Zhaoyi Meng

Wansen Wang, Pu Zhang, Renjie Ji et al.

Some smart contracts violate decentralization principles by defining privileged accounts that manage other users' assets without permission, introducing centralization risks that have caused financial losses. Existing methods, however, face challenges in accurately detecting diverse centralization risks due to their dependence on predefined behavior patterns. In this paper, we propose JANUS, an automated analyzer for Solidity smart contracts that detects financial centralization risks independently of their specific behaviors. JANUS identifies differences between states reached by privileged and ordinary accounts, and analyzes whether these differences are finance-related. Focusing on the impact of risks rather than behaviors, JANUS achieves improved accuracy compared to existing tools and can uncover centralization risks with unknown patterns. To evaluate JANUS's performance, we compare it with other tools using a dataset of 540 contracts. Our evaluation demonstrates that JANUS outperforms representative tools in terms of detection accuracy for financial centralization risks . Additionally, we evaluate JANUS on a real-world dataset of 33,151 contracts, successfully identifying two types of risks that other tools fail to detect. We also prove that the state traversal method and variable summaries, which are used in JANUS to reduce the number of states to be compared, do not introduce false alarms or omissions in detection.

Zhaoyi Meng, Yan Xiong, Wenchao Huang et al.

Android users are now suffering severe threats from unwanted behaviors of various apps. The analysis of apps' audit logs is one of the essential methods for some device manufacturers to unveil the underlying malice within apps. We propose and implement AppAngio, a novel system that reveals contextual information in Android app behaviors by API-level audit logs. Our goal is to help analysts of device manufactures understand what has happened on users' devices and facilitate the identification of the malice within apps. The key module of AppAngio is identifying the path matched with the logs on the app's control-flow graph (CFG). The challenge, however, is that the limited-quantity logs may incur high computational complexity in the log matching, where there are a large number of candidates caused by the coupling relation of successive logs. To address the challenge, we propose a divide and conquer strategy that precisely positions the nodes matched with log records on the corresponding CFGs and connects the nodes with as few backtracks as possible. Our experiments show that AppAngio reveals the contextual information of behaviors in real-world apps. Moreover, the revealed results assist the analysts in identifying malice of app behaviors and complement existing analysis schemes. Meanwhile, AppAngio incurs negligible performance overhead on the Android device.