Abstract Binary analysis, the process of examining software without its source code, plays a crucial role in understanding program behavior, e.g., evaluating the security properties of commercial software, and analyzing malware. One challenging aspect of this process is to classify data encoding schemes, such as encryption and compression, due to the absence of high-level semantic information. Existing approaches either rely on code similarity, which only works for known schemes, or heuristic rules, which lack scalability. In this paper, we propose DESCG, a novel deep learning-based method for automatically classifying four widely employed kinds of data encoding schemes in binary programs: encryption, compression, decompression, and hashing. Our approach leverages dynamic analysis to extract execution traces from binary programs, builds data dependency graphs from these traces, and incorporates critical feature engineering. By combining the specialized graph representation with the Graph Neural Network (GNN), our approach enables accurate classification without requiring prior knowledge of specific encoding schemes. The Evaluation result shows that DESCG achieves 97.7% accuracy and an F1 score of 97.67%, outperforming baseline models. We also conducted an extensive evaluation of DESCG to explore which feature is more important for it and examine its performance and overhead.

In recent years, Internet-of-Things (IoT) platforms and systems have been rapidly emerging. Although IoT is a new technology, new does not mean simpler (than existing networked systems). Contrarily, the complexity (of IoT platforms and systems) is actually being increased in terms of the interactions between the physical world and cyberspace. The increased complexity indeed results in new vulnerabilities. This article seeks to provide a review of the recently discovered logic bugs that are specific to IoT platforms and systems and discuss the lessons we learned from these bugs. In particular, 20 logic bugs and one weakness falling into seven categories of vulnerabilities are reviewed in this survey.

Cyber–Physical–Social Systems are frequently prescribed for providing valuable information on personalized services . The foundation of these services is big data which must be trustily collected and efficiently processed. Though High Performance Computing and Communication technique makes great contributions to addressing the issue of data processing, its effectiveness still relies on the veracity of data generated from Internet of Things (IoT) devices. Nevertheless, IoT devices, as basic production facilities to ensure data’s security, are unable to deploy expensive security extensions. Consequently, it causes the implementation of the task sandboxing, the fundamental security mechanism in Real-Time Operating Systems (RTOSs), much simpler and more vulnerable. In this paper, we take ARM Mbed uVisor as an example system, utilizing hypervisor-based task sandboxing mechanisms, and presents three new findings: First, we discover vulnerabilities against Mbed task sandboxing, which can be exploited to compromise system-maintained data structure to manipulate any tasks’ data. Second, we present LIPS (Lightweight Intra-Mode Privilege Separation), building a special protection domain to isolate particular system-maintained data structures. Finally, thorough evaluation and experimental tests show the efficiency of LIPS to defeat these attacks, with small runtime overheads and good portability.

With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which are very heterogeneous to each other. Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware. In recent years, we have witnessed devastating security breaches targeting low-end microcontroller-based IoT devices. These security concerns have significantly hamstrung further evolution of the IoT technology. In this work, we present Laelaps, a device emulator specifically designed to run diverse software of microcontroller devices. We do not encode into our emulator any specific information about a device. Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly. This unique design feature makes Laelaps capable of running diverse firmware with no a priori knowledge about the target device. To demonstrate the capabilities of Laelaps, we applied dynamic analysis techniques on top of our emulator. We successfully identified both self-injected and real-world vulnerabilities.

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.

Sensitive data in a process could be scattered over the memory of a computer system for a prolonged period of time. Unfortunately, DRAM chips were proven insecure in previous studies. The problem becomes worse in the mobile environment, in which users' smartphones are easily lost or stolen. The powered-on phones may contain sensitive data in the vulnerable DRAM chips. In this paper, we propose MemVault, a mechanism to protect sensitive data in Android devices against physical memory attacks. MemVault keeps track of the propagation of well-marked sensitive data sources, and selectively encrypts tainted sensitive memory contents in the DRAM chip. When a tainted object is accessed, MemVault redirects the access to the internal RAM (iRAM), where the cipher-text object is decrypted transparently. iRAM is a system-on-chip (SoC) component which is by nature immune to physical memory exploits. We have implemented a MemVault prototype system, and have evaluated it with extensive experiments. Our results validate that MemVault effectively eliminates the occurrences of clear-text sensitive objects in DRAM chips, and imposes acceptable overheads.

The rapid evolution of Internet-of-Things (IoT) technologies has led to an emerging need to make them smarter. However, the smartness comes at the cost of multi-vector security exploits. From cyber space, a compromised operating system could access all the data in a cloud-aware IoT device. From physical space, cold-boot attacks and DMA attacks impose a great threat to the unattended devices. In this paper, we propose TrustShadow that provides a comprehensively protected execution environment for unmodified application running on ARM-based IoT devices. To defeat cyber attacks, TrustShadow takes advantage of ARM TrustZone technology and partitions resources into the secure and normal worlds. In the secure world, TrustShadow constructs a trusted execution environment for security-critical applications. This trusted environment is maintained by a lightweight runtime system. The runtime system does not provide system services itself. Rather, it forwards them to the untrusted normal-world OS, and verifies the returns. The runtime system further employs a page based encryption mechanism to ensure that all the data segments of a security-critical application appear in ciphertext in DRAM chip. When an encrypted data page is accessed, it is transparently decrypted to a page in the internal RAM, which is immune to physical exploits.

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.

Although the input validation vulnerabilities play a critical role in web application security, such vulnerabilities are so far largely neglected in the Android security research community. We found that due to the unique Framework Code layer, Android devices do need specific input validation vulnerability analysis in system services. In this work, we take the first steps to analyze Android specific input validation vulnerabilities. In particular, a) we take the first steps towards measuring the corresponding attack surface and reporting the current input validation status of Android system services. b) We developed a new input validation vulnerability scanner for Android devices. This tool fuzzes all the Android system services by sending requests with malformed arguments to them. Through comprehensive evaluation of Android system with over 90 system services and over 1,900 system service methods, we identified 16 vulnerabilities in Android system services. We have reported all the issues to Google and Google has confirmed them.