Visible to the public Biblio

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C
Cheng, X., Zhou, M., Song, X., Gu, M., Sun, J..  2017.  IntPTI: Automatic integer error repair with proper-type inference. 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE). :996–1001.

Integer errors in C/C++ are caused by arithmetic operations yielding results which are unrepresentable in certain type. They can lead to serious safety and security issues. Due to the complicated semantics of C/C++ integers, integer errors are widely harbored in real-world programs and it is error-prone to repair them even for experts. An automatic tool is desired to 1) automatically generate fixes which assist developers to correct the buggy code, and 2) provide sufficient hints to help developers review the generated fixes and better understand integer types in C/C++. In this paper, we present a tool IntPTI that implements the desired functionalities for C programs. IntPTI infers appropriate types for variables and expressions to eliminate representation issues, and then utilizes the derived types with fix patterns codified from the successful human-written patches. IntPTI provides a user-friendly web interface which allows users to review and manage the fixes. We evaluate IntPTI on 7 real-world projects and the results show its competitive repair accuracy and its scalability on large code bases. The demo video for IntPTI is available at: https://youtu.be/9Tgd4A\_FgZM.

L
Li, H., He, Y., Sun, L., Cheng, X., Yu, J..  2016.  Side-channel information leakage of encrypted video stream in video surveillance systems. IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications. :1–9.

Video surveillance has been widely adopted to ensure home security in recent years. Most video encoding standards such as H.264 and MPEG-4 compress the temporal redundancy in a video stream using difference coding, which only encodes the residual image between a frame and its reference frame. Difference coding can efficiently compress a video stream, but it causes side-channel information leakage even though the video stream is encrypted, as reported in this paper. Particularly, we observe that the traffic patterns of an encrypted video stream are different when a user conducts different basic activities of daily living, which must be kept private from third parties as obliged by HIPAA regulations. We also observe that by exploiting this side-channel information leakage, attackers can readily infer a user's basic activities of daily living based on only the traffic size data of an encrypted video stream. We validate such an attack using two off-the-shelf cameras, and the results indicate that the user's basic activities of daily living can be recognized with a high accuracy.

Li, W., Song, T., Li, Y., Ma, L., Yu, J., Cheng, X..  2017.  A Hierarchical Game Framework for Data Privacy Preservation in Context-Aware IoT Applications. 2017 IEEE Symposium on Privacy-Aware Computing (PAC). :176–177.

Due to the increasing concerns of securing private information, context-aware Internet of Things (IoT) applications are in dire need of supporting data privacy preservation for users. In the past years, game theory has been widely applied to design secure and privacy-preserving protocols for users to counter various attacks, and most of the existing work is based on a two-player game model, i.e., a user/defender-attacker game. In this paper, we consider a more practical scenario which involves three players: a user, an attacker, and a service provider, and such a complicated system renders any two-player model inapplicable. To capture the complex interactions between the service provider, the user, and the attacker, we propose a hierarchical two-layer three-player game framework. Finally, we carry out a comprehensive numerical study to validate our proposed game framework and theoretical analysis.

S
Shi, F., Chen, Z., Cheng, X..  2020.  Behavior Modeling and Individual Recognition of Sonar Transmitter for Secure Communication in UASNs. IEEE Access. 8:2447—2454.

It is necessary to improve the safety of the underwater acoustic sensor networks (UASNs) since it is mostly used in the military industry. Specific emitter identification is the process of identifying different transmitters based on the radio frequency fingerprint extracted from the received signal. The sonar transmitter is a typical low-frequency radiation source and is an important part of the UASNs. Class D power amplifier, a typical nonlinear amplifier, is usually used in sonar transmitters. The inherent nonlinearity of power amplifiers provides fingerprint features that can be distinguished without transmitters for specific emitter recognition. First, the nonlinearity of the sonar transmitter is studied in-depth, and the nonlinearity of the power amplifier is modeled and its nonlinearity characteristics are analyzed. After obtaining the nonlinear model of an amplifier, a similar amplifier in practical application is obtained by changing its model parameters as the research object. The output signals are collected by giving the same input of different models, and, then, the output signals are extracted and classified. In this paper, the memory polynomial model is used to model the amplifier. The power spectrum features of the output signals are extracted as fingerprint features. Then, the dimensionality of the high-dimensional features is reduced. Finally, the classifier is used to recognize the amplifier. The experimental results show that the individual sonar transmitter can be well identified by using the nonlinear characteristics of the signal. By this way, this method can enhance the communication safety of the UASNs.

X
Xia, H., Xiao, F., Zhang, S., Hu, C., Cheng, X..  2019.  Trustworthiness Inference Framework in the Social Internet of Things: A Context-Aware Approach. IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. :838–846.
The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.