Biblio

Filters: Author is Yuan, J.  [Clear All Filters]
2019-09-11
Wang, L., Wang, D., Gao, J., Huo, C., Bai, H., Yuan, J..  2019.  Research on Multi-Source Data Security Protection of Smart Grid Based on Quantum Key Combination. 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). :449–453.

Power communication network is an important infrastructure of power system. For a large number of widely distributed business terminals and communication terminals. The data protection is related to the safe and stable operation of the whole power grid. How to solve the problem that lots of nodes need a large number of keys and avoid the situation that these nodes cannot exchange information safely because of the lack of keys. In order to solve the problem, this paper proposed a segmentation and combination technology based on quantum key to extend the limited key. The basic idea was to obtain a division scheme according to different conditions, and divide a key into several different sub-keys, and then combine these key segments to generate new keys and distribute them to different terminals in the system. Sufficient keys were beneficial to key updating, and could effectively enhance the ability of communication system to resist damage and intrusion. Through the analysis and calculation, the validity of this method in the use of limited quantum keys to achieve the business data secure transmission of a large number of terminal was further verified.

2017-12-12
Bhattacharjee, S. Das, Yuan, J., Jiaqi, Z., Tan, Y. P..  2017.  Context-aware graph-based analysis for detecting anomalous activities. 2017 IEEE International Conference on Multimedia and Expo (ICME). :1021–1026.

This paper proposes a context-aware, graph-based approach for identifying anomalous user activities via user profile analysis, which obtains a group of users maximally similar among themselves as well as to the query during test time. The main challenges for the anomaly detection task are: (1) rare occurrences of anomalies making it difficult for exhaustive identification with reasonable false-alarm rate, and (2) continuously evolving new context-dependent anomaly types making it difficult to synthesize the activities apriori. Our proposed query-adaptive graph-based optimization approach, solvable using maximum flow algorithm, is designed to fully utilize both mutual similarities among the user models and their respective similarities with the query to shortlist the user profiles for a more reliable aggregated detection. Each user activity is represented using inputs from several multi-modal resources, which helps to localize anomalies from time-dependent data efficiently. Experiments on public datasets of insider threats and gesture recognition show impressive results.