security behaviors in the absence of management controls

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Specialization in the identity ecosystem

textCyberspace has dramatically improved our daily lives in the past several decades. Meanwhile, people’s personal identifiable information (PII) is exposed online and is at risk of identity theft and cybercrimes. The Identity Ecosystem developed by the Center for Identity in the University of Texas at Austin addresses this problem and provides a statistical framework for understanding the value, risk and mutual relationships of PII. The Identity Ecosystem currently uses a general Bayesian Network Model to simulate the relationships among PII, which may be quite inaccurate for specific groups of people. This thesis proposes a solution that specializes the Bayesian Network used for particular groups of people. Both one-dimension specialization and multi-dimension specialization are investigated. Research problems like how to choose specialization criterion, how to set specialization boundaries, and how to overcome the difficult of insufficient data, are carefully studied. Specialization functionality is demonstrated based on empirical data. Finally, experiments of specialization are conducted on data obtained from online stories. This work is important in the sense that it provides a guide-line of designing more accurate models of PII within the Identity Ecosystem.Electrical and Computer Engineerin

Threat Modelling and Analysis of Web Application Attacks

There has been a rapid growth in the use of the Internet over the years with billions of businesses using it as a means of communication. The World Wide Web has served as the major tool for disseminating information which has resulted into the development of an architecture used in information sharing between remotely connected clients. A web application is a computer program that operates on web technologies and browsers to carry out assignments over the Internet. In designing a secured web application, it is essential to assess and model the viable threats. Threat Modelling is a process used to improve on the application security by pointing out threats and vulnerabilities, outlining mitigation measures to prevent or eliminate the effect of threats in a system. With the constant increase in the number of attacks on web applications, it has become essential to constantly improve on the existing threat models to increase the level of security posture of web applications for proactiveness and strategic goals in operational and application security. In this thesis, three different threat models; STRIDE, Kill Chain and Attack Tree were simulated and analyzed for SQL injection and Cross Site Scripting attacks using the Microsoft SDL threat modelling tool, Trike modelling tool and SeaMonster modelling tool respectively. This study would be useful for future research in developing a new and more efficient threat model based on the existing ones, it would also help organizations determine which of the models used in this research is best suited for the business’ security framework. The objective of this thesis is to analyze the three commonly used models, examining the strengths and weaknesses discovered during the simulation and compare the performances