ChimpCheck: Property-Based Randomized Test Generation for Interactive Apps

We consider the problem of generating relevant execution traces to test rich interactive applications. Rich interactive applications, such as apps on mobile platforms, are complex stateful and often distributed systems where sufficiently exercising the app with user-interaction (UI) event sequences to expose defects is both hard and time-consuming. In particular, there is a fundamental tension between brute-force random UI exercising tools, which are fully-automated but offer low relevance, and UI test scripts, which are manual but offer high relevance. In this paper, we consider a middle way---enabling a seamless fusion of scripted and randomized UI testing. This fusion is prototyped in a testing tool called ChimpCheck for programming, generating, and executing property-based randomized test cases for Android apps. Our approach realizes this fusion by offering a high-level, embedded domain-specific language for defining custom generators of simulated user-interaction event sequences. What follows is a combinator library built on industrial strength frameworks for property-based testing (ScalaCheck) and Android testing (Android JUnit and Espresso) to implement property-based randomized testing for Android development. Driven by real, reported issues in open source Android apps, we show, through case studies, how ChimpCheck enables expressing effective testing patterns in a compact manner.Comment: 20 pages, 21 figures, Symposium on New ideas, New Paradigms, and Reflections on Programming and Software (Onward!2017

FINE-GRAINED ACCESS CONTROL ON ANDROID COMPONENT

The pervasiveness of Android devices in today’s interconnected world emphasizes the importance of mobile security in protecting user privacy and digital assets. Android’s current security model primarily enforces application-level mechanisms, which fail to address component-level (e.g., Activity, Service, and Content Provider) security concerns. Consequently, third-party code may exploit an application’s permissions, and security features like MDM or BYOD face limitations in their implementation. To address these concerns, we propose a novel Android component context-aware access control mechanism that enforces layered security at multiple Exception Levels (ELs), including EL0, EL1, and EL3. This approach effectively restricts component privileges and controls resource access as needed. Our solution comprises Flasa at EL0, extending SELinux policies for inter-component interactions and SQLite content control; Compac, spanning EL0 and EL1, which enforces component-level permission controls through Android runtime and kernel modifications; and TzNfc, leveraging TrustZone technologies to secure third-party services and limit system privileges via Trusted Execution Environment (TEE). Our evaluations demonstrate the effectiveness of our proposed solution in containing component privileges, controlling inter-component interactions and protecting component level resource access. This enhanced solution, complementing Android’s existing security architecture, provides a more comprehensive approach to Android security, benefiting users, developers, and the broader mobile ecosystem

User Behavior-Based Implicit Authentication

In this work, we proposed dynamic retraining (RU), wind vane module (WVM), BubbleMap (BMap), and reinforcement authentication (RA) to improve the efficacy of implicit authentication (IA). Motivated by the great potential of implicit and seamless user authentication, we have built an implicit authentication system with adaptive sampling that automatically selects dynamic sets of activities for user behavior extraction. Various activities, such as user location, application usage, user motion, and battery usage have been popular choices to generate behaviors, the soft biometrics, for implicit authentication. Unlike password-based or hard biometric-based authentication, implicit authentication does not require explicit user action or expensive hardware. However, user behaviors can change unpredictably, which renders it more challenging to develop systems that depend on them. In addition to dynamic behavior extraction, the proposed implicit authentication system differs from the existing systems in terms of energy efficiency for battery-powered mobile devices. Since implicit authentication systems rely on machine learning, the expensive training process needs to be outsourced to the remote server. However, mobile devices may not always have reliable network connections to send real-time data to the server for training. In addition, IA systems are still at their infancy and exhibit many limitations, one of which is how to determine the best retraining frequency when updating the user behavior model. Another limitation is how to gracefully degrade user privilege when authentication fails to identify legitimate users (i.e., false negatives) for a practical IA system.To address the retraining problem, we proposed an algorithm that utilizes Jensen-Shannon (JS)-dis(tance) to determine the optimal retraining frequency, which is discussed in Chapter 2. We overcame the limitation of traditional IA by proposing a W-layer, an overlay that provides a practical and energy-efficient solution for implicit authentication on mobile devices. The W-layer is discussed in Chapter 3 and 4. In Chapter 5, a novel privilege-control mechanism, BubbleMap (BMap), is introduced to provide fine-grained privileges to users based on their behavioral scores. In the same chapter, we describe reinforcement authentication (RA) to achieve a more reliable authentication

DR BACA: dynamic role based access control for Android

Thesis (M.S.)--Boston UniversityAndroid, as an open platform, dominates the booming mobile market. However, its permission mechanism is inflexible and often results in over-privileged applications. This in turn creates severe security issues. Aiming to support the Principle of Least Privilege, we propose a Dynamic Role Based Access Control for Android (DR BACA) model and implement the DR BACA system to address these problems. Our system offers multi-user management on Android mobile devices, comparable to traditional workstations, and provides fine-grained Role Based Access Control (RBAC) to en- hance Android security at both the application and permission level. Moreover, by leveraging context-aware capabilities of mobile devices and Near Field communication (NFC) technology, our solution supports dynamic RBAC that provides more flexible access control while still being able to mitigate some of the most serious security risks on mobile devices. The DR BACA system is highly scalable, suitable for both end- users and large business environments. It simplifies configuration and management of Android devices and can help enterprises to deal with security issues by implementing a uniform security policy. We show that our DR BACA system can be deployed and used with eet:se. With a proper security policy, our evaluation shows that DR BACA can effectively mitigate the security risks posed by both malicious and vulnerable non-malicious applications while incurring only a small overall system overhead

Program Analysis Based Approaches to Ensure Security and Safety of Emerging Software Platforms

Our smartphones, homes, hospitals, and automobiles are being enhanced with software that provide an unprecedentedly rich set of functionalities, which has created an enormous market for the development of software that run on almost every personal computing devices in a person's daily life, including security- and safety-critical ones. However, the software development support provided by the emerging platforms also raises security risks by allowing untrusted third-party code, which can potentially be buggy, vulnerable or even malicious to control user's device. Moreover, as the Internet-of-Things (IoT) technology is gaining vast adoptions by a wide range of industries, and is penetrating every aspects of people's life, safety risks brought by the open software development support of the emerging IoT platform (e.g., smart home) could bring more severe threat to the well-being of customers than what security vulnerabilities in mobile apps have done to a cell phone user. To address this challenge posed on the software security in emerging domains, my dissertation focuses on the flaws, vulnerabilities and malice in the software developed for platforms in these domains. Specifically, we demonstrate that systematic program analyses of software (1) Lead to an understanding of design and implementation flaws across different platforms that can be leveraged in miscellaneous attacks or causing safety problems; (2) Lead to the development of security mechanisms that limit the potential for these threats.We contribute static and dynamic program analysis techniques for three modern platforms in emerging domains -- smartphone, smart home, and autonomous vehicle. Our app analysis reveals various different vulnerabilities and design flaws on these platforms, and we propose (1) static analysis tool OPAnalyzer to automates the discovery of problems by searching for vulnerable code patterns; (2) dynamic testing tool AutoFuzzer to efficiently produce and capture domain specific issues that are previously undefined; and (3) propose new access control mechanism ContexIoT to strengthen the platform's immunity to the vulnerability and malice in third-party software. Concretely, we first study a vulnerability family caused by the open ports on mobile devices, which allows remote exploitation due to insufficient protection. We devise a tool called OPAnalyzer to perform the first systematic study of open port usage and their security implications on mobile platform, which effectively identify and characterize vulnerable open port usage at scale in popular Android apps. We further identify the lack of context-based access control as a main enabler for such attacks, and begin to seek for defense solution to strengthen the system security. We study the popular smart home platform, and find the existing access control mechanisms to be coarse-grand, insufficient, and undemanding. Taking lessons from previous permission systems, we propose the ContexIoT approach, a context-based permission system for IoT platform that supports third-party app development, which protects the user from vulnerability and malice in these apps through fine-grained identification of context. Finally, we design dynamic fuzzing tool, AutoFuzzer for the testing of self-driving functionalities, which demand very high code quality using improved testing practice combining the state-of-the-art fuzzing techniques with vehicular domain knowledge, and discover problems that lead to crashes in safety-critical software on emerging autonomous vehicle platform.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145845/1/jackjia_1.pd

Effective Identity Management on Mobile Devices Using Multi-Sensor Measurements

Due to the dramatic increase in popularity of mobile devices in the past decade, sensitive user information is stored and accessed on these devices every day. Securing sensitive data stored and accessed from mobile devices, makes user-identity management a problem of paramount importance. The tension between security and usability renders the task of user-identity verification on mobile devices challenging. Meanwhile, an appropriate identity management approach is missing since most existing technologies for user-identity verification are either one-shot user verification or only work in restricted controlled environments. To solve the aforementioned problems, we investigated and sought approaches from the sensor data generated by human-mobile interactions. The data are collected from the on-board sensors, including voice data from microphone, acceleration data from accelerometer, angular acceleration data from gyroscope, magnetic force data from magnetometer, and multi-touch gesture input data from touchscreen. We studied the feasibility of extracting biometric and behaviour features from the on-board sensor data and how to efficiently employ the features extracted to perform user-identity verification on the smartphone device. Based on the experimental results of the single-sensor modalities, we further investigated how to integrate them with hardware such as fingerprint and Trust Zone to practically fulfill a usable identity management system for both local application and remote services control. User studies and on-device testing sessions were held for privacy and usability evaluation.Computer Science, Department o

MobiSys 2016

The 14th ACM International Conference on Mobile Systems, Applications, and Services (MobiSys 2016) spanned a range of themes and domains, from smart environments to security and privacy. The highlights presented here cover the keynotes, paper sessions, and first Asian Students Symposium on Emerging Technologies

Is This Really You? An Empirical Study on Risk-Based Authentication Applied in the Wild

Risk-based authentication (RBA) is an adaptive security measure to strengthen password-based authentication. RBA monitors additional implicit features during password entry such as device or geolocation information, and requests additional authentication factors if a certain risk level is detected. RBA is recommended by the NIST digital identity guidelines, is used by several large online services, and offers protection against security risks such as password database leaks, credential stuffing, insecure passwords and large-scale guessing attacks. Despite its relevance, the procedures used by RBA-instrumented online services are currently not disclosed. Consequently, there is little scientific research about RBA, slowing down progress and deeper understanding, making it harder for end users to understand the security provided by the services they use and trust, and hindering the widespread adoption of RBA. In this paper, with a series of studies on eight popular online services, we (i) analyze which features and combinations/classifiers are used and are useful in practical instances, (ii) develop a framework and a methodology to measure RBA in the wild, and (iii) survey and discuss the differences in the user interface for RBA. Following this, our work provides a first deeper understanding of practical RBA deployments and helps fostering further research in this direction.Comment: 14 pages, 7 table