Visible to the public Biblio

Filters: Author is Yang, B.  [Clear All Filters]
Yang, B., Liu, F., Yuan, L., Zhang, Y..  2020.  6LoWPAN Protocol Based Infrared Sensor Network Human Target Locating System. 2020 15th IEEE Conference on Industrial Electronics and Applications (ICIEA). :1773–1779.
This paper proposes an infrared sensor human target locating system for the Internet of Things. In this design, the wireless sensor network is designed and developed to detect human targets by using 6LoWPAN protocol and pyroelectric infrared (PIR) sensors. Based on the detection data acquired by multiple sensor nodes, K-means++ clustering algorithm combined with cost function is applied to complete human target location in a 10m×10m detection area. The experimental results indicate the human locating system works well and the user can view the location information on the terminal devices.
Yang, B., Xu, G., Zeng, X., Liu, J., Zhang, Y..  2018.  A Lightweight Anonymous Mobile User Authentication Scheme for Smart Grid. 2018 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). :821-827.

Smart Grid (SG) technology has been developing for years, which facilitates users with portable access to power through being applied in numerous application scenarios, one of which is the electric vehicle charging. In order to ensure the security of the charging process, users need authenticating with the smart meter for the subsequent communication. Although there are many researches in this field, few of which have endeavored to protect the anonymity and the untraceability of users during the authentication. Further, some studies consider the problem of user anonymity, but they are non-light-weight protocols, even some can not assure any fairness in key agreement. In this paper, we first points out that existing authentication schemes for Smart Grid are neither lack of critical security nor short of important property such as untraceability, then we propose a new two-factor lightweight user authentication scheme based on password and biometric. The authentication process of the proposed scheme includes four message exchanges among the user mobile, smart meter and the cloud server, and then a security one-time session key is generated for the followed communication process. Moreover, the scheme has some new features, such as the protection of the user's anonymity and untraceability. Security analysis shows that our proposed scheme can resist various well-known attacks and the performance analysis shows that compared to other three schemes, our scheme is more lightweight, secure and efficient.

Yang, B., Ro\v zić, V., Grujić, M., Mentens, N., Verbauwhede, I..  2017.  On-Chip Jitter Measurement for True Random Number Generators. 2017 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). :91–96.

Applications of true random number generators (TRNGs) span from art to numerical computing and system security. In cryptographic applications, TRNGs are used for generating new keys, nonces and masks. For this reason, a TRNG is an essential building block and often a point of failure for embedded security systems. One type of primitives that are widely used as source of randomness are ring oscillators. For a ring-oscillator-based TRNG, the true randomness originates from its timing jitter. Therefore, determining the jitter strength is essential to estimate the quality of a TRNG. In this paper, we propose a method to measure the jitter strength of a ring oscillator implemented on an FPGA. The fast tapped delay chain is utilized to perform the on-chip measurement with a high resolution. The proposed method is implemented on both a Xilinx FPGA and an Intel FPGA. Fast carry logic components on different FPGAs are used to implement the fast delay line. This carry logic component is designed to be fast and has dedicated routing, which enables a precise measurement. The differential structure of the delay chain is used to thwart the influence of undesirable noise from the measurement. The proposed methodology can be applied to other FPGA families and ASIC designs.

Shen, G., Tang, Y., Li, S., Chen, J., Yang, B..  2017.  A General Framework of Hardware Trojan Detection: Two-Level Temperature Difference Based Thermal Map Analysis. 2017 11th IEEE International Conference on Anti-Counterfeiting, Security, and Identification (ASID). :172–178.

With the globalization of integrated circuit design and manufacturing, Hardware Trojan have posed serious threats to the security of commercial chips. In this paper, we propose the framework of two-level temperature difference based thermal map analysis detection method. In our proposed method, thermal maps of an operating chip during a period are captured, and they are differentiated with the thermal maps of a golden model. Then every pixel's differential temperature of differential thermal maps is extracted and compared with other pixel's. To mitigate the Gaussian white noise and to differentiate the information of Hardware Trojan from the information of normal circuits, Kalman filter algorithm is involved. In our experiment, FPGAs configured with equivalent circuits are utilized to simulate the real chips to validate our proposed approach. The experimental result reveals that our proposed framework can detect Hardware Trojan whose power proportion magnitude is 10''3.

Wang, S., Yan, Q., Chen, Z., Yang, B., Zhao, C., Conti, M..  2017.  TextDroid: Semantics-based detection of mobile malware using network flows. 2017 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). :18–23.

The wide-spreading mobile malware has become a dreadful issue in the increasingly popular mobile networks. Most of the mobile malware relies on network interface to coordinate operations, steal users' private information, and launch attack activities. In this paper, we propose TextDroid, an effective and automated malware detection method combining natural language processing and machine learning. TextDroid can extract distinguishable features (n-gram sequences) to characterize malware samples. A malware detection model is then developed to detect mobile malware using a Support Vector Machine (SVM) classifier. The trained SVM model presents a superior performance on two different data sets, with the malware detection rate reaching 96.36% in the test set and 76.99% in an app set captured in the wild, respectively. In addition, we also design a flow header visualization method to visualize the highlighted texts generated during the apps' network interactions, which assists security researchers in understanding the apps' complex network activities.

Yang, B., Zhang, T..  2016.  A Scalable Meta-Model for Big Data Security Analyses. 2016 IEEE 2nd International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing (HPSC), and IEEE International Conference on Intelligent Data and Security (IDS). :55–60.

This paper proposes a highly scalable framework that can be applied to detect network anomaly at per flow level by constructing a meta-model for a family of machine learning algorithms or statistical data models. The approach is scalable and attainable because raw data needs to be accessed only one time and it will be processed, computed and transformed into a meta-model matrix in a much smaller size that can be resident in the system RAM. The calculation of meta-model matrix can be achieved through disposable updating operations at per row level: once a per-flow information is proceeded, it is no longer needed in calculating the meta-model matrix. While the proposed framework covers both Gaussian and non-Gaussian data, the focus of this work is on the linear regression models. Specifically, a new concept called meta-model sufficient statistics is proposed to analyze a group of models, where exact, not the approximate, results are derived. In addition, the proposed framework can quickly discover an optimal statistical or computer model from a family of candidate models without the need of rescanning the raw dataset. This suggest an extremely efficient and effectively theory and method is possible for big data security analysis.