Visible to the public Biblio

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2018
Chen, G., Wang, D., Li, T., Zhang, C., Gu, M., Sun, J..  2018.  Scalable Verification Framework for C Program. 2018 25th Asia-Pacific Software Engineering Conference (APSEC). :129-138.

Software verification has been well applied in safety critical areas and has shown the ability to provide better quality assurance for modern software. However, as lines of code and complexity of software systems increase, the scalability of verification becomes a challenge. In this paper, we present an automatic software verification framework TSV to address the scalability issues: (i) the extended structural abstraction and property-guided program slicing to solve large-scale program verification problem, saving time and memory without losing accuracy; (ii) automatically select different verification methods according to the program and property context to improve the verification efficiency. For evaluation, we compare TSV's different configurations with existing C program verifiers based on open benchmarks. We found that TSV with auto-selection performs better than with bounded model checking only or with extended structural abstraction only. Compared to existing tools such as CMBC and CPAChecker, it acquires 10%-20% improvement of accuracy and 50%-90% improvement of memory consumption.

Cho, S., Chen, G., Chun, H., Coon, J. P., O'Brien, D..  2018.  Impact of multipath reflections on secrecy in VLC systems with randomly located eavesdroppers. 2018 IEEE Wireless Communications and Networking Conference (WCNC). :1–6.
Considering reflected light in physical layer security (PLS) is very important because a small portion of reflected light enables an eavesdropper (ED) to acquire legitimate information. Moreover, it would be a practical strategy for an ED to be located at an outer area of the room, where the reflection light is strong, in order to escape the vigilance of a legitimate user. Therefore, in this paper, we investigate the impact of multipath reflections on PLS in visible light communication in the presence of randomly located eavesdroppers. We apply spatial point processes to characterize randomly distributed EDs. The generalized error in signal-to-noise ratio that occurs when reflections are ignored is defined as a function of the distance between the receiver and the wall. We use this error for quantifying the domain of interest that needs to be considered from the secrecy viewpoint. Furthermore, we investigate how the reflection affects the secrecy outage probability (SOP). It is shown that the effect of the reflection on the SOP can be removed by adjusting the light emitting diode configuration. Monte Carlo simulations and numerical results are given to verify our analysis.
2017
Lu, Y., Chen, G., Luo, L., Tan, K., Xiong, Y., Wang, X., Chen, E..  2017.  One more queue is enough: Minimizing flow completion time with explicit priority notification. IEEE INFOCOM 2017 - IEEE Conference on Computer Communications. :1–9.

Ideally, minimizing the flow completion time (FCT) requires millions of priorities supported by the underlying network so that each flow has its unique priority. However, in production datacenters, the available switch priority queues for flow scheduling are very limited (merely 2 or 3). This practical constraint seriously degrades the performance of previous approaches. In this paper, we introduce Explicit Priority Notification (EPN), a novel scheduling mechanism which emulates fine-grained priorities (i.e., desired priorities or DP) using only two switch priority queues. EPN can support various flow scheduling disciplines with or without flow size information. We have implemented EPN on commodity switches and evaluated its performance with both testbed experiments and extensive simulations. Our results show that, with flow size information, EPN achieves comparable FCT as pFabric that requires clean-slate switch hardware. And EPN also outperforms TCP by up to 60.5% if it bins the traffic into two priority queues according to flow size. In information-agnostic setting, EPN outperforms PIAS with two priority queues by up to 37.7%. To the best of our knowledge, EPN is the first system that provides millions of priorities for flow scheduling with commodity switches.

Chen, G., Coon, J..  2017.  Enhancing secrecy by full-duplex antenna selection in cognitive networks. 2017 IEEE Symposium on Computers and Communications (ISCC). :540–545.

We consider an underlay cognitive network with secondary users that support full-duplex communication. In this context, we propose the application of antenna selection at the secondary destination node to improve the secondary user secrecy performance. Antenna selection rules for cases where exact and average knowledge of the eavesdropping channels are investigated. The secrecy outage probabilities for the secondary eavesdropping network are analyzed, and it is shown that the secrecy performance improvement due to antenna selection is due to coding gain rather than diversity gain. This is very different from classical antenna selection for data transmission, which usually leads to a higher diversity gain. Numerical simulations are included to verify the performance of the proposed scheme.

Ren, Z., Chen, G..  2017.  EntropyVis: Malware classification. 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). :1–6.

Malware writers often develop malware with automated measures, so the number of malware has increased dramatically. Automated measures tend to repeatedly use significant modules, which form the basis for identifying malware variants and discriminating malware families. Thus, we propose a novel visualization analysis method for researching malware similarity. This method converts malicious Windows Portable Executable (PE) files into local entropy images for observing internal features of malware, and then normalizes local entropy images into entropy pixel images for malware classification. We take advantage of the Jaccard index to measure similarities between entropy pixel images and the k-Nearest Neighbor (kNN) classification algorithm to assign entropy pixel images to different malware families. Preliminary experimental results show that our visualization method can discriminate malware families effectively.

Luo, X., Chen, K., Pang, G., Shou, L., Chen, G..  2017.  Visible Nearest Neighbor Search for Objects Moving on Consecutive Trajectories. 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC). :1296–1303.

A visible nearest neighbor (VNN) query returns the k nearest objects that are visible to a query point, which is used to support various applications such as route planning, target monitoring, and antenna placement. However, with the proliferation of wireless communications and advances in positioning technology for mobile equipments, efficiently searching for VNN among moving objects are required. While most previous work on VNN query focused on static objects, in this paper, we treats the objects as moving consecutively when indexing them, and study the visible nearest neighbor query for moving objects (MVNN) . Assuming that the objects are represented as trajectories given by linear functions of time, we propose a scheme which indexes the moving objects by time-parameterized R-tree (TPR-tree) and obstacles by R-tree. The paper offers four heuristics for visibility and space pruning. New algorithms, Post-pruning and United-pruning, are developed for efficiently solving MVNN queries with all four heuristics. The effectiveness and efficiency of our solutions are verified by extensive experiments over synthetic datasets on real road network.