Autoencoder-based on anomaly detection with intrusion scoring for smart factory environments
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Bae, Gimin | - |
dc.contributor.author | Jang, Sunggyun | - |
dc.contributor.author | Kim, Minseop | - |
dc.contributor.author | Joe, Inwhee | - |
dc.date.accessioned | 2022-07-10T09:41:35Z | - |
dc.date.available | 2022-07-10T09:41:35Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-02 | - |
dc.identifier.issn | 1865-0929 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/148337 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Verlag | - |
dc.title | Autoencoder-based on anomaly detection with intrusion scoring for smart factory environments | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Joe, Inwhee | - |
dc.identifier.doi | 10.1007/978-981-13-5907-1_44 | - |
dc.identifier.scopusid | 2-s2.0-85062267358 | - |
dc.identifier.bibliographicCitation | Communications in Computer and Information Science, v.931, pp.414 - 423 | - |
dc.relation.isPartOf | Communications in Computer and Information Science | - |
dc.citation.title | Communications in Computer and Information Science | - |
dc.citation.volume | 931 | - |
dc.citation.startPage | 414 | - |
dc.citation.endPage | 423 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Anomaly detection | - |
dc.subject.keywordPlus | Deep learning | - |
dc.subject.keywordPlus | Distributed computer systems | - |
dc.subject.keywordPlus | Intrusion detection | - |
dc.subject.keywordPlus | Personal computing | - |
dc.subject.keywordPlus | Security systems | - |
dc.subject.keywordPlus | Stream flow | - |
dc.subject.keywordPlus | Auto encoders | - |
dc.subject.keywordPlus | DBSCAN | - |
dc.subject.keywordPlus | Density-based spatial clustering of applications with noise | - |
dc.subject.keywordPlus | Industrial IoT | - |
dc.subject.keywordPlus | Industrial revolutions | - |
dc.subject.keywordPlus | Scoring | - |
dc.subject.keywordPlus | Sustainable products | - |
dc.subject.keywordPlus | Traditional products | - |
dc.subject.keywordPlus | Internet of things | - |
dc.subject.keywordAuthor | Anomaly detection | - |
dc.subject.keywordAuthor | Autoencoder | - |
dc.subject.keywordAuthor | DBSCAN | - |
dc.subject.keywordAuthor | Industrial IoT | - |
dc.subject.keywordAuthor | Intrusion detection | - |
dc.subject.keywordAuthor | Scoring | - |
dc.subject.keywordAuthor | Smart factory | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-981-13-5907-1_44 | - |
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