Identifying the Strengths and Weaknesses of Over-the-Shoulder Attack Resistant Prototypical Graphical Authentication Schemes

Authentication verifies users’ identities to protect against costly attacks. Graphical authentication schemes utilize pictures as passcodes rather than strings of characters. Pictures have been found to be more memorable than the strings of characters used in alphanumeric passwords. However, graphical passcodes have been criticized for being susceptible to Over-the-Shoulder Attacks (OSA). To overcome this concern, many graphical schemes have been designed to be resistant to OSA. Security to this type of attack is accomplished by grouping targets among distractors, translating the selection of targets elsewhere, disguising targets, and using gaze-based input. Prototypical examples of graphical schemes that use these strategies to bolster security against OSAs were directly compared in within-subjects runoffs in studies 1 and 2. The first aim of this research was to discover the current usability limitations of graphical schemes. The data suggested that error rates are a common issue among graphical passcodes attempting to resist OSAs. Studies 3 and 4 investigated the memorability of graphical passcodes when users need to remember multiple passcodes or longer passcodes. Longer passcodes provide advantages to security by protecting against brute force attacks, and multiple passcodes need to be investigated as users need to authenticate for numerous accounts. It was found that participants have strong item retention for passcodes of up to eight images and for up to eight accounts. Also these studies leveraged context to facilitate memorability. Context slightly improved the memorability of graphical passcodes when participants needed to remember credentials for eight accounts. These studies take steps toward understanding the readiness of graphical schemes as an authentication option

Authentication Schemes\u27 Impact on Working Memory

Authentication is the process by which a computing system validates a user’s identity. Although this process is necessary for system security, users view authentication as a frequent disruption to their primary tasks. During this disruption, primary task information must be actively maintained in working memory. As a result, primary task information stored in working memory is at risk of being lost or corrupted while users authenticate. For over two decades, researchers have focused on developing more memorable passwords by replacing alphanumeric text with visual graphics (Biddle et al., 2012). However, very little attention has been given to the impact authentication has on working memory. A recent exploratory study suggests that working memory can be disrupted during graphical authentication (Still & Cain, 2019). In this study, we take the next step by controlling for task difficulty and contrasting performance with conventional password-based authentication. Baddeley’s model was employed to examine the impact of authentication on verbal, visuospatial, and central executive working memory (Baddeley & Hitch, 1974). Our findings may help designers select authentication systems that minimize adverse effects on users’ critical primary task performance. For instance, we revealed that conventional passwords do not have a greater negative impact on verbal primary task information compared to graphical passcodes. We also replicated findings reported by Still and Cain (2019), where visuospatial was least impaired by authentication. These findings are not intuitive, highlighting the need for further investigation of how authentication impacts primary task information in working memory

Towards Baselines for Shoulder Surfing on Mobile Authentication

Given the nature of mobile devices and unlock procedures, unlock authentication is a prime target for credential leaking via shoulder surfing, a form of an observation attack. While the research community has investigated solutions to minimize or prevent the threat of shoulder surfing, our understanding of how the attack performs on current systems is less well studied. In this paper, we describe a large online experiment (n=1173) that works towards establishing a baseline of shoulder surfing vulnerability for current unlock authentication systems. Using controlled video recordings of a victim entering in a set of 4- and 6-length PINs and Android unlock patterns on different phones from different angles, we asked participants to act as attackers, trying to determine the authentication input based on the observation. We find that 6-digit PINs are the most elusive attacking surface where a single observation leads to just 10.8% successful attacks, improving to 26.5\% with multiple observations. As a comparison, 6-length Android patterns, with one observation, suffered 64.2% attack rate and 79.9% with multiple observations. Removing feedback lines for patterns improves security from 35.3\% and 52.1\% for single and multiple observations, respectively. This evidence, as well as other results related to hand position, phone size, and observation angle, suggests the best and worst case scenarios related to shoulder surfing vulnerability which can both help inform users to improve their security choices, as well as establish baselines for researchers.Comment: Will appear in Annual Computer Security Applications Conference (ACSAC

Developing and evaluating a gestural and tactile mobile interface to support user authentication

As awareness grows surrounding the importance of protecting sensitive data, stored on or accessed through a mobile device, a need has been identified to develop authentication schemes which better match the needs of users, and are more resistant to observer attacks. This paper describes the design and evaluation of H4Plock (pronounced “Hap-lock”), a novel authentication mechanism to address the situation. In order to authenticate, the user enters up to four pre-selected on-screen gestures, informed by tactile prompts. The system has been designed in such a way that the sequence of gestures will vary on each authentication attempt, reducing the capability of a shoulder surfer to recreate entry. 94.1% of participants were able to properly authenticate using H4Plock, with 73.3% successfully accessing the system after a gap of five days without rehearsal. Only 23.5% of participants were able to successfully recreate passcodes in a video-based attack scenario, where gestures were unique in design and entered at different locations around the interface

An Approach to Software Development for Continuous Authentication of Smart Wearable Device Users

abstract: With the recent expansion in the use of wearable technology, a large number of users access personal data with these smart devices. The consumer market of wearables includes smartwatches, health and fitness bands, and gesture control armbands. These smart devices enable users to communicate with each other, control other devices, relax and work out more effectively. As part of their functionality, these devices store, transmit, and/or process sensitive user personal data, perhaps biological and location data, making them an abundant source of confidential user information. Thus, prevention of unauthorized access to wearables is necessary. In fact, it is important to effectively authenticate users to prevent intentional misuse or alteration of individual data. Current authentication methods for the legitimate users of smart wearable devices utilize passcodes, and graphical pattern based locks. These methods have the following problems: (1) passcodes can be stolen or copied, (2) they depend on conscious user inputs, which can be undesirable to a user, (3) they authenticate the user only at the beginning of the usage session, and (4) they do not consider user behavior or they do not adapt to evolving user behavior. In this thesis, an approach is presented for developing software for continuous authentication of the legitimate user of a smart wearable device. With this approach, the legitimate user of a smart wearable device can be authenticated based on the user's behavioral biometrics in the form of motion gestures extracted from the embedded sensors of the smart wearable device. The continuous authentication of this approach is accomplished by adapting the authentication to user's gesture pattern changes. This approach is demonstrated by using two comprehensive datasets generated by two research groups, and it is shown that this approach achieves better performance than existing methods.Dissertation/ThesisMasters Thesis Software Engineering 201