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C
Wang, Kai, Zhang, Yuqing, Liu, Peng.  2016.  Call Me Back!: Attacks on System Server and System Apps in Android Through Synchronous Callback. Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. :92–103.

Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.

F
Zhu, Lipeng, Fu, Xiaotong, Yao, Yao, Zhang, Yuqing, Wang, He.  2019.  FIoT: Detecting the Memory Corruption in Lightweight IoT Device Firmware. 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :248–255.
The IoT industry has developed rapidly in recent years, which has attracted the attention of security researchers. However, the researchers are hampered by the wide variety of IoT device operating systems and their hardware architectures. Especially for the lightweight IoT devices, many manufacturers do not provide the device firmware images, embedded firmware source code or even the develop documents. As a result, it hinders traditional static analysis and dynamic analysis techniques. In this paper, we propose a novel dynamic analysis framework, called FIoT, which aims at finding memory corruption vulnerabilities in lightweight IoT device firmware images. The key idea is dynamically run the binary code snippets through symbolic execution with carrying out a fuzzing test. Specifically, we generate code snippets through traversing the control-flow graph (CFG) in a backward manner. We improved the CFG recovery approach and backward slice approach for better performance. To reduce the influence of the binary firmware, FIoT leverages loading address determination analysis and library function identification approach. We have implemented a prototype of FIoT and conducted experiments. Our results show that FIoT can complete the Fuzzing test within 40 seconds in average. Considering 170 seconds for static analysis, FIoT can load and analyze a lightweight IoT firmware within 210 seconds in total. Furthermore, we illustrate the effectiveness of FIoT by applying it over 115 firmware images from 17 manufacturers. We have found 35 images exist memory corruptions, which are all zero-day vulnerabilities.
R
Jia, Ziyi, Wu, Chensi, Zhang, Yuqing.  2019.  Research on the Destructive Capability Metrics of Common Network Attacks. 2019 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). :1419—1424.

An improved algorithm of the Analytic Hierarchy Process (AHP) is proposed in this paper, which is realized by constructing an improved judgment matrix. Specifically, rough set theory is used in the algorithm to calculate the weight of the network metric data, and then the improved AHP algorithm nine-point systemic is structured, finally, an improved AHP judgment matrix is constructed. By performing an AHP operation on the improved judgment matrix, the weight of the improved network metric data can be obtained. If only the rough set theory is applied to process the network index data, the objective factors would dominate the whole process. If the improved algorithm of AHP is used to integrate the expert score into the process of measurement, then the combination of subjective factors and objective factors can be realized. Based on the aforementioned theory, a new network attack metrics system is proposed in this paper, which uses a metric structure based on "attack type-attack attribute-attack atomic operation-attack metrics", in which the metric process of attack attribute adopts AHP. The metrics of the system are comprehensive, given their judgment of frequent attacks is universal. The experiment was verified by an experiment of a common attack Smurf. The experimental results show the effectiveness and applicability of the proposed measurement system.