Biblio

Code Hunt (https://www.codehunt.com/) from Microsoft Research is a web-based serious gaming platform being popularly used for various programming contests. In this paper, we demonstrate preliminary statistical analysis of a Code Hunt data set that contains the programs written by students (only) worldwide during a contest over 48 hours. There are 259 users, 24 puzzles (organized into 4 sectors), and about 13,000 programs submitted by these users. Our analysis results can help improve the creation of puzzles in a future contest.

Healthcare technology—sometimes called “healthtech” or “healthsec”—is enmeshed with security and privacy via usability, performance, and cost-effectiveness issues. It is multidisciplinary, distributed, and complex—and it also involves many competing stakeholders and interests. To address the problems that arise in such a multifaceted field—comprised of physicians, IT professionals, management information specialists, computer scientists, edical informaticists, and epidemiologists, to name a few—the Healthtech Declaration was initiated at the most recent USENIX Summit on Information Technologies for Health (Healthtech 2015) held in Washington, DC. This Healthtech Declaration includes an easy-touse—and easy-to-cite—checklist of key issues that anyone proposing a solution must consider (see “The Healthtech Declaration Checklist” sidebar). In this article, we provide the context and motivation for the declaration.

The prevalence of smart devices has promoted the popularity of mobile applications (a.k.a. apps) in recent years. A number of interesting and important questions remain unanswered, such as why a user likes/dislikes an app, how an app becomes popular or eventually perishes, how a user selects apps to install and interacts with them, how frequently an app is used and how much trac it generates, etc. This paper presents an empirical analysis of app usage behaviors collected from millions of users of Wandoujia, a leading Android app marketplace in China. The dataset covers two types of user behaviors of using over 0.2 million Android apps, including (1) app management activities (i.e., installation, updating, and uninstallation) of over 0.8 million unique users and (2) app network trac from over 2 million unique users. We explore multiple aspects of such behavior data and present interesting patterns of app usage. The results provide many useful implications to the developers, users, and disseminators of mobile apps.

In this paper we explore the differential perceptions of cybersecurity professionals and general users regarding access rules and passwords. We conducted a preliminary survey involving 28 participants: 15 cybersecurity professionasl and 13 general users. We present our preliminary findings and explain how such survey data might be used to improve security in practice. We focus on user fatigue with access rules and passwords.

While there have been various studies identifying and classifying Android malware, there is limited discussion of the broader class of apps that fall in a gray area. Mobile grayware is distinct from PC grayware due to differences in operating system properties. Due to mobile grayware’s subjective nature, it is difficult to identify mobile grayware via program analysis alone. Instead, we hypothesize enhancing analysis with text analytics can effectively reduce human effort when triaging grayware. In this paper, we design and implement heuristics for seven main categories of grayware.We then use these heuristics to simulate grayware triage on a large set of apps from Google Play. We then present the results of our empirical study, demonstrating a clear problem of grayware. In doing so, we show how even relatively simple heuristics can quickly triage apps that take advantage of users in an undesirable way.
 

In recent years, online programming and software engineering education via information technology has gained a lot of popularity. Typically, popular courses often have hundreds or thousands of students but only a few course sta members. Tool automation is needed to maintain the quality of education. In this paper, we envision that the capability of quantifying behavioral similarity between programs is helpful for teaching and learning programming and software engineering, and propose three metrics that approximate the computation of behavioral similarity. Speci cally, we leverage random testing and dynamic symbolic execution (DSE) to generate test inputs, and run programs on these test inputs to compute metric values of the behavioral similarity. We evaluate our metrics on three real-world data sets from the Pex4Fun platform (which so far has accumulated more than 1.7 million game-play interactions). The results show that our metrics provide highly accurate approximation to the behavioral similarity. We also demonstrate a number of practical applications of our metrics including hint generation, progress indication, and automatic grading.

Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS mes- sages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security- sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security- sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7% precision and 95% recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses.

Sophistication and flexibility of software development make it easy to leave security vulnerabilities in software applications for attack- ers. It is critical to educate and train software engineers to avoid in- troducing vulnerabilities in software applications in the first place such as adopting secure coding mechanisms and conducting secu- rity testing. A number of websites provide training grounds to train people’s hacking skills, which are highly related to security test- ing skills, and train people’s secure coding skills. However, there exists no interactive gaming platform for instilling gaming aspects into the education and training of secure coding. To address this issue, we propose to construct secure coding duels in Code Hunt, a high-impact serious gaming platform released by Microsoft Re- search. In Code Hunt, a coding duel consists of two code segments: a secret code segment and a player-visible code segment. To solve a coding duel, a player iteratively modifies the player-visible code segment to match the functional behaviors of the secret code seg- ment. During the duel-solving process, the player is given clues as a set of automatically generated test cases to characterize sample functional behaviors of the secret code segment. The game aspect in Code Hunt is to recognize a pattern from the test cases, and to re-engineer the player-visible code segment to exhibit the expected behaviors. Secure coding duels proposed in this work are coding duels that are carefully designed to train players’ secure coding skills, such as sufficient input validation and access control.

In real world domains, from healthcare to power to finance, we deploy computer systems intended to streamline and im- prove the activities of human agents in the corresponding non-cyber worlds. However, talking to actual users (instead of just computer security experts) reveals endemic circum- vention of the computer-embedded rules. Good-intentioned users, trying to get their jobs done, systematically work around security and other controls embedded in their IT systems.

This poster reports on our work compiling a large corpus of such incidents and developing a model based on semi- otic triads to examine security circumvention. This model suggests that mismorphisms—mappings that fail to preserve structure—lie at the heart of circumvention scenarios; dif- ferential perceptions and needs explain users’ actions. We support this claim with empirical data from the corpus.

Agent-based modeling can serve as a valuable asset to security personnel who wish to better understand the security landscape within their organization, especially as it relates to user behavior and circumvention. In this paper, we ar- gue in favor of cognitive behavioral agent-based modeling for usable security, report on our work on developing an agent- based model for a password management scenario, perform a sensitivity analysis, which provides us with valuable insights into improving security (e.g., an organization that wishes to suppress one form of circumvention may want to endorse another), and provide directions for future work.

In real world domains, from healthcare to power to finance, we deploy computer systems intended to streamline and improve the activities of human agents in the corresponding non-cyber worlds. However, talking to actual users (instead of just computer security experts) reveals endemic circumvention of the computer-embedded rules. Good-intentioned users, trying to get their jobs done, systematically work around security and other controls embedded in their IT systems.

This paper reports on our work compiling a large corpus of such incidents and developing a model based on semiotic triads to examine security circumvention. This model suggests that mismorphisms— mappings that fail to preserve structure—lie at the heart of circumvention scenarios; differential percep- tions and needs explain users’ actions. We support this claim with empirical data from the corpus.

Security subsystems are often designed with flawed assumptions arising from system designers' faulty mental models. Designers tend to assume that users behave according to some textbook ideal, and to consider each potential exposure/interface in isolation. However, fieldwork continually shows that even well-intentioned users often depart from this ideal and circumvent controls in order to perform daily work tasks, and that "incorrect" user behaviors can create unexpected links between otherwise "independent" interfaces. When it comes to security features and parameters, designers try to find the choices that optimize security utility–-except these flawed assumptions give rise to an incorrect curve, and lead to choices that actually make security worse, in practice. We propose that improving this situation requires giving designers more accurate models of real user behavior and how it influences aggregate system security. Agent-based modeling can be a fruitful first step here. In this paper, we study a particular instance of this problem, propose user-centric techniques designed to strengthen the security of systems while simultaneously improving the usability of them, and propose further directions of inquiry.