NLP-inspired deep learning for binary code analysis demonstrates notable performance. Considering the diverse Instruction Set Architectures (ISAs) on the market, it is important to be able to analyze code of various ISAs. However, training a deep learning model usually requires a large amount of data, which poses a challenge for certain ISAs such as PowerPC that suffer from the “data scarcity” issue. For instance, acquiring a large dataset of PowerPC malware proves to be challenging. Moreover, given a binary analysis task and multiple ISAs, it takes much time and effort (e.g., for data collection, labeling and cleaning, and parameter tuning) to train one modelperISA. We propose a new direction,retargeted-architecture binary code analysis, to handle the data scarcity issue and alleviate the per-ISA effort. Our idea is totransfer knowledge from one ISA to others—that is, a model, trained with rich data and much time and effort for one ISA, can perform prediction for otherswithout any modification. We showcase the idea through two important tasks: malware detection and function similarity detection. An extensive evaluation involving four ISAs (x86, ARM, MIPS, and PowerPC) demonstrates the effectiveness of the approach and the high performance is interpreted.

Secure pairing is crucial for ensuring the trustwor thy deployment and operation of Internet of Things (IoT) devices. However, traditional pairing methods are often unsuitable for IoT devices due to their lack of conventional user interfaces, such as keyboards. Proximity-based pairing approaches are usable but vulnerable to exploitation by co-located malicious devices. While methods based on a user's physical operations (such as shaking) on IoT devices offer greater security, they typically rely on inertial sensors to sense the operations, which most IoT devices lack. We introduce a novel technique calledUniversal Operation Sensing, enabling IoT devices to sense the user's physical operations without the need for inertial sensors. With this technique, users can complete pairing within seconds using simple actions such as pressing a button or twisting a knob, whether they are holding a smartphone or wearing a smartwatch. Moreover, we identify an inaccuracy issue caused by information loss in the commonly used fuzzy commitment protocol. To address it, we propose an accurate pairing protocol, without using fuzzy commitment, that incurszeroinformation loss. The comprehensive evaluation shows that it is secure, usable and efficient.

Matter is an IoT standard endorsed by hundreds of companies, designed to ensure interoperability between devices from various vendors. The Matter Software Development Kit (SDK) serves as the foundation for developing Matter devices, making bug discovery in Matter SDKs crucial. Given the extensive specification and the rapid evolution of Matter—five versions released in just two and a half years—the need for automated solutions is increasingly urgent. In this paper, we present MatterGuard, the first automated system for identifying bugs in Matter SDKs that violate the specification. Unlike traditional SDK testing approaches, which typically integrate testing code with the SDK code, Matter-Guard decouples the two, allowing the testing code to be reused across SDK versions. Furthermore, MatterGuard leverages a large language model to analyze the Matter specification and uses the extracted knowledge to guide the bug discovery process. In our evaluation across all five SDK versions, MatterGuard uncovers 109 bugs, demonstrating the effectiveness and scalability of our approach.

Given the massive difficulty in emulating IoT firmware, blackbox fuzzing of IoT devices for vulnerability discovery has become an attractive option. However, existing blackbox IoT fuzzers need much time and tedious effort to reverse engineer the IoT companion app (or manually collect test scripts) of each IoT device, which is unscalable when analyzing many devices. Moreover, fuzzing through a companion app is impeded by the input sanitization inside the app and limited to the manually revealed functions. We notice that IoT devices are typically able to connect a hub using standard wireless protocols (such as ZigBee, Z-Wave, and WiFi). We thus propose a uniform hub-based architecture for fuzzing various IoT devices, without reverse engineering any companion apps. It exploits the messages exchanged between a hub and an IoT device to automatically discover all the functions, and then launches systematic function-oriented message-semantics-guided fuzzing. It avoids sanitization imposed by a companion app. In addition, it conducts device state-sensitive fuzzing, which we find very effective in finding IoT bugs. We implement the system named HubFuzzer. The evaluation shows that HubFuzzer leads to much higher coverage than prior state of the art. We test 21 IoT devices and find 23 zero-day vulnerabilities. Four CVEs have been assigned.

Because of its cost effectiveness and timeliness, package delivery using unmanned aerial vehicles (UAVs), called drone delivery, is drawing growing attention. Authentication is critical for ensuring that a package is not picked up by an attacker's drone or delivered to an attacker. As delivery drones are costly and may carry sensitive or expensive packages, a drone should not get very close to a person unless she is authenticated; thus, conventional authentication approaches that require human-drone physical contact do not work. Existing authentication methods for drone delivery suffer from one or multiple of the following limitations: (1) requiring special user-side hardware; (2) enforcing one-way authentication only; (3) being vulnerable to relay attacks; (4) having compatibility issues. We present the first system, named Greet-to-Auth (G2Auth, for short), that supports mutual authentication between a user and a drone, without these limitations. A user waves her hand holding a smartphone to conduct the authentication. The evaluation shows that it is secure, accurate, usable, and robust.

Existing symbolic execution typically assumes the analyzer can control the I/O environment and/or access the library code, which, however, is not the case when programs run on a remote proprietary execution environment managed by another party. For example, SmartThings, one of the most popular IoT platforms, is such a cloud-based execution environment. For programmers who write automation applications to be deployed on IoT cloud platforms, it raises significant challenges when they want to systematically test their code and find bugs. We propose fuzzing-assisted remote dynamic symbolic execution, which uses dynamic symbolic execution as backbone and utilizes fuzzing when necessary to automatically test programs running in a remote proprietary execution environment over which the analyzer has little control. As a case study, we enable it for analyzing smart apps running on SmartThings. We have developed a prototype and the evaluation shows that it is effective in testing smart apps and finding bugs.

Application repackaging is a severe threat to Android users and the market. Not only does it infringe on intellectual property, but it is also one of the most common ways of propagating mobile malware. Existing countermeasures mostly detect repackaging based on app similarity measurement, which tends to be imprecise when obfuscations are applied to repackaged apps. Moreover, they rely on a central party, typically the hosting app store, to perform the detection, but many app stores fail to commit proper effort to piracy detection. We consider building the application repackaging detection capability into apps, such that user devices are made use to detect repackaging in a decentralized fashion.The main challenge is how to protect the detection code from being manipulated by attacks. We propose a creative use oflogic bombs, which are otherwise regularly used in malware. Thetrigger conditionsof bombs are constructed to exploit the differences between the attacker and users, such that a bomb that lies dormant on the attacker side will be activated on the user side. The detection code, which is part of the bombpayload, is executed only if the bomb is activated. We introducecryptographically obfuscated logic bombto enhance the bomb: (1) the detection code iswoveninto the neighboring original app code, (2) the mixed code gets encrypted using a key, and (3) the key is deleted from the app and can only be derived when the bomb is activated. Thus, attacks that try to modify or delete the detection code will corrupt the app itself, and searching the key in the application will be in vain. Moreover, we propose abomb sprayingtechnique that allows many bombs to be injected into an app, multiplying the needed adversary effort for bypassing the detection. In addition to repackaging detection, we present application tampering detection to fight attacks that insert malicious code into repackaged apps. We have implemented a prototype, namedBombDroid, that builds repackaging and tampering detection into apps through bytecode instrumentation. The evaluation and the security analysis show that the technique is effective, efficient, and resilient to various bomb analysis techniques including fuzzing, symbolic execution, multi-path exploration, and program slicing. Ethical issues due to the use of logic bombs are also discussed.

Secure pairing is key to trustworthy deployment and application of Internet of Things (IoT) devices. However, IoT devices lack conventional user interfaces, such as keyboards and displays, which makes many traditional pairing approaches inapplicable. Proximity-based pairing approaches are very usable, but can be exploited by co-located malicious devices. Approaches based on a user's physical operations on IoT devices are more secure, but typically require inertial sensors, while many devices do not satisfy this requirement. A secure and usable pairing approach that can be applied to heterogeneous IoT devices still does not exist. We develop a technique, Universal Operation Sensing, which allows an IoT device to sense the user's physical operations on it without requiring inertial sensors. With this technique, a user holding a smartphone or wearing a wristband can finish pairing in seconds through some very simple operations, e.g., pressing a button or twisting a knob. Moreover, we reveal an inaccuracy issue in original fuzzy commitment and propose faithful fuzzy commitment to resolve it. We design a pairing protocol using faithful fuzzy commitment, and build a prototype system named Touch-to-Pair (T2Pair, for short). The comprehensive evaluation shows that it is secure and usable.

Given a permission-based framework, its permission specification, which is a mapping between API methods of the framework and the permissions they require, is important for software developers and analysts. In the case of Android Framework, which contains millions of lines of code, static analysis is promising for analyzing such a large codebase to derive its permission specification. One of the common building blocks for static analysis is the generation of a global call graph. However, as common for object-oriented languages, the target of a virtual function call depends on the runtime type of the receiving object, which is undecidable statically. Existing work applies traditional analysis approaches, such as class-hierarchy analysis and points-to analysis, to building an over-approximated call graph of the framework, causing much imprecision to downstream analysis. We propose the heap memory snapshot assisted program analysis that leverages the dynamic information stored in the heap of Android Framework execution to assist in generating a more precise call graph; then, further analysis is performed on the call graph to extract the permission specification. We have developed a prototype and evaluated it on different versions of Android Framework. The evaluation shows that our method significantly improves on prior work, producing more precise results.

Android Application Framework is an integral and foundational part of the Android system. Each of the two billion (as of 2017) Android devices relies on the system services of Android Framework to manage applications and system resources. Given its critical role, a vulnerability in the framework can be exploited to launch large-scale cyber attacks and cause severe harms to user security and privacy. Recently, many vulnerabilities in Android Framework were exposed, showing that it is indeed vulnerable and exploitable. While there is a large body of studies on Android application analysis, research on Android Framework analysis is very limited. In particular, to our knowledge, there is no prior work that investigates how to enable symbolic execution of the framework, an approach that has proven to be very powerful for vulnerability discovery and exploit generation. We design and build the first system, Centaur, that enables symbolic execution of Android Framework. Due to the middleware nature and technical peculiarities of the framework that impinge on the analysis, many unique challenges arise and are addressed in Centaur. The system has been applied to discovering new vulnerability instances, which can be exploited by recently uncovered attacks against the framework, and to generating PoC exploits.

Exploitation of heap vulnerabilities has been on the rise, leading to many devastating attacks. Conventional heap patch generation is a lengthy procedure requiring intensive manual efforts. Worse, fresh patches tend to harm system dependability, hence deterring users from deploying them. We propose a heap patching system HEAPTHERAPY+ that simultaneously has the following prominent advantages: (1) generating patches without manual efforts; (2) installing patches without altering the code (so called code-less patching); (3) handling various heap vulnerability types; (4) imposing a very low overhead; and (5) no dependency on specific heap allocators. As a separate contribution, we propose targeted calling context encoding, which is a suite of algorithms for optimizing calling context encoding, an important technique with applications in many areas. The system properly combines heavyweight offline attack analysis with lightweight online defense generation, and provides a new countermeasure against heap attacks. The evaluation shows that the system is effective and efficient.

Adversarial examples (AEs) are crafted by adding human-imperceptible perturbations to inputs such that a machine-learning based classifier incorrectly labels them. They have become a severe threat to the trustworthiness of machine learning. While AEs in the image domain have been well studied, audio AEs are less investigated. Recently, multiple techniques are proposed to generate audio AEs, which makes countermeasures against them urgent. Our experiments show that, given an audio AE, the transcription results by Automatic Speech Recognition (ASR) systems differ significantly (that is, poor transferability), as different ASR systems use different architectures, parameters, and training datasets. Based on this fact and inspired by Multiversion Programming, we propose a novel audio AE detection approach MVP-Ears, which utilizes the diverse off-the-shelf ASRs to determine whether an audio is an AE. We build the largest audio AE dataset to our knowledge, and the evaluation shows that the detection accuracy reaches 99.88%. While transferable audio AEs are difficult to generate at this moment, they may become a reality in future. We further adapt the idea above to proactively train the detection system for coping with transferable audio AEs. Thus, the proactive detection system is one giant step ahead of attackers working on transferable AEs.

Internet of Things (IoT) are densely deployed in smart environments, such as homes, factories and laboratories, where many people have physical access to IoT devices. How to authenticate users operating on these devices is thus an important problem. IoT devices usually lack conventional user interfaces, such as keyboards and mice, which makes traditional authentication methods inapplicable. We present a virtual sensing technique that allows IoT devices to virtually sense user 'petting' (in the form of some very simple touches for about 2 seconds) on the devices. Based on this technique, we build a secure and intuitive authentication method that authenticates device users by comparing the petting operations sensed by devices and those captured by the user wristband. The authentication method is highly secure as physical operations are required, rather than based on proximity. It is also intuitive, adopting very simple authentication operations, e.g., clicking buttons, twisting rotary knobs, and swiping touchscreens. Unlike the state-of-the-art methods, our method does not require any hardware modifications of devices, and thus can be applied to commercial off-the-shelf (COTS) devices. We build prototypes and evaluate them comprehensively, demonstrating their high effectiveness, security, usability, and efficiency.

Application repackaging is a severe threat to Android users and the market. Existing countermeasures mostly detect repackaging based on app similarity measurement and rely on a central party to perform detection, which is unscalable and imprecise. We instead consider building the detection capability into apps, such that user devices are made use of to detect repackaging in a decentralized fashion. The main challenge is how to protect repackaging detection code from attacks. We propose a creative use of logic bombs, which are regularly used in malware, to conquer the challenge. A novel bomb structure is invented and used: the trigger conditions are constructed to exploit the differences between the attacker and users, such that a bomb that lies dormant on the attacker side will be activated on one of the user devices, while the repackaging detection code, which is packed as the bomb payload, is kept inactive until the trigger conditions are satisfied. Moreover, the repackaging detection code is woven into the original app code and gets encrypted; thus, attacks by modifying or deleting suspicious code will corrupt the app itself. We have implemented a prototype, named BombDroid, that builds the repackaging detection into apps through bytecode instrumentation, and the evaluation shows that the technique is effective, efficient, and resilient to various adversary analysis including symbol execution, multi-path exploration, and program slicing.

Android Application Framework is an integral and foundational part of the Android system. Each of the 1.4 billion Android devices relies on the system services of Android Framework to manage applications and system resources. Given its critical role, a vulnerability in the framework can be exploited to launch large-scale cyber attacks and cause severe harms to user security and privacy. Recently, many vulnerabilities in Android Framework were exposed, showing that it is vulnerable and exploitable. However, most of the existing research has been limited to analyzing Android applications, while there are very few techniques and tools developed for analyzing Android Framework. In particular, to our knowledge, there is no previous work that analyzes the framework through symbolic execution, an approach that has proven to be very powerful for vulnerability discovery and exploit generation. We design and build the first system, Centaur, that enables symbolic execution of Android Framework. Due to some unique characteristics of the framework, such as its middleware nature and extraordinary complexity, many new challenges arise and are tackled in Centaur. In addition, we demonstrate how the system can be applied to discovering new vulnerability instances, which can be exploited by several recently uncovered attacks against the framework, and to generating PoC exploits.

Existing code similarity comparison methods, whether source or binary code based, are mostly not resilient to obfuscations. Identifying similar or identical code fragments among programs is very important in some applications. For example, one application is to detect illegal code reuse. In the code theft cases, emerging obfuscation techniques have made automated detection increasingly difficult. Another application is to identify cryptographic algorithms which are widely employed by modern malware to circumvent detection, hide network communications, and protect payloads among other purposes. Due to diverse coding styles and high programming flexibility, different implementation of the same algorithm may appear very distinct, causing automatic detection to be very hard, let alone code obfuscations are sometimes applied. In this paper, we propose a binary-oriented, obfuscation-resilient binary code similarity comparison method based on a new concept, longest common subsequence of semantically equivalent basic blocks , which combines rigorous program semantics with longest common subsequence based fuzzy matching. We model the semantics of a basic block by a set of symbolic formulas representing the input-output relations of the block. This way, the semantic equivalence (and similarity) of two blocks can be checked by a theorem prover. We then model the semantic similarity of two paths using the longest common subsequence with basic blocks as elements. This novel combination has resulted in strong resiliency to code obfuscation. We have developed a prototype. The experimental results show that our method can be applied to software plagiarism and algorithm detection, and is effective and practical to analyze real-world software.

App repackaging has become a severe threat to theAndroid ecosystem. While various protection techniques, such as watermarking and repackaging detection, have been proposed, a defense that stops repackaged apps from working on user devices, i.e., repackage-proofing, is missing. We propose a technique that builds a reliable and stealthy repackage-proofing capability into Android apps. A large number of detection nodes are inserted into the original app without incurring much overhead, each is woven into the surrounding code to blur itself. Once repackaging is detected, a response node injects a failure in the form of delayed malfunctions, making it difficult to trace back. The response nodes and detection nodes form high-degree connections and communicate through stealthy communication channels, such that upon detection several of the many response nodes are selected stochastically to take actions, which further obfuscates and enhances the protection. We have built a prototype. The evaluation shows that the technique is effective and efficient.

Existing code similarity comparison methods, whether source or binary code based, are mostly not resilient to obfuscations. In the case of software plagiarism, emerging obfuscation techniques have made automated detection increasingly difficult. In this paper, we propose a binary-oriented, obfuscation-resilient method based on a new concept, longest common subsequence of semantically equivalent basic blocks, which combines rigorous program semantics with longest common subsequence based fuzzy matching. We model the semantics of a basic block by a set of symbolic formulas representing the input-output relations of the block. This way, the semantics equivalence (and similarity) of two blocks can be checked by a theorem prover. We then model the semantics similarity of two paths using the longest common subsequence with basic blocks as elements. This novel combination has resulted in strong resiliency to code obfuscation. We have developed a prototype and our experimental results show that our method is effective and practical when applied to real-world software.