Time-based multi-layer perceptron for novelty detection in sensor networks

被引：0

作者：

Von Pless, G ^{[1
]}

Al Karim, T ^{[1
]}

Reznik, L ^{[1
]}

机构：

[1] Rochester Inst Technol, Dept Comp Sci, Rochester, NY 14623 USA

来源：

PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA'04) | 2004年

关键词：

sensor networks; time series prediction; novelty detection;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a novelty detection system for sensor networks. The system utilizes a neural network function prediction methodology to predict sensor outputs in order to determine if the sensor outputs are novel. In addition to the novelty detection system, a modification to a standard neural network function predictor is proposed that allows the novelty detection system to quickly learn to accurately predict next sensor outputs. The parameter choice and the relationship between. the threshold values and false alarm and missing attack rates are studied and recommendations are provided.

引用

页码：156 / 163

页数：8

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