User Behavioral Modeling of Web-based Systems for Continuous User Authentication

Authentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.

Can Cooling Technology Save Many-Core Parallel Programming from Its Programming Woes?

An abstract of this work will be presented at the Compiler, Architecture and Tools Conference (CATC), Intel Development Center, Haifa, Israel November 23, 2015.

THE PRICE SENSITIVITY OF DEMAND FOR HIGHER EDUCATION AMONG NON-TRADITIONAL STUDENTS

This dissertation examines the price sensitivity of demand for higher education among non-traditional students in the United States. Chapter 1 discusses the issues related to the demand for higher education. It presents the recent trends and reviews the literature addressing these issues. A major conclusion that emerges from this chapter is that the price sensitivity of demand for higher education appears to depend on the source of the variation in price and the characteristics of the students who face the price change. The baseline estimate for the price sensitivity of demand is that a $1,000 (in year 2000 dollars) decrease in tuition costs should result in a 4 percentage-point increase in enrollment for the traditional 18- to 24-year-old student. Chapter 2 examines the price sensitivity of demand for higher education for military spouses resulting from variation in tuition due to military-mandated moves across states. The data suggest that a $1,000 (in year 2000 dollars) decrease in the cost of 2-year schools is associated with a 1--1.5 percentage-point increase in the probability of attending college. This estimate is less than half the previous estimates due to in-state tuition price differences faced by the civilian 18- to 24-year-old population on a percentage-point basis. However, this represents a 7--10 percent increase for this population, and the magnitude of this metric is in line with previous estimates. This suggests tuition assistance can be an effective means of increasing enrollment for military spouses, but other barriers to education for this population may also need to be addressed. Chapter 3 examines the impact of a change in the tax treatment of savings set aside for higher education by those who decide to suspend their education and enter the workforce. The taxation of these funds appears to have increased the rate at which these funds are included in an employee's initial contract and the quantity of funds allocated. These results are counterintuitive if the tax preference was the primary reason for the savings plan. However, these results suggest the rationale for the savings plan was to offer targeted additional compensation to recruits with greater negotiating power. Taxation of funds previously set aside did not appear to have a statistically significant impact on their utilization. Point estimates of the price sensitivity of demand from changes in the out-of-pocket costs for higher education induced by the taxation of these funds were small and often not statistically significant. The results from this dissertation show responses to changes in the net cost of college that differ by the source of price variation and the population experiencing them. This is consistent with the previous literature. This dissertation contributes to the literature by providing estimates for the price sensitivity of demand for higher education to previously understudied non-traditional students.