An Anomaly Detection Scheme based on LSTM Autoencoder for Energy Management

2020 ◽  
Author(s):  
Hong-Soon Nam ◽  
Youn-Kwae Jeong ◽  
Jong Won Park
Keyword(s):  
Anomaly Detection ◽  
Energy Management ◽  
Detection Scheme

LogNADS: Network anomaly detection scheme based on semantic representation

2021 ◽  
Author(s):  
Xu Liu ◽  
Weiyou Liu ◽  
Xiaoqiang Di ◽  
Jinqing Li ◽  
Binbin Cai ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Semantic Representation ◽  
Detection Scheme ◽  
Network Anomaly Detection

scholarly journals Lightweight Anomaly Detection Scheme Using Incremental Principal Component Analysis and Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8017
Author(s):  
Nurfazrina M. Zamry ◽  
Anazida Zainal ◽  
Murad A. Rassam ◽  
Eman H. Alkhammash ◽  
Fuad A. Ghaleb ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Sensor Networks ◽  
Computational Complexity ◽  
Anomaly Detection ◽  
Principal Component ◽  
Support Vector ◽  
Communication Overhead ◽  
Detection Scheme ◽  
Memory Utilization

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with (𝑛𝑑) memory utilization and no communication overhead.

Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings

Energy ◽  
2018 ◽  
Vol 157 ◽  
pp. 336-352 ◽  
Author(s):  
Alfonso Capozzoli ◽  
Marco Savino Piscitelli ◽  
Silvio Brandi ◽  
Daniele Grassi ◽  
Gianfranco Chicco
Keyword(s):  
Anomaly Detection ◽  
Energy Management ◽  
Pattern Learning ◽  
Smart Buildings ◽  
Load Pattern

Growing Structure Multiple Model Systems for Anomaly Detection and Fault Diagnosis

2009 ◽  
Vol 131 (5) ◽  
Author(s):  
Jianbo Liu ◽  
Dragan Djurdjanovic ◽  
Kenneth Marko ◽  
Jun Ni
Keyword(s):  
Fault Diagnosis ◽  
Anomaly Detection ◽  
System Development ◽  
Simulation Software ◽  
Model Systems ◽  
Multiple Model ◽  
Detection Scheme ◽  
Engine Simulation ◽  
Local Decision ◽  
Residual Errors

A new anomaly detection scheme based on growing structure multiple model system (GSMMS) is proposed in this paper to detect and quantify the effects of anomalies. The GSMMS algorithm combines the advantages of growing self-organizing networks with efficient local model parameter estimation into an integrated framework for modeling and identification of general nonlinear dynamic systems. The identified model then serves as a foundation for building an effective anomaly detection and fault diagnosis system. By utilizing the information about system operation region provided by the GSMMS, the residual errors can be analyzed locally within each operation region. This local decision making scheme can accommodate for unequally distributed residual errors across different operational regions. The performance of the newly proposed method is evaluated through anomaly detection and quantification in an electronically controlled throttle system, which is simulated using a high-fidelity engine simulation software package provided by a major automotive manufacturer for control system development.

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