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Autoencoder-Based Anomaly Detection with Intrusion Scoring for Smart Factory Environments

Title
Autoencoder-Based Anomaly Detection with Intrusion Scoring for Smart Factory Environments
Author
조인휘
Keywords
Anomaly detection; Intrusion detection; Scoring; Autoencoder; DBSCAN; Smart factory; Industrial IoT
Issue Date
2019-02
Publisher
Springer Verlag
Citation
Communications in Computer and Information Science, v. 931, Page. 414-423
Abstract
The industry 4.0 and Industrial IoT is leading new industrial revolution. Industrial IoT technologies make more reliable and sustainable products than traditional products in automation industry. Industrial IoT devices transfer data between one another. This concept is need for advanced connectivity and intelligent security services. We focus on the security threat in Industrial IoT. The general security systems enable to detect normal security threat. However, it is not easy to detect anomaly threat or network intrusion or new hacking methods. In the paper, we propose autoencoder (AE) using the deep learning based anomaly detection with invasion scoring for the smart factory environments. We have analysis F-Score and accuracy between the Density Based Spatial Clustering of Applications with Noise (DBSCAN) and the autoencoder using the KDD data set. We have used real data from Korea steel companies and the collected data is general data such as temperature, stream flow, the shocks of machines, and etc. Finally, experiments show that the proposed autoencoder model is better than DBSCAN. © Springer Nature Singapore Pte Ltd. 2019.
URI
https://link.springer.com/chapter/10.1007%2F978-981-13-5907-1_44http://repository.hanyang.ac.kr/handle/20.500.11754/108274
ISBN
978-981135906-4; 978-981-13-5907-1
ISSN
1865-0929
DOI
10.1007/978-981-13-5907-1_44
Appears in Collections:
COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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