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

共 50 条

[1] Intrusion Detection System Based on Multi-Layer Perceptron Neural Networks and Decision Tree
Esmaily, Jamal
Moradinezhad, Reza
Ghasemi, Jamal
[J]. 2015 7TH CONFERENCE ON INFORMATION AND KNOWLEDGE TECHNOLOGY (IKT), 2015,
[2] An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron
Alazab, Moutaz
Abu Khurma, Ruba
Castillo, Pedro A.
Abu-Salih, Bilal
Martin, Alejandro
Camacho, David
[J]. EGYPTIAN INFORMATICS JOURNAL, 2024, 25
[3] Classification of Sensorimotor Rhythms Based on Multi-layer Perceptron Neural Networks
Toderean, Roxana
[J]. 2020 15TH INTERNATIONAL CONFERENCE ON DEVELOPMENT AND APPLICATION SYSTEMS (DAS), 2020,
[4] Modified time-based multilayer perceptron for sensor networks and image processing applications
Von Pless, G
Al Karim, T
Reznik, L
[J]. PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 2201 - 2206
[5] A MULTI-LAYER PERCEPTRON APPROACH FOR FLOW-BASED ANOMALY DETECTION
Van Efferen, Lennart
Ali-Eldin, Amr M. T.
[J]. 2017 INTERNATIONAL SYMPOSIUM ON NETWORKS, COMPUTERS AND COMMUNICATIONS (ISNCC), 2017,
[6] Monotonic multi-layer perceptron networks as universal approximators
Lang, B
[J]. ARTIFICIAL NEURAL NETWORKS: FORMAL MODELS AND THEIR APPLICATIONS - ICANN 2005, PT 2, PROCEEDINGS, 2005, 3697 : 31 - 37
[7] MULTI-LAYER PERCEPTRON BASED SPEECH ACTIVITY DETECTION FOR SPEAKER VERIFICATION
Ganapathy, Sriram
Rajan, Padmanabhan
Hermansky, Hynek
[J]. 2011 IEEE WORKSHOP ON APPLICATIONS OF SIGNAL PROCESSING TO AUDIO AND ACOUSTICS (WASPAA), 2011, : 321 - 324
[8] RECURSIVE ASSEMBLY OF MULTI-LAYER PERCEPTRON NEURAL NETWORKS
Motato, Eliot
Radcliffe, Clark
[J]. 7TH ANNUAL DYNAMIC SYSTEMS AND CONTROL CONFERENCE, 2014, VOL 2, 2014,
[9] Hand Pose Detection in HMD Environments by Sensor Fusion using Multi-layer Perceptron
Luc Cong Vu
You, Bum-Jae
[J]. 2019 1ST INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION (ICAIIC 2019), 2019, : 218 - 223
[10] An extension of multi-layer perceptron based on layer-topology
Zuters, J
[J]. ENFORMATIKA, VOL 7: IEC 2005 PROCEEDINGS, 2005, : 178 - 181

← 1 2 3 4 5 →