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From Bi-Level to One-Level: A Framework for Structural Attacks to Graph Anomaly Detection
Yulin Zhu;Yuni Lai;Kaifa Zhao;Xiapu Luo;Mingquan Yuan;Jun Wu;Jian Ren;Kai Zhou
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
The success of graph neural networks stimulates the prosperity of graph mining and the corresponding downstream tasks including graph anomaly detection (GAD). However, it has been explored that those graph mining methods are vulnerable to structural manipulations on relational data. That is, the attacker can maliciously perturb the graph structures to assist the target nodes in evading anomaly det...Show More
Demystifying Privacy and Security Issues in Potentially Harmful Mobile Applications
2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS)
Year: 2023 | Conference Paper |
Android system's features, such as support for peer-to-peer networking, cloud computing services, and various communication protocols and network technologies, make it a suitable platform for distributed computing systems that require efficient resource management and coordination. The widespread popularity of Android, as one of the most widely-used mobile operating systems [1], is attributed to i...Show More
Demystifying Privacy Policy of Third-Party Libraries in Mobile Apps
Kaifa Zhao;Xian Zhan;Le Yu;Shiyao Zhou;Hao Zhou;Xiapu Luo;Haoyu Wang;Yepang Liu
2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE)
Year: 2023 | Conference Paper |
Cited by: Papers (8)
The privacy of personal information has received significant attention in mobile software. Although researchers have designed methods to identify the conflict between app behavior and privacy policies, little is known about the privacy compliance issues relevant to third-party libraries (TPLs). The regulators enacted articles to regulate the usage of personal information for TPLs (e.g., the CCPA r...Show More
BinarizedAttack: Structural Poisoning Attacks to Graph-based Anomaly Detection
Yulin Zhu;Yuni Lai;Kaifa Zhao;Xiapu Luo;Mingquan Yuan;Jian Ren;Kai Zhou
2022 IEEE 38th International Conference on Data Engineering (ICDE)
Year: 2022 | Conference Paper |
Cited by: Papers (11)
Graph-based Anomaly Detection (GAD) is becoming prevalent due to the powerful representation abilities of graphs as well as recent advances in graph mining techniques. These GAD tools, however, expose a new attacking surface, ironically due to their unique advantage of being able to exploit the relations among data. That is, attackers now can manipulate those relations (i.e., the structure of the ...Show More
Inaccurate Prediction Is Not Always Bad: Open-World Driver Recognition via Error Analysis
2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Driver identification is of fundamental importance in many vehicle-related applications, such as fleet monitoring and anti-theft system. The vast majority of existing methods work under the closed-world assumption, which may be unrealistic in practice. In this paper, we consider a more practical but challenging scenario, i.e., open-world driver recognition, and propose a systematic method dubbed D...Show More
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