scholarly journals The LSTM-based Engine Clutch Engagement/Disengagement Anomaly Detection Algorithm for P2 HEV

2021 ◽  
Vol 29 (12) ◽  
pp. 1133-1146
Author(s):  
Yonghyeok Ji ◽  
Hyeongcheol Lee
Keyword(s):  
Anomaly Detection ◽  
Detection Algorithm ◽  
Clutch Engagement ◽  
Engine Clutch

scholarly journals A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles

2021 ◽  
Vol 11 (21) ◽  
pp. 10187
Author(s):  
Yonghyeok Ji ◽  
Seongyong Jeong ◽  
Yeongjin Cho ◽  
Howon Seo ◽  
Jaesung Bang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Electric Vehicles ◽  
Electric Drive ◽  
Hybrid Electric Vehicles ◽  
Short Term Memory ◽  
Support Vector ◽  
Clutch Engagement ◽  
Hybrid Electric ◽  
Engine Clutch

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.

scholarly journals An Anomaly Detection Algorithm for Microservice Architecture Based on Robust Principal Component Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226397-226408
Author(s):  
Mingxu Jin ◽  
Aoran Lv ◽  
Yuanpeng Zhu ◽  
Zijiang Wen ◽  
Yubin Zhong ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Anomaly Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Detection Algorithm ◽  
Robust Principal Component Analysis ◽  
Microservice Architecture

scholarly journals Performance evaluation of an anomaly-detection algorithm for keystroke-typing based insider detection

2018 ◽  
Vol 23 (5) ◽  
pp. 513-525
Author(s):  
Liang He ◽  
Zhixiang Li ◽  
Chao Shen
Keyword(s):  
Performance Evaluation ◽  
Anomaly Detection ◽  
Detection Algorithm

scholarly journals Evaluating the SEVIRI Fire Thermal Anomaly Detection Algorithm across the Central African Republic Using the MODIS Active Fire Product

2014 ◽  
Vol 6 (3) ◽  
pp. 1890-1917 ◽  
Author(s):  
Patrick Freeborn ◽  
Martin Wooster ◽  
Gareth Roberts ◽  
Weidong Xu
Keyword(s):  
Anomaly Detection ◽  
Central African Republic ◽  
Thermal Anomaly ◽  
Detection Algorithm ◽  
Active Fire

scholarly journals A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Yadi Wang ◽  
Wangyang Yu ◽  
Peng Teng ◽  
Guanjun Liu ◽  
Dongming Xiang
Keyword(s):  
Anomaly Detection ◽  
Business Processes ◽  
Data Flow ◽  
Detection Algorithm ◽  
Control Flow ◽  
Smart Devices ◽  
Integration Model ◽  
Whole Process ◽  
Mobile Transaction ◽  
And Control

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.

scholarly journals BCEAD: A Blockchain-Empowered Ensemble Anomaly Detection for Wireless Sensor Network via Isolation Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiong Yang ◽  
Yuling Chen ◽  
Xiaobin Qian ◽  
Tao Li ◽  
Xiao Lv
Keyword(s):  
Anomaly Detection ◽  
Block Structure ◽  
Detection Algorithm ◽  
Wireless Sensor ◽  
Detection Systems ◽  
Blockchain Technology ◽  
The Internet Of Things ◽  
Isolation Forest ◽  
Distributed Deployment ◽  
Internal Attacks

The distributed deployment of wireless sensor networks (WSNs) makes the network more convenient, but it also causes more hidden security hazards that are difficult to be solved. For example, the unprotected deployment of sensors makes distributed anomaly detection systems for WSNs more vulnerable to internal attacks, and the limited computing resources of WSNs hinder the construction of a trusted environment. In recent years, the widely observed blockchain technology has shown the potential to strengthen the security of the Internet of Things. Therefore, we propose a blockchain-based ensemble anomaly detection (BCEAD), which stores the model of a typical anomaly detection algorithm (isolated forest) in the blockchain for distributed anomaly detection in WSNs. By constructing a suitable block structure and consensus mechanism, the global model for detection can iteratively update to enhance detection performance. Moreover, the blockchain guarantees the trust environment of the network, making the detection algorithm resistant to internal attacks. Finally, compared with similar schemes, in terms of performance, cost, etc., the results prove that BCEAD performs better.

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