Hao Fu

Wenping Wang, Yunxi Guo, Chiyao Shen et al.

Embedding based retrieval has seen its usage in a variety of search applications like e-commerce, social networking search etc. While the approach has demonstrated its efficacy in tasks like semantic matching and contextual search, it is plagued by the problem of uncontrollable relevance. In this paper, we conduct an analysis of embedding-based retrieval launched in early 2021 on our social network search engine, and define two main categories of failures introduced by it, integrity and junkiness. The former refers to issues such as hate speech and offensive content that can severely harm user experience, while the latter includes irrelevant results like fuzzy text matching or language mismatches. Efficient methods during model inference are further proposed to resolve the issue, including indexing treatments and targeted user cohort treatments, etc. Though being simple, we show the methods have good offline NDCG and online A/B tests metrics gain in practice. We analyze the reasons for the improvements, pointing out that our methods are only preliminary attempts to this important but challenging problem. We put forward potential future directions to explore.

Tianbo Gu, Zheng Fang, Allaukik Abhishek et al.

Internet of Things (IoT) has become the most promising technology for service automation, monitoring, and interconnection, etc. However, the security and privacy issues caused by IoT arouse concerns. Recent research focuses on addressing security issues by looking inside platform and apps. In this work, we creatively change the angle to consider security problems from a wireless context perspective. We propose a novel framework called IoTGaze, which can discover potential anomalies and vulnerabilities in the IoT system via wireless traffic analysis. By sniffing the encrypted wireless traffic, IoTGaze can automatically identify the sequential interaction of events between apps and devices. We discover the temporal event dependencies and generate the Wireless Context for the IoT system. Meanwhile, we extract the IoT Context, which reflects user's expectation, from IoT apps' descriptions and user interfaces. If the wireless context does not match the expected IoT context, IoTGaze reports an anomaly. Furthermore, IoTGaze can discover the vulnerabilities caused by the inter-app interaction via hidden channels, such as temperature and illuminance. We provide a proof-of-concept implementation and evaluation of our framework on the Samsung SmartThings platform. The evaluation shows that IoTGaze can effectively discover anomalies and vulnerabilities, thereby greatly enhancing the security of IoT systems.

Tianbo Gu, Allaukik Abhishek, Hao Fu et al.

As a massive number of the Internet of Things (IoT) devices are deployed, the security and privacy issues in IoT arouse more and more attention. The IoT attacks are causing tremendous loss to the IoT networks and even threatening human safety. Compared to traditional networks, IoT networks have unique characteristics, which make the attack detection more challenging. First, the heterogeneity of platforms, protocols, software, and hardware exposes various vulnerabilities. Second, in addition to the traditional high-rate attacks, the low-rate attacks are also extensively used by IoT attackers to obfuscate the legitimate and malicious traffic. These low-rate attacks are challenging to detect and can persist in the networks. Last, the attackers are evolving to be more intelligent and can dynamically change their attack strategies based on the environment feedback to avoid being detected, making it more challenging for the defender to discover a consistent pattern to identify the attack. In order to adapt to the new characteristics in IoT attacks, we propose a reinforcement learning-based attack detection model that can automatically learn and recognize the transformation of the attack pattern. Therefore, we can continuously detect IoT attacks with less human intervention. In this paper, we explore the crucial features of IoT traffics and utilize the entropy-based metrics to detect both the high-rate and low-rate IoT attacks. Afterward, we leverage the reinforcement learning technique to continuously adjust the attack detection threshold based on the detection feedback, which optimizes the detection and the false alarm rate. We conduct extensive experiments over a real IoT attack dataset and demonstrate the effectiveness of our IoT attack detection framework.

Shan Chen, Weiguo Dai, Yuanxi Dai et al.

This paper presents Thinkey, an efficient, secure, infinitely scalable and decentralized blockchain architecture. It ensures system correctness and liveness by a multi-layer structure. In particular, the system is based on a double-chain architecture and uses a multi-layer consensus protocol to guarantee consistency. Thinkey also uses a novel account model which is based on Actor Model to support the complex logic in the multi-chain structure. Experiment results show that the proposed Thinkey architecture can achieve higher throughput as the number of nodes increases.

Hao Fu, Zizhan Zheng, Sencun Zhu et al.

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.

Hao Fu, Zizhan Zheng, Somdutta Bose et al.

Mobile applications (apps) often transmit sensitive data through network with various intentions. Some transmissions are needed to fulfill the app's functionalities. However, transmissions with malicious receivers may lead to privacy leakage and tend to behave stealthily to evade detection. The problem is twofold: how does one unveil sensitive transmissions in mobile apps, and given a sensitive transmission, how does one determine if it is legitimate? In this paper, we propose LeakSemantic, a framework that can automatically locate abnormal sensitive network transmissions from mobile apps. LeakSemantic consists of a hybrid program analysis component and a machine learning component. Our program analysis component combines static analysis and dynamic analysis to precisely identify sensitive transmissions. Compared to existing taint analysis approaches, LeakSemantic achieves better accuracy with fewer false positives and is able to collect runtime data such as network traffic for each transmission. Based on features derived from the runtime data, machine learning classifiers are built to further differentiate between the legal and illegal disclosures. Experiments show that LeakSemantic achieves 91% accuracy on 2279 sensitive connections from 1404 apps.

Hao Fu, Zizhan Zheng, Aveek K. Das et al.

The exponential growth of mobile devices has raised concerns about sensitive data leakage. In this paper, we make the first attempt to identify suspicious location-related HTTP transmission flows from the user's perspective, by answering the question: Is the transmission user-intended? In contrast to previous network-level detection schemes that mainly rely on a given set of suspicious hostnames, our approach can better adapt to the fast growth of app market and the constantly evolving leakage patterns. On the other hand, compared to existing system-level detection schemes built upon program taint analysis, where all sensitive transmissions as treated as illegal, our approach better meets the user needs and is easier to deploy. In particular, our proof-of-concept implementation (FlowIntent) captures sensitive transmissions missed by TaintDroid, the state-of-the-art dynamic taint analysis system on Android platforms. Evaluation using 1002 location sharing instances collected from more than 20,000 apps shows that our approach achieves about 91% accuracy in detecting illegitimate location transmissions.

Hao Fu, Hongxing Li, Zizhan Zheng et al.

The proliferation of Social Network Sites (SNSs) has greatly reformed the way of information dissemination, but also provided a new venue for hosts with impure motivations to disseminate malicious information. Social trust is the basis for information dissemination in SNSs. Malicious hosts judiciously and dynamically make the balance between maintaining its social trust and selfishly maximizing its malicious gain over a long time-span. Studying the optimal response strategies for each malicious host could assist to design the best system maneuver so as to achieve the targeted level of overall malicious activities. In this paper, we propose an interaction-based social trust model, and formulate the maximization of long-term malicious gains of multiple competing hosts as a non-cooperative differential game. Through rigorous analysis, optimal response strategies are identified and the best system maneuver mechanism is presented. Extensive numerical studies further verify the analytical results.