Showing papers by "Mira Mezini published in 2020"
Posted Content•
TL;DR: It is demonstrated that in real-world scenarios the current GAP design is vulnerable to profiling and possibly de-anonymizing infected persons, and relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system.
Abstract: Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called "Google/Apple Proposal", which we abbreviate by "GAP". We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.
55 citations
01 Jan 2020
TL;DR: MuDetect as discussed by the authors uses a new graph representation of API usages that captures different types of API misuses and a systematically designed ranking strategy that effectively improves precision, achieving twice the recall of previous detectors and 2.5x higher precision.
Abstract: Application Programming Interfaces (APIs) often impose constraints such as call order or preconditions. API misuses, i.e., usages violating these constraints, may cause software crashes, data-loss, and vulnerabilities. Researchers developed several approaches to detect API misuses, typically still resulting in low recall and precision. In this work, we investigate ways to improve API-misuse detection. We design MuDetect, an API-misuse detector that builds on the strengths of existing detectors and tries to mitigate their weaknesses. MuDetect uses a new graph representation of API usages that captures different types of API misuses and a systematically designed ranking strategy that effectively improves precision. Evaluation shows that MuDetect identifies real-world API misuses with twice the recall of previous detectors and 2.5x higher precision. It even achieves almost 4x higher precision and recall, when mining patterns across projects, rather than from only the target project.
32 citations
01 Dec 2020
TL;DR: In this article, the authors demonstrate that the current GAP design is vulnerable to profiling and possibly de-anonymizing infected persons, and relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system.
Abstract: Google and Apple have jointly provided an API for exposure notification in order to implement decentralized contract tracing apps using Bluetooth Low Energy, the so-called “Google/Apple Proposal”, which we abbreviate by “GAP”. We demonstrate that in real-world scenarios the current GAP design is vulnerable to (i) profiling and possibly de-anonymizing infected persons, and (ii) relay-based wormhole attacks that basically can generate fake contacts with the potential of affecting the accuracy of an app-based contact tracing system. For both types of attack, we have built tools that can easily be used on mobile phones or Raspberry Pis (e.g., Bluetooth sniffers). The goal of our work is to perform a reality check towards possibly providing empirical real-world evidence for these two privacy and security risks. We hope that our findings provide valuable input for developing secure and privacy-preserving digital contact tracing systems.
29 citations
TL;DR: It is shown that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion and developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing.
Abstract: String obfuscation is an established technique used by proprietary, closed-source applications to protect intellectual property. Furthermore, it is also frequently used to hide spyware or malware in applications. In both cases, the techniques range from bit-manipulation over XOR operations to AES encryption. However, string obfuscation techniques/tools suffer from one shared weakness: They generally have to embed the necessary logic to deobfuscate strings into the app code.
In this paper, we show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion. We developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing.
We evaluated StringHound on both benign and malicious Android apps. In summary, we deobfuscate almost 30 times more obfuscated strings than other string deobfuscation tools. Additionally, we analyzed 100,000 Google Play Store apps and found multiple obfuscated strings that hide vulnerable cryptographic usages, insecure internet accesses, API keys, hard-coded passwords, and exploitation of privileges without the awareness of the developer. Furthermore, our analysis reveals that not only malware uses string obfuscation but also benign apps make extensive use of string obfuscation.
12 citations
29 Oct 2020
TL;DR: In this article, a disruption-tolerant networking bundle agent that uses LoRa radio technology to provide decentralized basic means of communication is proposed, and a geospatial routing mechanism for efficient message forwarding is proposed.
Abstract: Modern information and communication technology (ICT) is often very vulnerable to disruptions through disasters. Yet, the ability to communicate and distribute messages is vital for efficient disaster response. Furthermore, ad hoc deployment of flexible, robust, and affordable communication systems in a disaster area are often necessary. Therefore, we propose a disruption-tolerant networking bundle agent that uses LoRa radio technology to provide decentralized basic means of communication. To address the hardware’s technological limitations as well as the uncertainty of the user locations and their movement behavior, we propose a geospatial routing mechanism for efficient message forwarding. In conjunction with the communication and routing solutions presented, we also designed specific pager-like hardware for intuitive message reception and bridging of smartphones into LoRa networks. We evaluated our solutions in various simulations as well as through real-world implementations on different hardware platforms.
9 citations
05 Oct 2020
TL;DR: In this paper, the authors show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion, and they develop StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing.
Abstract: String obfuscation is an established technique used by proprietary, closed-source applications to protect intellectual property. Furthermore, it is also frequently used to hide spyware or malware in applications. In both cases, the techniques range from bit-manipulation over XOR operations to AES encryption. However, string obfuscation techniques/tools suffer from one shared weakness: They generally have to embed the necessary logic to deobfuscate strings into the app code. In this paper, we show that most of the string obfuscation techniques found in malicious and benign applications for Android can easily be broken in an automated fashion. We developed StringHound, an open-source tool that uses novel techniques that identify obfuscated strings and reconstruct the originals using slicing. We evaluated StringHound on both benign and malicious Android apps. In summary, we deobfuscate almost 30 times more obfuscated strings than other string deobfuscation tools. Additionally, we analyzed 100,000 Google Play Store apps and found multiple obfuscated strings that hide vulnerable cryptographic usages, insecure internet accesses, API keys, hard-coded passwords, and exploitation of privileges without the awareness of the developer. Furthermore, our analysis reveals that not only malware uses string obfuscation but also benign apps make extensive use of string obfuscation.
9 citations
08 Nov 2020
TL;DR: In this article, the authors present an approach to static analyses that leverages the modularity of blackboard systems and combines declarative and imperative techniques to improve soundness, precision, and scalability.
Abstract: Current approaches combining multiple static analyses deriving different, independent properties focus either on modularity or performance. Whereas declarative approaches facilitate modularity and automated, analysis-independent optimizations, imperative approaches foster manual, analysis-specific optimizations. In this paper, we present a novel approach to static analyses that leverages the modularity of blackboard systems and combines declarative and imperative techniques. Our approach allows exchangeability, and pluggable extension of analyses in order to improve sound(i)ness, precision, and scalability and explicitly enables the combination of otherwise incompatible analyses. With our approach integrated in the OPAL framework, we were able to implement various dissimilar analyses, including a points-to analysis that outperforms an equivalent analysis from Doop, the state-of-the-art points-to analysis framework.
8 citations
18 Jul 2020
TL;DR: A programming model for semi-implicit parallelization of static analyses which is inspired by reactive programming, able to outperform a state-of-the-art, specialized parallel IFDS solver both in absolute performance and scalability is proposed.
Abstract: Parallelization of static analyses is necessary to scale to real-world programs, but it is a complex and difficult task and, therefore, often only done manually for selected high-profile analyses. In this paper, we propose a programming model for semi-implicit parallelization of static analyses which is inspired by reactive programming. Reusing the domain-expert knowledge on how to parallelize anal- yses encoded in the programming framework, developers do not need to think about parallelization and concurrency issues on their own. The programming model supports stateful computations, only requires monotonic computations over lattices, and is independent of specific analyses. Our evaluation shows the applicability of the programming model to different analyses and the importance of user-selected scheduling strategies. We implemented an IFDS solver that was able to outperform a state-of-the-art, specialized parallel IFDS solver both in absolute performance and scalability.
8 citations
01 Nov 2020
TL;DR: The results show that throughput, latency, and power consumption are significantly improved when executing applications on the Wi-Fi chip rather than in the operating system kernel or in user space, and that the high-level programming abstractions of ReactiFi have no performance overhead compared to manually written C code.
Abstract: Network programmability will be required to handle future increased network traffic and constantly changing application needs. However, there is currently no way of using a high-level, easy to use programming language to program Wi-Fi firmware. This impedes rapid prototyping and deployment of novel network services/applications and hinders continuous performance optimization in Wi-Fi networks, since expert knowledge is required for both the used hardware platforms and the Wi-Fi domain. In this paper, we present ReactiFi, a high-level reactive programming language to program Wi-Fi chips on mobile consumer devices. ReactiFi enables programmers to implement extensions of PHY, MAC, and IP layer mechanisms without requiring expert knowledge of Wi-Fi chips, allowing for novel applications and network protocols. ReactiFi programs are executed directly on the Wi-Fi chip, improving performance and power consumption compared to execution on the main CPU. ReactiFi is conceptually similar to functional reactive languages, but is dedicated to the domain-specific needs of Wi-Fi firmware. First, it handles low-level platform-specific details without interfering with the core functionality of Wi-Fi chips. Second, it supports static reasoning about memory usage of applications, which is important for typically memory-constrained Wi-Fi chips. Third, it limits dynamic changes of dependencies between computations to dynamic branching, in order to enable static reasoning about the order of computations. We evaluate ReactiFi empirically in two real-world case studies. Our results show that throughput, latency, and power consumption are significantly improved when executing applications on the Wi-Fi chip rather than in the operating system kernel or in user space. Moreover, we show that the high-level programming abstractions of ReactiFi have no performance overhead compared to manually written C code.
5 citations
Posted Content•
TL;DR: A novel approach to static analyses that leverages the modularity of blackboard systems and combines declarative and imperative techniques is presented, which allows exchangeability, and pluggable extension of analyses in order to improve soundness, precision, and scalability.
Abstract: Current approaches combining multiple static analyses deriving different, independent properties focus either on modularity or performance. Whereas declarative approaches facilitate modularity and automated, analysis-independent optimizations, imperative approaches foster manual, analysis-specific optimizations.
In this paper, we present a novel approach to static analyses that leverages the modularity of blackboard systems and combines declarative and imperative techniques. Our approach allows exchangeability, and pluggable extension of analyses in order to improve sound(i)ness, precision, and scalability and explicitly enables the combination of otherwise incompatible analyses. With our approach integrated in the OPAL framework, we were able to implement various dissimilar analyses, including a points-to analysis that outperforms an equivalent analysis from Doop, the state-of-the-art points-to analysis framework.
5 citations
TL;DR: This paper presents an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime and systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code.
Posted Content•
TL;DR: This work presents go-geiger, a novel tool for Go developers to quantify unsafe usages in a project's source code and all of its dependencies, and go-safer, a Novel static analysis tool to identify dangerous and common usage patterns that were previously undetected with existing tools.
Abstract: The Go programming language aims to provide memory and thread safety through measures such as automated memory management with garbage collection and a strict type system. However, it also offers a way of circumventing this safety net through the use of the unsafe package. While there are legitimate use cases for unsafe, developers must exercise caution to avoid introducing vulnerabilities like buffer overflows or memory corruption in general. Using go-geiger, we conducted a study on the usage of unsafe in the top 500 most popular open-source Go projects on GitHub, including a manual analysis of 1,400 code samples on how unsafe is used. From the projects using Go's module system, 38% directly contain at least one unsafe usage, and 91% contain at least one unsafe usage in the project itself or one of its transitive dependencies. Based on the usage patterns found, we present possible exploit vectors in different scenarios. Finally, we present go-safer, a novel static analysis tool to identify dangerous and common usage patterns that were previously undetected with existing tools.
15 Jun 2020
TL;DR: TACAI is proposed, a refinable IR that is based on abstract interpretation results of a method's bytecode that can be efficiently computed and provides slightly more precise receiver-type information than Soot's Shimple representation.
Abstract: Most Java static analysis frameworks provide an intermediate presentation (IR) of Java Bytecode to facilitate the development of static analyses. While such IRs are often based on three-address code, the transformation itself is a great opportunity to apply optimizations to the transformed code, such as constant propagation. In this paper, we propose TACAI, a refinable IR that is based on abstract interpretation results of a method's bytecode. Exchanging the underlying abstract interpretation domains enables the creation of various IRs of different precision levels. Our evaluation shows that TACAI can be efficiently computed and provides slightly more precise receiver-type information than Soot's Shimple representation. Furthermore, we show how exchanging the underlying abstract domains directly impacts the generated IR.
01 Dec 2020
TL;DR: Go-Geiger as discussed by the authors is a static analysis tool to quantify unsafe usages in a project's source code and all of its dependencies, which can be used to identify dangerous and common usage patterns.
Abstract: The Go programming language aims to provide memory and thread safety through measures such as automated memory management with garbage collection and a strict type system. However, it also offers a way of circumventing this safety net through the use of the unsafe package. While there are legitimate use cases for unsafe, developers must exercise caution to avoid introducing vulnerabilities like buffer overflows or memory corruption in general. In this work, we present go-geiger, a novel tool for Go developers to quantify unsafe usages in a project's source code and all of its dependencies. Using go-geiger, we conducted a study on the usage of unsafe in the top 500 most popular open-source Go projects on GitHub, including a manual analysis of 1,400 code samples on how unsafe is used. From the projects using Go's module system, 38% directly contain at least one unsafe usage, and 91% contain at least one unsafe usage in the project itself or one of its transitive dependencies. Based on the usage patterns found, we present possible exploit vectors in different scenarios. Finally, we present go-safer, a novel static analysis tool to identify dangerous and common usage patterns that were previously undetected with existing tools.
TL;DR: ReactiFi as discussed by the authors is a high-level reactive programming language to program Wi-Fi chips on mobile consumer devices, which enables programmers to implement extensions of PHY, MAC, and IP layer mechanisms without requiring expert knowledge of WiFi chips.
Abstract: Network programmability will be required to handle future increased network traffic and constantly changing application needs. However, there is currently no way of using a high-level, easy to use programming language to program Wi-Fi firmware. This impedes rapid prototyping and deployment of novel network services/applications and hinders continuous performance optimization in Wi-Fi networks, since expert knowledge is required for both the used hardware platforms and the Wi-Fi domain. In this paper, we present ReactiFi, a high-level reactive programming language to program Wi-Fi chips on mobile consumer devices. ReactiFi enables programmers to implement extensions of PHY, MAC, and IP layer mechanisms without requiring expert knowledge of Wi-Fi chips, allowing for novel applications and network protocols. ReactiFi programs are executed directly on the Wi-Fi chip, improving performance and power consumption compared to execution on the main CPU. ReactiFi is conceptually similar to functional reactive languages, but is dedicated to the domain-specific needs of Wi-Fi firmware. First, it handles low-level platform-specific details without interfering with the core functionality of Wi-Fi chips. Second, it supports static reasoning about memory usage of applications, which is important for typically memory-constrained Wi-Fi chips. Third, it limits dynamic changes of dependencies between computations to dynamic branching, in order to enable static reasoning about the order of computations. We evaluate ReactiFi empirically in two real-world case studies. Our results show that throughput, latency, and power consumption are significantly improved when executing applications on the Wi-Fi chip rather than in the operating system kernel or in user space. Moreover, we show that the high-level programming abstractions of ReactiFi have no performance overhead compared to manually written C code.