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RNN-based Prediction for Network Intrusion Detection
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Publication Year
2020-02-01
Journal
2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
2020 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020, pp.572-574
Keyword
cosine similarityIDSIntrusion detectionLSTMn-gramRNN
Mesh Keyword
Anomaly detection methodsCosine similarityN-gramsNetwork intrusion detectionPrediction modelRegression techniquesScoring functionsSliding Window
All Science Classification Codes (ASJC)
Information Systems and ManagementArtificial IntelligenceComputer Networks and CommunicationsComputer Vision and Pattern RecognitionInformation SystemsSignal Processing
Abstract
We investigate a prediction model using RNN for network intrusion detection in industrial IoT environments. For intrusion detection, we use anomaly detection methods that estimate the next packet, measure and score the distance measurement in real packets to distinguish whether it is a normal packet or an abnormal packet. When the packet was learned in the LSTM model, two-gram and sliding window of N-gram showed the best performance in terms of errors and the performance of the LSTM model was the highest compared with other data mining regression techniques. Finally, cosine similarity was used as a scoring function, and anomaly detection was performed by setting a boundary for cosine similarity that consider as normal packet.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36567
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85084057762&origin=inward
DOI
https://doi.org/10.1109/icaiic48513.2020.9065249
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9046688
Type
Conference
Funding
This research was supported by the MIST(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP(Institute for Information AND communications Technology Planning AND Evaluation) (2015-0-00908)This research was supported by the MIST(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation) (2015-0-00908), and supported by Korea Electric Power Corporation. [Grant number: 18A-013]
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Choi, Youngjune Image
Choi, Youngjune최영준
Department of Software and Computer Engineering
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