Print Email Facebook Twitter Anomaly-Based DNN Model for Intrusion Detection in IoT and Model Explanation Title Anomaly-Based DNN Model for Intrusion Detection in IoT and Model Explanation: Explainable Artificial Intelligence Author Sharma, Bhawana (Manipal University Jaipur) Sharma, Lokesh (Manipal University Jaipur) Lal, C. (TU Delft Cyber Security) Contributor Rawat, Sanyog (editor) Kumar, Sandeep (editor) Kumar, Pramod (editor) Anguera, Jaume (editor) Date 2023 Abstract IoT has gained immense popularity recently with advancements in technologies and big data. IoT network is dynamically increasing with the addition of devices, and the big data is generated within the network, making the network vulnerable to attacks. Thus, network security is essential, and an intrusion detection system is needed. In this paper, we proposed a deep learning-based model for detecting intrusions or attacks in IoT networks. We constructed a DNN model, applied a filter method for feature reduction, and tuned the model with different parameters. We also compared the performance of DNN with other machine learning techniques in terms of accuracy, and the proposed DNN model with weight decay of 0.0001 and dropout rate of 0.01 achieved an accuracy of 0.993, and the reduced loss on the NSL-KDD dataset having five classes. DL models are a black box and hard to understand, so we explained the model predictions using LIME. Subject DLDNNDTIntrusion detection system (IDS)KNNLIMEMLSVM To reference this document use: http://resolver.tudelft.nl/uuid:a56315ce-ce7b-4183-9937-77091fb28302 DOI https://doi.org/10.1007/978-981-19-6661-3_28 Publisher Springer, Singapore Embargo date 2023-07-28 ISBN 978-981-19-6660-6 Source Proceedings of 2nd International Conference on Computational Electronics for Wireless Communications - ICCWC 2022 Event 2nd International Conference on Computational Electronics for Wireless Communications, ICCWC 2022, 2022-06-09 → 2022-06-10, Mangalore, India Series Lecture Notes in Networks and Systems, 2367-3370, 554 Bibliographical note Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. Part of collection Institutional Repository Document type conference paper Rights © 2023 Bhawana Sharma, Lokesh Sharma, C. Lal Files PDF 978_981_19_6661_3_28.pdf 543.13 KB Close viewer /islandora/object/uuid:a56315ce-ce7b-4183-9937-77091fb28302/datastream/OBJ/view