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.

CHARACTERIZATION OF POLYUBIQUITIN CHAINS BY MASS SPECTROMETRY

The work outlined in this dissertation will allow biochemists and cellular biologists to characterize polyubiquitin chains involved in their cellular environment by following a facile mass spectrometric based workflow. The characterization of polyubiquitin chains has been of interest since their discovery in 1984. The profound effects of ubiquitination on the movement and processing of cellular proteins depend exclusively on the structures of mono and polyubiquitin modifications anchored or unanchored on the protein within the cellular environment. However, structure-function studies have been hindered by the difficulty in identifying complex chain structures due to limited instrument capabilities of the past. Genetic mutations or reiterative immunoprecipitations have been used previously to characterize the polyubiquitin chains, but their tedium makes it difficult to study a broad ubiquitinome. Top-down and middle-out mass spectral based proteomic studies have been reported for polyubiquitin and have had success in characterizing parts of the chain, but no method to date has been successful at differentiating all theoretical ubiquitin chain isomers (ubiquitin chain lengths from dimer to tetramer alone have 1340 possible isomers). The workflow presented here can identify chain length, topology and linkages present using a chromatographic-time-scale compatible, LC-MS/MS based workflow. To accomplish this feat, the strategy had to exploit the most recent advances in top-down mass spectrometry. This included the most advanced electron transfer dissociation (ETD) activation and sensitivity for large masses from the orbitrap Fusion Lumos. The spectral interpretation had to be done manually with the aid of a graphical interface to assign mass shifts because of a lack of software capable to interpret fragmentation across isopeptide linkages. However, the method outlined can be applied to any mass spectral based system granted it results in extensive fragmentation across the polyubiquitin chain; making this method adaptable to future advances in the field.