Title :
Online anomaly detection with expert system feedback in social networks
Author :
Horn, Corinne ; Willett, Rebecca
Author_Institution :
Dept. of Electr. & Comput. Eng., Duke Univ., Durham, NC, USA
Abstract :
In this paper, we propose examining the participants in various meetings or communications within a social network, and using sequential inference based on these participant lists to quickly and accurately predict anomalies in the content of those communications. The proposed approach consists of two main elements: (1) filtering, or assigning a belief or likelihood to each successive measurement based upon our ability to predict it from previous noisy observations, and (2) hedging, or flagging potential anomalies by comparing the current belief against a time-varying and data-adaptive threshold. The threshold is adjusted based on feedback requested from an expert system. In general, parsing communication data can require nontrivial computational resources, but since parsed data is only used sparingly for feedback, the overall computational complexity of the proposed approach is relatively low. Regret bounds quantify the performance of the proposed approach, and experiments on the Enron email database demonstrate its efficacy.
Keywords :
Internet; computational complexity; electronic mail; expert systems; feedback; inference mechanisms; information filtering; security of data; social networking (online); Enron email database; anomaly hedging; belief assignment; belief filtering; computational complexity; data-adaptive threshold; expert system feedback; online anomaly detection; sequential inference; social networks; time-varying threshold; Computational complexity; Electronic mail; Expert systems; Filtering; Noise measurement; Presses; Social network services; anomaly detection; exponential families; filtering; label-efficient prediction; sequential probability assignment;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946887