Determining Unique Agents by Evaluating Web Form Interaction

Because of the inherent risks in today’s online activities, it becomes imperative to identify a malicious user masquerading as someone else. Incorporating biometric analysis enhances the confidence of authenticating valid users over the Internet while providing additional layers of security with no hindrance to the end user. Through the analysis of traffic patterns and HTTP Header analysis, the detection and early refusal of robot agents plays a great role in reducing fraudulent login attempts

Enhancing Usability and Security through Alternative Authentication Methods

With the expanding popularity of various Internet services, online users have be- come more vulnerable to malicious attacks as more of their private information is accessible on the Internet. The primary defense protecting private information is user authentication, which currently relies on less than ideal methods such as text passwords and PIN numbers. Alternative methods such as graphical passwords and behavioral biometrics have been proposed, but with too many limitations to replace current methods. However, with enhancements to overcome these limitations and harden existing methods, alternative authentications may become viable for future use. This dissertation aims to enhance the viability of alternative authentication systems. In particular, our research focuses on graphical passwords, biometrics that depend, directly or indirectly, on anthropometric data, and user authentication en- hancements using touch screen features on mobile devices. In the study of graphical passwords, we develop a new cued-recall graphical pass- word system called GridMap by exploring (1) the use of grids with variable input entered through the keyboard, and (2) the use of maps as background images. as a result, GridMap is able to achieve high key space and resistance to shoulder surfing attacks. to validate the efficacy of GridMap in practice, we conduct a user study with 50 participants. Our experimental results show that GridMap works well in domains in which a user logs in on a regular basis, and provides a memorability benefit if the chosen map has a personal significance to the user. In the study of anthropometric based biometrics through the use of mouse dy- namics, we present a method for choosing metrics based on empirical evidence of natural difference in the genders. In particular, we develop a novel gender classifi- cation model and evaluate the model’s accuracy based on the data collected from a group of 94 users. Temporal, spatial, and accuracy metrics are recorded from kine- matic and spatial analyses of 256 mouse movements performed by each user. The effectiveness of our model is validated through the use of binary logistic regressions. Finally, we propose enhanced authentication schemes through redesigned input, along with the use of anthropometric biometrics on mobile devices. We design a novel scheme called Triple Touch PIN (TTP) that improves traditional PIN number based authentication with highly enlarged keyspace. We evaluate TTP on a group of 25 participants. Our evaluation results show that TTP is robust against dictio- nary attacks and achieves usability at acceptable levels for users. We also assess anthropometric based biometrics by attempting to differentiate user fingers through the readings of the sensors in the touch screen. We validate the viability of this biometric approach on 33 users, and observe that it is feasible for distinguishing the fingers with the largest anthropometric differences, the thumb and pinkie fingers

Towards Usable End-user Authentication

Authentication is the process of validating the identity of an entity, e.g., a person, a machine, etc.; the entity usually provides a proof of identity in order to be authenticated. When the entity - to be authenticated - is a human, the authentication process is called end-user authentication. Making an end-user authentication usable entails making it easy for a human to obtain, manage, and input the proof of identity in a secure manner. In machine-to-machine authentication, both ends have comparable memory and computational power to securely carry out the authentication process using cryptographic primitives and protocols. On the contrary, as a human has limited memory and computational power, in end-user authentication, cryptography is of little use. Although password based end-user authentication has many well-known security and usability problems, it is the de facto standard. Almost half a century of research effort has produced a multitude of end-user authentication methods more sophisticated than passwords; yet, none has come close to replacing passwords. In this dissertation, taking advantage of the built-in sensing capability of smartphones, we propose an end-user authentication framework for smartphones - called ePet - which does not require any active participation from the user most of the times; thus the proposed framework is highly usable. Using data collected from subjects, we validate a part of the authentication framework for the Android platform. For web authentication, in this dissertation, we propose a novel password creation interface, which helps a user remember a newly created password with more confidence - by allowing her to perform various memory tasks built upon her new password. Declarative and motor memory help the user remember and efficiently input a password. From a within-subjects study we show that declarative memory is sufficient for passwords; motor memory mostly facilitate the input process and thus the memory tasks have been designed to help cement the declarative memory for a newly created password. This dissertation concludes with an evaluation of the increased usability of the proposed interface through a between-subjects study

Secure Arcade: A Gamified Defense Against Cyber Attacks

In modernity, we continually receive increasingly intricate technologies that allow us to increase our lives convenience and efficiency. Our technology, particularly technology available over the internet, is advancing at unprecedented speed. However, this speed of advancement allows those behind malicious attacks to have an increasingly easier time taking advantage of those who know little about computer security. Unfortunately, education in the computer security field is generally limited only to tertiary education. This research addresses this problem through a gamified web-based application that drives users to reach learning goals to help them become more vigilant internet users: 1. Learn and memorize general computer security terminology, 2. Become familiar with basic cryptography concepts, 3. Learn to recognize potential phishing scams via email quickly, and 4. Learn common attacks on servers and how to deal with them

Evaluation of Real-World Risk-Based Authentication at Online Services Revisited: Complexity Wins

Risk-based authentication (RBA) aims to protect end-users against attacks involving stolen or otherwise guessed passwords without requiring a second authentication method all the time. Online services typically set limits on what is still seen as normal and what is not, as well as the actions taken afterward. Consequently, RBA monitors different features, such as geolocation and device during login. If the features' values differ from the expected values, then a second authentication method might be requested. However, only a few online services publish information about how their systems work. This hinders not only RBA research but also its development and adoption in organizations. In order to understand how the RBA systems online services operate, black box testing is applied. To verify the results, we re-evaluate the three large providers: Google, Amazon, and Facebook. Based on our test setup and the test cases, we notice differences in RBA based on account creation at Google. Additionally, several test cases rarely trigger the RBA system. Our results provide new insights into RBA systems and raise several questions for future work

Learning Assigned Secrets for Unlocking Mobile Devices

ABSTRACT Nearly all smartphones and tablets support unlocking with a short user-chosen secret: e.g., a numeric PIN or a pattern. To address users' tendency to choose guessable PINs and patterns, we compare two approaches for helping users learn assigned random secrets. In one approach, built on our prior work [16], we assign users a second numeric PIN and, during each login, we require them to enter it after their chosen PIN. In a new approach, we re-arrange the digits on the keypad so that the user's chosen PIN appears on an assigned random sequence of key positions. We performed experiments with over a thousand participants to compare these two repetition-learning approaches to simple user-chosen PINs and assigned PINs that users are required to learn immediately at account set-up time. Almost all of the participants using either repetition-learning approach learned their assigned secrets quickly and could recall them three days after the study. Those using the new mapping approach were less likely to write down their secret. Surprisingly, the learning process was less time consuming for those required to enter an extra PIN

Learning Assigned Secrets for Unlocking Mobile Devices

ABSTRACT Nearly all smartphones and tablets support unlocking with a short user-chosen secret: e.g., a numeric PIN or a pattern. To address users' tendency to choose guessable PINs and patterns, we compare two approaches for helping users learn assigned random secrets. In one approach, built on our prior work [16], we assign users a second numeric PIN and, during each login, we require them to enter it after their chosen PIN. In a new approach, we re-arrange the digits on the keypad so that the user's chosen PIN appears on an assigned random sequence of key positions. We performed experiments with over a thousand participants to compare these two repetition-learning approaches to simple user-chosen PINs and assigned PINs that users are required to learn immediately at account set-up time. Almost all of the participants using either repetition-learning approach learned their assigned secrets quickly and could recall them three days after the study. Those using the new mapping approach were less likely to write down their secret. Surprisingly, the learning process was less time consuming for those required to enter an extra PIN

On the Usability of Next-Generation Authentication: A Study on Eye Movement and Brainwave-based Mechanisms

Passwords remain a widely-used authentication mechanism, despite their well-known security and usability limitations. To improve on this situation, next-generation authentication mechanisms, based on behavioral biometric factors such as eye movement and brainwave have emerged. However, their usability remains relatively under-explored. To fill this gap, we conducted an empirical user study (n=32 participants) to evaluate three brain-based and three eye-based authentication mechanisms, using both qualitative and quantitative methods. Our findings show good overall usability according to the System Usability Scale for both categories of mechanisms, with average SUS scores in the range of 78.6-79.6 and the best mechanisms rated with an "excellent" score. Participants particularly identified brainwave authentication as more secure yet more privacy-invasive and effort-intensive compared to eye movement authentication. However, the significant number of neutral responses indicates participants' need for more detailed information about the security and privacy implications of these authentication methods. Building on the collected evidence, we identify three key areas for improvement: privacy, authentication interface design, and verification time. We offer recommendations for designers and developers to improve the usability and security of next-generation authentication mechanisms

Toward A Secure Account Recovery: Machine Learning Based User Modeling for protection of Account Recovery in a Managed Environment

As a result of our heavy reliance on internet usage and running online transactions, authentication has become a routine part of our daily lives. So, what happens when we lose or cannot use our digital credentials? Can we securely recover our accounts? How do we ensure it is the genuine user that is attempting a recovery while at the same time not introducing too much friction for the user? In this dissertation, we present research results demonstrating that account recovery is a growing need for users as they increase their online activity and use different authentication factors. We highlight that the account recovery process is the weakest link in the authentication domain because it is vulnerable to account takeover attacks because of the less secure fallback authentication mechanisms usually used. To close this gap, we study user behavior-based machine learning (ML) modeling as a critical part of the account recovery process. The primary threat model for ML implementation in the context of authentication is poisoning and evasion attacks. Towards that end, we research randomized modeling techniques and present the most effective randomization strategy in the context of user behavioral biometrics modeling for account recovery authentication. We found that a randomization strategy that exclusively relied on the user’s data, such as stochastically varying the features used to generate an ensemble of models, outperformed a design that incorporated external data, such as adding gaussian noise to outputs. This dissertation asserts that account recovery process security posture can be vastly improved by incorporating user behavior modeling to add resiliency against account takeover attacks and nudging users towards voluntary adoption of more robust authentication factors