The latest smartphones have started providing multiple authentication options including PINs, patterns, and passwords (knowledge based), as well as face, fingerprint, iris, and voice identification (biometric-based). In this article, we conducted two user studies to investigate how the convenience and security of unlocking phones are influenced by the provision of multiple authentication options. In a task-based user study with 52 participants, we analyze how participants choose an option to unlock their smartphone in daily life. The user study results demonstrate that providing multiple biometric-based authentication choices does not really influence convenience, because fingerprint had monopolistic dominance in the usage of unlock methods (111 of a total of 115 unlock trials that used a biometric-based authentication factor) due to users’ habitual behavior and fastness in unlocking phones. However, convenience was influenced by the provision of both knowledge-based and biometric-based authentication categories, as biometric-based authentication options were used in combination with knowledge-based authentication options—pattern was another frequently used unlock method. Our findings were confirmed and generalized through a follow-up survey with 327 participants. First, knowledge-based and biometric-based authentication options are used interchangeably. Second, providing multiple authentication options for knowledge-based authentication may influence convenience—both PINs (55.7%) and patterns (39.2%) are quite evenly used. Last, in contrast to knowledge-based authentication, providing multiple authentication choices for biometric-based authentication has less influence on choosing unlock options—fingerprint scanner is the most frequently used option (134 of 187 unlock methods used among biometric-based authentication options).

To understand users' risk perceptions about sharing their PHR on SNS, we first conducted a qualitative user study by interviewing 16 participants. Next, we conducted a large-scale online user study with 497 participants in the U.S. to validate our qualitative results from the first study. Our study results show that a majority of users do not show strong motivation of sharing PHR on SNS due to several concerns such as misuse/abuse and security issues of shared PHR. In particular, participants are highly concerned about sharing diseases and diagnostic test results than other types of PHRs (e.g., details of hospital visits and medical interviews). However, we found about 55.13% of the participants have the experiences of sharing their health-related information on SNS. Also, we learned that users' sharing behavior for disease data can be significantly influenced by the severity/type of the disease as well as preferred recipients. Based on our findings, we propose a recommendation method to automatically determine whether users' posts can be shared with everyone on SNS by analyzing the keywords frequently occurred in health-related posts. Our implementation using Random Forest achieved an F-measure of 87.4%, indicating that PHR can be restrictively shared with high accuracy, when sharing health-related posts on SNS.

Smartwatches support access to a wide range of private information but little is known about the security and usability of existing smartwatch screen lock mechanisms. Prior studies suggest that smartwatch authentication via standard techniques such as 4-digit PINs is challenging and error-prone. We conducted interviews to shed light on current practices, revealing that smartwatch users consider the ten-key keypad required for PIN entry to be hard to use due to its small button sizes. To address this issue, we propose the Personal Identification Chord (PIC), an authentication system based on a four-button chorded keypad that enables users to enter ten different inputs via taps to one or two larger buttons. Two studies assessing usability and security of our technique indicate PICs lead to increases in setup and (modestly) recall time, but can be entered accurately while maintaining high recall rates and may improve guessing entropy compared to PINs.

Establishing secret command and control (C&C) channels from attackers is important in malware design. This paper presents design and analysis of malware architecture exploiting push notification services as C&C channels. The key feature of the push notification-based malware design is remote triggering , which allows attackers to trigger and execute their malware by push notifications. The use of push notification services as covert channels makes it difficult to distinguish this type of malware from other normal applications also using the same services. We implemented a backdoor prototype on Android devices as a proof-of-concept of the push notification-based malware and evaluated its stealthiness and feasibility. Our malware implementation effectively evaded the existing malware analysis tools such as 55 antimalware scanners from VirusTotal and SandDroid. In addition, our backdoor implementation successfully cracked about 98% of all the tested unlock secrets (either PINs or unlock patterns) in 5 seconds with only a fraction (less than 0.01%) of the total power consumption of the device. Finally, we proposed several defense strategies to mitigate push notification-based malware by carefully analyzing its attack process. Our defense strategies include filtering subscription requests for push notifications from suspicious applications, providing centralized management and access control of registration tokens of applications, detecting malicious push messages by analyzing message contents and characteristic patterns demonstrated by malicious push messages, and detecting malware by analyzing the behaviors of applications after receiving push messages.

To improve the security of user-chosen Android screen lock patterns, we propose a novel system-guided pattern lock scheme called "SysPal" that mandates the use of a small number of randomly selected points while selecting a pattern. Users are given the freedom to use those mandated points at any position. We conducted a large-scale online study with 1,717 participants to evaluate the security and usability of three SysPal policies, varying the number of mandatory points that must be used (upon selecting a pattern) from one to three. Our results suggest that the two SysPal policies that mandate the use of one and two points can help users select significantly more secure patterns compared to the current Android policy: 22.58% and 23.19% fewer patterns were cracked. Those two SysPal policies, however, did not show any statistically significant inferiority in pattern recall success rate (the percentage of participants who correctly recalled their pattern after 24 hours). In our lab study, we asked participants to install our screen unlock application on their own Android device, and observed their real-life phone unlock behaviors for a day. Again, our lab study did not show any statistically significant difference in memorability for those two SysPal policies compared to the current Android policy.

This paper presents a new type of online password guessing attack called "WiPING" (Wi-Fi signal-based PIN Guessing attack) to guess a victim's PIN (Personal Identification Number) within a small number of unlock attempts. WiPING uses wireless signal patterns identified from observing sequential finger movements involved in typing a PIN to unlock a mobile device. A list of possible PIN candidates is generated from the wireless signal patterns, and is used to improve performance of PIN guessing attacks. We implemented a proof-of-concept attack to demonstrate the feasibility of WiPING. Our results showed that WiPING could be practically effective: while pure guessing attacks failed to guess all 20 PINs, WiPING successfully guessed two PINs.

Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of mobile-based advertising services where advertisers are charged for their advertisements on mobile applications. In the Android platform, we particularly implement bot programs that can massively generate click events on advertisements on mobile applications and test their feasibility with eight popular advertising networks. Our experimental results show that six advertising networks (75 %) out of eight are vulnerable to our attacks. To mitigate click fraud attacks, we suggest three possible defense mechanisms: (1) filtering out program-generated touch events; (2) identifying click fraud attacks with faked advertisement banners; and (3) detecting anomalous behaviors generated by click fraud attacks. We also discuss why few companies were only willing to deploy such defense mechanisms by examining economic misincentives on the mobile advertising industry.

Smartphone advertisement is increasingly used among many applications and allows developers to obtain revenue through in-app advertising. Our study aims at identifying potential security risks of a type of mobile advertisement where advertisers are charged for their advertisements only when a user clicks (or touches) on the advertisements in their applications. In the Android platform, we design an automated click generation attack and empirically evaluate eight popular advertising networks by performing real attacks on them. Our experimental results show that six advertising networks (75%) out of eight (Millennial Media, App Lovin, Ad Fit, Mdot M, Rev Mob and Cauly Ads) are vulnerable to our attacks. We also discuss how to develop effective defense mechanisms to mitigate such automated click fraud attacks.

We propose an effective pattern lock strength meter to help users choose stronger pattern locks on Android devices. To evaluate the effectiveness of the proposed meter with a real world dataset (i.e., with complete ecological validity), we created an Android application called EnCloud that allows users to encrypt their Dropbox files. 101 pattern locks generated by real EnCloud users were collected and analyzed, where some portion of the users were provided with the meter support. Our statistical analysis indicates that about 10% of the pattern locks that were generated without the meter support could be compromised through just 16 guessing attempts. As for the pattern locks that were generated with the meter support, that number goes up to 48 guessing attempts, showing significant improvement in security. Our recommendation is to implement a strength meter in the next version of Android.

Use of third-party keyboards makes Android more flexible and customizable. However, we demonstrate their potential security risks by implementing a proof-of-concept keylogger that can effectively steal users' sensitive keystrokes with 81 popular websites (out of 100 tested websites). We also empirically analyzed the security behaviors of 139 keyboard applications that were available on Google Play. Our study results show that the majority of existing keyboard applications (84 out of 139) could be potentially misused as malicious keyloggers. To avoid such keylogging attacks, we discuss possible defense mechanisms.