Visible to the public Biblio

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Lin, L., Zhong, S., Jia, C., Chen, K..  2017.  Insider Threat Detection Based on Deep Belief Network Feature Representation. 2017 International Conference on Green Informatics (ICGI). :54–59.

Insider threat is a significant security risk for information system, and detection of insider threat is a major concern for information system organizers. Recently existing work mainly focused on the single pattern analysis of user single-domain behavior, which were not suitable for user behavior pattern analysis in multi-domain scenarios. However, the fusion of multi-domain irrelevant features may hide the existence of anomalies. Previous feature learning methods have relatively a large proportion of information loss in feature extraction. Therefore, this paper proposes a hybrid model based on the deep belief network (DBN) to detect insider threat. First, an unsupervised DBN is used to extract hidden features from the multi-domain feature extracted by the audit logs. Secondly, a One-Class SVM (OCSVM) is trained from the features learned by the DBN. The experimental results on the CERT dataset demonstrate that the DBN can be used to identify the insider threat events and it provides a new idea to feature processing for the insider threat detection.

Chen, K., Mao, H., Shi, X., Xu, Y., Liu, A..  2017.  Trust-Aware and Location-Based Collaborative Filtering for Web Service QoS Prediction. 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC). 2:143–148.

The rapid development of cloud computing has resulted in the emergence of numerous web services on the Internet. Selecting a suitable cloud service is becoming a major problem for users especially non-professionals. Quality of Service (QoS) is considered to be the criterion for judging web services. There are several Collaborative Filtering (CF)-based QoS prediction methods proposed in recent years. QoS values among different users may vary largely due to the network and geographical location. Moreover, QoS data provided by untrusted users will definitely affect the prediction accuracy. However, most existing methods seldom take both facts into consideration. In this paper, we present a trust-aware and location-based approach for web service QoS prediction. A trust value for each user is evaluated before the similarity calculation and the location is taken into account in similar neighbors selecting. A series of experiments are performed based on a realworld QoS dataset including 339 service users and 5,825 services. The experimental analysis shows that the accuracy of our method is much higher than other CF-based methods.

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.