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Dijiang Huang
Researcher at Arizona State University
Publications - 214
Citations - 6191
Dijiang Huang is an academic researcher from Arizona State University. The author has contributed to research in topics: Cloud computing & Encryption. The author has an hindex of 38, co-authored 205 publications receiving 5288 citations. Previous affiliations of Dijiang Huang include University of Missouri–Kansas City & Hewlett-Packard.
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NICE: Network Intrusion Detection and Countermeasure Selection in Virtual Network Systems
TL;DR: This work proposes a multiphase distributed vulnerability detection, measurement, and countermeasure selection mechanism called NICE, which is built on attack graph-based analytical models and reconfigurable virtual network-based countermeasures to significantly improve attack detection and mitigate attack consequences.
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Location-aware key management scheme for wireless sensor networks
TL;DR: A grid-group scheme which uniformly deploy sensors in a large area and requires less number of keys preinstalled for each sensor and is resilient to selective node capture attack and node fabrication attack is proposed.
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PACP: An Efficient Pseudonymous Authentication-Based Conditional Privacy Protocol for VANETs
TL;DR: A new privacy preservation scheme, named pseudonymous authentication-based conditional privacy (PACP), which allows vehicles in a vehicular ad hoc network (VANET) to use pseudonyms instead of their true identity to obtain provably good privacy.
Proceedings ArticleDOI
MobiCloud: Building Secure Cloud Framework for Mobile Computing and Communication
TL;DR: The proposed MobiCloud framework enhances the operation of the ad hoc network itself by treating mobile devices as service nodes and will enhance communication by addressing trust management, secure routing, and risk management issues in the network.
Journal ArticleDOI
iDoctor: Personalized and professionalized medical recommendations based on hybrid matrix factorization
TL;DR: The experimental results show that iDoctor provides a higher predication rating and increases the accuracy of healthcare recommendation significantly, and is compared with previous healthcare recommendation methods using real datasets.