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

  • Gimin Bae
  • Sunggyun Jang
  • Minseop Kim
  • Inwhee JoeEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 931)


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.


Anomaly detection Intrusion detection Scoring Autoencoder DBSCAN Smart factory Industrial IoT 



This work was supported by the Technology development Program (S2521883) funded by the Ministry of SMEs and Startups (MSS, Korea).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Gimin Bae
    • 1
  • Sunggyun Jang
    • 1
  • Minseop Kim
    • 1
  • Inwhee Joe
    • 1
    Email author
  1. 1.Department of Computer and SoftwareHanyang UniversitySeoulKorea

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