{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T06:33:49Z","timestamp":1768977229355,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,1,30]]},"DOI":"10.1145\/3579375.3579376","type":"proceedings-article","created":{"date-parts":[[2023,3,13]],"date-time":"2023-03-13T22:09:58Z","timestamp":1678745398000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Zero Trust Network Intrusion Detection System (NIDS) using Auto Encoder for Attention-based CNN-BiLSTM"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7733-8163","authenticated-orcid":false,"given":"Abeer","family":"Alalmaie","sequence":"first","affiliation":[{"name":"University of Technology Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5748-155X","authenticated-orcid":false,"given":"Priyadarsi","family":"Nanda","sequence":"additional","affiliation":[{"name":"University of Technology Sydney, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8962-540X","authenticated-orcid":false,"given":"Xiangjian","family":"He","sequence":"additional","affiliation":[{"name":"University of Nottingham, China"}]}],"member":"320","published-online":{"date-parts":[[2023,3,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"2015 7th Conference on Information and Knowledge Technology (IKT). 1\u20135. https:\/\/doi.org\/10","author":"Abolhasanzadeh Bahareh","year":"2015","unstructured":"Bahareh Abolhasanzadeh . 2015 . Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features . In 2015 7th Conference on Information and Knowledge Technology (IKT). 1\u20135. https:\/\/doi.org\/10 .1109\/IKT.2015.7288799 10.1109\/IKT.2015.7288799 Bahareh Abolhasanzadeh. 2015. Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features. In 2015 7th Conference on Information and Knowledge Technology (IKT). 1\u20135. https:\/\/doi.org\/10.1109\/IKT.2015.7288799"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1089\/big.2020.0263","article-title":"A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection","volume":"9","author":"Al-Turaiki Isra","year":"2021","unstructured":"Isra Al-Turaiki and Najwa Altwaijry . 2021 . A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection . Big Data 9 , 3 (2021), 233 \u2013 252 . https:\/\/doi.org\/10.1089\/big.2020.0263 10.1089\/big.2020.0263 Isra Al-Turaiki and Najwa Altwaijry. 2021. A Convolutional Neural Network for Improved Anomaly-Based Network Intrusion Detection. Big Data 9, 3 (2021), 233\u2013252. https:\/\/doi.org\/10.1089\/big.2020.0263","journal-title":"Big Data"},{"key":"e_1_3_2_1_3_1","unstructured":"Abeer Alalmaie Priyadarsi Nanda and Xiangjian He. unpublished. Zero Trust-NIDS: Extended Multi-View Approach for Network Trace Anonymization and Auto-Encoder Convolutional Neural Network for Network Intrusion Detection. TrustCom (unpublished).  Abeer Alalmaie Priyadarsi Nanda and Xiangjian He. unpublished. Zero Trust-NIDS: Extended Multi-View Approach for Network Trace Anonymization and Auto-Encoder Convolutional Neural Network for Network Intrusion Detection. TrustCom (unpublished)."},{"key":"e_1_3_2_1_4_1","volume-title":"Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset","author":"Alani M.","unstructured":"Mohammed\u00a0 M. Alani . 2022. Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset . In Intelligent Systems Design and Applications, Ajith Abraham, Niketa Gandhi, Thomas Hanne, Tzung-Pei Hong, Tatiane Nogueira\u00a0Rios, and Weiping Ding (Eds.). Springer International Publishing , Cham , 548\u2013558. Mohammed\u00a0M. Alani. 2022. Implementation-Oriented Feature Selection in UNSW-NB15 Intrusion Detection Dataset. In Intelligent Systems Design and Applications, Ajith Abraham, Niketa Gandhi, Thomas Hanne, Tzung-Pei Hong, Tatiane Nogueira\u00a0Rios, and Weiping Ding (Eds.). Springer International Publishing, Cham, 548\u2013558."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2019.102031"},{"key":"e_1_3_2_1_6_1","volume-title":"Scalable Intrusion Detection with Recurrent Neural Networks. In 2010 Seventh International Conference on Information Technology: New Generations. 919\u2013923","author":"Anyanwu O.","year":"2010","unstructured":"Longy\u00a0 O. Anyanwu , Jared Keengwe , and Gladys\u00a0 A. Arome . 2010 . Scalable Intrusion Detection with Recurrent Neural Networks. In 2010 Seventh International Conference on Information Technology: New Generations. 919\u2013923 . https:\/\/doi.org\/10.1109\/ITNG.2010.45 10.1109\/ITNG.2010.45 Longy\u00a0O. Anyanwu, Jared Keengwe, and Gladys\u00a0A. Arome. 2010. Scalable Intrusion Detection with Recurrent Neural Networks. In 2010 Seventh International Conference on Information Technology: New Generations. 919\u2013923. https:\/\/doi.org\/10.1109\/ITNG.2010.45"},{"key":"e_1_3_2_1_7_1","volume-title":"Network Intrusion Detection Model Based on CNN and GRU. Applied Sciences 12, 9","author":"Cao Bo","year":"2022","unstructured":"Bo Cao , Chenghai Li , Yafei Song , Yueyi Qin , and Chen Chen . 2022. Network Intrusion Detection Model Based on CNN and GRU. Applied Sciences 12, 9 ( 2022 ). https:\/\/doi.org\/10.3390\/app12094184 10.3390\/app12094184 Bo Cao, Chenghai Li, Yafei Song, Yueyi Qin, and Chen Chen. 2022. Network Intrusion Detection Model Based on CNN and GRU. Applied Sciences 12, 9 (2022). https:\/\/doi.org\/10.3390\/app12094184"},{"key":"e_1_3_2_1_8_1","first-page":"222","article-title":"An Intrusion-Detection Model","volume":"13","author":"E.","year":"1987","unstructured":"D.\u00a0 E. DENNING. 1987 . An Intrusion-Detection Model . IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 13 , 2 (1987), 222 \u2013 232 . D.\u00a0E. DENNING. 1987. An Intrusion-Detection Model. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 13, 2 (1987), 222\u2013232.","journal-title":"IEEE TRANSACTIONS ON SOFTWARE ENGINEERING"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","first-page":"2419","DOI":"10.1007\/s12083-020-00986-3","article-title":"Sparse auto encoder driven support vector regression based deep learning model for predicting network intrusions","volume":"14","author":"Devan Preethi","year":"2021","unstructured":"Preethi Devan and Neelu Khare . 2021 . Sparse auto encoder driven support vector regression based deep learning model for predicting network intrusions . Peer-to-Peer Networking and Applications 14 (2021), 2419 \u2013 2429 . Preethi Devan and Neelu Khare. 2021. Sparse auto encoder driven support vector regression based deep learning model for predicting network intrusions. Peer-to-Peer Networking and Applications 14 (2021), 2419\u20132429.","journal-title":"Peer-to-Peer Networking and Applications"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2018.10.416"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDAR.2015.7333894"},{"key":"#cr-split#-e_1_3_2_1_12_1.1","doi-asserted-by":"crossref","unstructured":"Tarfa Hamed Rozita Dara and Stefan\u00a0C. Kremer. 2017. Chapter 6 - Intrusion Detection in Contemporary Environments. In Computer and Information Security Handbook (Third Edition) (third edition ed.) J.\u00a0R. Vacca (Ed.). Morgan Kaufmann Boston 109-130. https:\/\/doi.org\/10.1016\/B978-0-12-803843-7.00006-5 10.1016\/B978-0-12-803843-7.00006-5","DOI":"10.1016\/B978-0-12-803843-7.00006-5"},{"key":"#cr-split#-e_1_3_2_1_12_1.2","doi-asserted-by":"crossref","unstructured":"Tarfa Hamed Rozita Dara and Stefan\u00a0C. Kremer. 2017. Chapter 6 - Intrusion Detection in Contemporary Environments. In Computer and Information Security Handbook (Third Edition) (third edition ed.) J.\u00a0R. Vacca (Ed.). Morgan Kaufmann Boston 109-130. https:\/\/doi.org\/10.1016\/B978-0-12-803843-7.00006-5","DOI":"10.1016\/B978-0-12-803843-7.00006-5"},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 9th International Conference on Neural Information Processing Systems","author":"Hochreiter Sepp","year":"1996","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1996 . LSTM Can Solve Hard Long Time Lag Problems . In Proceedings of the 9th International Conference on Neural Information Processing Systems ( Denver, Colorado) (NIPS\u201996). MIT Press, Cambridge, MA, USA, 473\u2013479. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1996. LSTM Can Solve Hard Long Time Lag Problems. In Proceedings of the 9th International Conference on Neural Information Processing Systems (Denver, Colorado) (NIPS\u201996). MIT Press, Cambridge, MA, USA, 473\u2013479."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.11591\/ijeecs.v25.i2.pp1151-1158","article-title":"Network intrusion detection system: machine learning approach","volume":"25","author":"Jaradat S.","year":"2022","unstructured":"Ameera\u00a0 S. Jaradat , Malek\u00a0 M. Barhoush , and Rawan\u00a0 S. Bani\u00a0Easa . 2022 . Network intrusion detection system: machine learning approach . Indonesian Journal of Electrical Engineering and Computer Science 25 , 2(2022), 1151 \u2013 1158 . Ameera\u00a0S. Jaradat, Malek\u00a0M. Barhoush, and Rawan\u00a0S. Bani\u00a0Easa. 2022. Network intrusion detection system: machine learning approach. Indonesian Journal of Electrical Engineering and Computer Science 25, 2(2022), 1151\u20131158.","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"key":"e_1_3_2_1_15_1","first-page":"e2","article-title":"A deep learning approach for network intrusion detection system","volume":"3","author":"Javaid Ahmad","year":"2016","unstructured":"Ahmad Javaid , Quamar Niyaz , Weiqing Sun , and Mansoor Alam . 2016 . A deep learning approach for network intrusion detection system . Eai Endorsed Transactions on Security and Safety 3 , 9 (2016), e2 . Ahmad Javaid, Quamar Niyaz, Weiqing Sun, and Mansoor Alam. 2016. A deep learning approach for network intrusion detection system. Eai Endorsed Transactions on Security and Safety 3, 9 (2016), e2.","journal-title":"Eai Endorsed Transactions on Security and Safety"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2973730"},{"key":"e_1_3_2_1_17_1","volume-title":"SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset. 2019 IEEE 13th International Conference on ASIC (ASICON)","author":"Jing Dishan","year":"2019","unstructured":"Dishan Jing and Hai-Bao Chen . 2019 . SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset. 2019 IEEE 13th International Conference on ASIC (ASICON) (2019), 1\u20134. Dishan Jing and Hai-Bao Chen. 2019. SVM Based Network Intrusion Detection for the UNSW-NB15 Dataset. 2019 IEEE 13th International Conference on ASIC (ASICON) (2019), 1\u20134."},{"key":"e_1_3_2_1_18_1","volume-title":"Md.\u00a0Mahbubur Rahman, and Samrat\u00a0Kumar Dey.","author":"Kabir Mohammad\u00a0Humayun","year":"2022","unstructured":"Mohammad\u00a0Humayun Kabir , Md\u00a0Shahriar Rajib , Abu Saleh Md\u00a0Towfiqur Rahman , Md.\u00a0Mahbubur Rahman, and Samrat\u00a0Kumar Dey. 2022 . Network Intrusion Detection Using UNSW-NB15 Dataset : Stacking Machine Learning Based Approach. In 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE) . 1\u20136. https:\/\/doi.org\/10.1109\/ICAEEE54957.2022.9836404 10.1109\/ICAEEE54957.2022.9836404 Mohammad\u00a0Humayun Kabir, Md\u00a0Shahriar Rajib, Abu Saleh Md\u00a0Towfiqur Rahman, Md.\u00a0Mahbubur Rahman, and Samrat\u00a0Kumar Dey. 2022. Network Intrusion Detection Using UNSW-NB15 Dataset: Stacking Machine Learning Based Approach. In 2022 International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE). 1\u20136. https:\/\/doi.org\/10.1109\/ICAEEE54957.2022.9836404"},{"key":"e_1_3_2_1_19_1","volume-title":"International Journal of Recent Technology and Engineering 7 (01","author":"Kanimozhi V.","year":"2019","unstructured":"V. Kanimozhi and Prem Jacob . 2019. UNSW-NB15 dataset feature selection and network intrusion detection using deep learning . International Journal of Recent Technology and Engineering 7 (01 2019 ), 443\u2013446. V. Kanimozhi and Prem Jacob. 2019. UNSW-NB15 dataset feature selection and network intrusion detection using deep learning. International Journal of Recent Technology and Engineering 7 (01 2019), 443\u2013446."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","DOI":"10.1186\/s40537-020-00379-6","article-title":"Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset","volume":"7","author":"Kasongo M.","year":"2020","unstructured":"Sydney\u00a0 M. Kasongo and Yanxia Sun . 2020 . Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset . Journal of Big Data 7 , 105 (2020). https:\/\/doi.org\/10.1186\/s40537-020-00379-6 10.1186\/s40537-020-00379-6 Sydney\u00a0M. Kasongo and Yanxia Sun. 2020. Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSW-NB15 Dataset. Journal of Big Data 7, 105 (2020). https:\/\/doi.org\/10.1186\/s40537-020-00379-6","journal-title":"Journal of Big Data"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","first-page":"51","DOI":"10.4236\/ait.2011.13007","article-title":"Advances in Intrusion Detection System for WLAN","volume":"1","author":"Kaur R.","year":"2011","unstructured":"R. Kaur . 2011 . Advances in Intrusion Detection System for WLAN . Advances in Internet of Things 1 , 3 (2011), 51 \u2013 54 . https:\/\/doi.org\/10.4236\/ait.2011.13007 10.4236\/ait.2011.13007 R. Kaur. 2011. Advances in Intrusion Detection System for WLAN. Advances in Internet of Things 1, 3 (2011), 51\u201354. https:\/\/doi.org\/10.4236\/ait.2011.13007","journal-title":"Advances in Internet of Things"},{"key":"e_1_3_2_1_22_1","volume-title":"Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. In 2016 International Conference on Platform Technology and Service (PlatCon). 1\u20135. https:\/\/doi.org\/10","author":"Kim Jihyun","year":"2016","unstructured":"Jihyun Kim , Jaehyun Kim , Huong\u00a0Le Thi\u00a0Thu , and Howon Kim . 2016 . Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. In 2016 International Conference on Platform Technology and Service (PlatCon). 1\u20135. https:\/\/doi.org\/10 .1109\/PlatCon.2016.7456805 10.1109\/PlatCon.2016.7456805 Jihyun Kim, Jaehyun Kim, Huong\u00a0Le Thi\u00a0Thu, and Howon Kim. 2016. Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. In 2016 International Conference on Platform Technology and Service (PlatCon). 1\u20135. https:\/\/doi.org\/10.1109\/PlatCon.2016.7456805"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"S. Krishnaveni S. Sivamohan S. Sridhar and S. Prabhakaran. 2022. Network intrusion detection based on ensemble classification and feature selection method for cloud computing. Concurrency Computat Pract Exper. 34 11 (2022).  S. Krishnaveni S. Sivamohan S. Sridhar and S. Prabhakaran. 2022. Network intrusion detection based on ensemble classification and feature selection method for cloud computing. Concurrency Computat Pract Exper. 34 11 (2022).","DOI":"10.1002\/cpe.6838"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-019-03008-x"},{"key":"e_1_3_2_1_25_1","first-page":"1","article-title":"IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism","volume":"8","author":"Laghrissi FatimaEzzahra","year":"2021","unstructured":"FatimaEzzahra Laghrissi , Samira Douzi , Khadija Douzi , and Badr Hssina . 2021 . IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism . Journal of Big Data 8 , 1 (2021), 1 \u2013 21 . FatimaEzzahra Laghrissi, Samira Douzi, Khadija Douzi, and Badr Hssina. 2021. IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism. Journal of Big Data 8, 1 (2021), 1\u201321.","journal-title":"Journal of Big Data"},{"key":"e_1_3_2_1_26_1","volume-title":"3-D Human Behavior Understanding Using Generalized TS-LSTM Networks","author":"Lee Inwoong","year":"2021","unstructured":"Inwoong Lee , Doyoung Kim , and Sanghoon Lee . 2021. 3-D Human Behavior Understanding Using Generalized TS-LSTM Networks . IEEE Transactions on Multimedia( 2021 ), 415\u2013428. https:\/\/doi.org\/10.1109\/TMM.2020.2978637 10.1109\/TMM.2020.2978637 Inwoong Lee, Doyoung Kim, and Sanghoon Lee. 2021. 3-D Human Behavior Understanding Using Generalized TS-LSTM Networks. IEEE Transactions on Multimedia(2021), 415\u2013428. https:\/\/doi.org\/10.1109\/TMM.2020.2978637"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3071263"},{"key":"e_1_3_2_1_28_1","volume-title":"Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset. Future Internet 12, 11","author":"Mahfouz Ahmed","year":"2020","unstructured":"Ahmed Mahfouz , Abdullah Abuhussein , Deepak Venugopal , and Sajjan Shiva . 2020. Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset. Future Internet 12, 11 ( 2020 ). https:\/\/doi.org\/10.3390\/fi12110180 10.3390\/fi12110180 Ahmed Mahfouz, Abdullah Abuhussein, Deepak Venugopal, and Sajjan Shiva. 2020. Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset. Future Internet 12, 11 (2020). https:\/\/doi.org\/10.3390\/fi12110180"},{"key":"e_1_3_2_1_29_1","volume-title":"Improving the Performance of Machine Learning-Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset. Computational Intelligence and Neuroscience 2021","author":"Moualla Soulaiman","year":"2021","unstructured":"Soulaiman Moualla , Khaldoun Khorzom , and Assef Jafar . 2021. Improving the Performance of Machine Learning-Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset. Computational Intelligence and Neuroscience 2021 ( 2021 ). https:\/\/doi.org\/10.1155\/2021\/5557577 10.1155\/2021 Soulaiman Moualla, Khaldoun Khorzom, and Assef Jafar. 2021. Improving the Performance of Machine Learning-Based Network Intrusion Detection Systems on the UNSW-NB15 Dataset. Computational Intelligence and Neuroscience 2021 (2021). https:\/\/doi.org\/10.1155\/2021\/5557577"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/65.283931"},{"key":"e_1_3_2_1_32_1","volume-title":"2017 Iranian Conference on Electrical Engineering (ICEE). 1459\u20131464","author":"Naderi N.","year":"2017","unstructured":"N. Naderi and B. Nasersharif . 2017. Multiresolution convolutional neural network for robust speech recognition . In 2017 Iranian Conference on Electrical Engineering (ICEE). 1459\u20131464 . https:\/\/doi.org\/10.1109\/IranianCEE. 2017 .7985272 10.1109\/IranianCEE.2017.7985272 N. Naderi and B. Nasersharif. 2017. Multiresolution convolutional neural network for robust speech recognition. In 2017 Iranian Conference on Electrical Engineering (ICEE). 1459\u20131464. https:\/\/doi.org\/10.1109\/IranianCEE.2017.7985272"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-021-11719-w"},{"key":"e_1_3_2_1_34_1","article-title":"An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition","volume":"17","author":"Nasersharif B.","year":"2021","unstructured":"B. Nasersharif and N. Naderi . 2021 . An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition . Iranian Journal of Electrical and Electronic Engineering 17 , 2(2021). https:\/\/doi.org\/10.22068\/IJEEE.17.2.1563 arXiv:http:\/\/ijeee.iust.ac.ir\/article-1-1563-en.pdf 10.22068\/IJEEE.17.2.1563 B. Nasersharif and N. Naderi. 2021. An Information-Theoretic Discussion of Convolutional Bottleneck Features for Robust Speech Recognition. Iranian Journal of Electrical and Electronic Engineering 17, 2(2021). https:\/\/doi.org\/10.22068\/IJEEE.17.2.1563 arXiv:http:\/\/ijeee.iust.ac.ir\/article-1-1563-en.pdf","journal-title":"Iranian Journal of Electrical and Electronic Engineering"},{"key":"e_1_3_2_1_35_1","volume-title":"2018 28th International Telecommunication Networks and Applications Conference (ITNAC). 1\u20136. https:\/\/doi.org\/10","author":"Roy Bipraneel","year":"2018","unstructured":"Bipraneel Roy and Hon Cheung . 2018 . A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network . In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). 1\u20136. https:\/\/doi.org\/10 .1109\/ATNAC.2018.8615294 10.1109\/ATNAC.2018.8615294 Bipraneel Roy and Hon Cheung. 2018. A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network. In 2018 28th International Telecommunication Networks and Applications Conference (ITNAC). 1\u20136. https:\/\/doi.org\/10.1109\/ATNAC.2018.8615294"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","unstructured":"S. Sangeetha S. Haripriya S.\u00a0G. Mohana\u00a0Priya V. Vaidehi and N. Srinivasan. 2010. Fuzzy Rule-Base Based Intrusion Detection System on Application Layer. In Recent Trends in Network Security and Applications Natarajan Meghanathan Selma Boumerdassi Nabendu Chaki and Dhinaharan Nagamalai (Eds.). Springer Berlin Heidelberg Berlin Heidelberg 27\u201336.  S. Sangeetha S. Haripriya S.\u00a0G. Mohana\u00a0Priya V. Vaidehi and N. Srinivasan. 2010. Fuzzy Rule-Base Based Intrusion Detection System on Application Layer. In Recent Trends in Network Security and Applications Natarajan Meghanathan Selma Boumerdassi Nabendu Chaki and Dhinaharan Nagamalai (Eds.). Springer Berlin Heidelberg Berlin Heidelberg 27\u201336.","DOI":"10.1007\/978-3-642-14478-3_3"},{"key":"e_1_3_2_1_37_1","article-title":"Http Rule Base Intrusion Detection and Prevention System","volume":"8","author":"Sathya D.","year":"2019","unstructured":"D. Sathya and S. Sangeetha . 2019 . Http Rule Base Intrusion Detection and Prevention System . International Journal of Innovative Technology and Exploring Engineering (IJITEE) 8 , 9(2) (2019), 438\u2013441. D. Sathya and S. Sangeetha. 2019. Http Rule Base Intrusion Detection and Prevention System. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 8, 9(2) (2019), 438\u2013441.","journal-title":"International Journal of Innovative Technology and Exploring Engineering (IJITEE)"},{"key":"e_1_3_2_1_38_1","volume-title":"Suresh\u00a0Kumar and Shaheda Akthar","author":"V.","year":"2022","unstructured":"P.\u00a0G.\u00a0 V. Suresh\u00a0Kumar and Shaheda Akthar . 2022 . Execution Improvement of Intrusion Detection System Through Dimensionality Reduction for UNSW-NB15 Information. In Mobile Computing and Sustainable Informatics, Subarna Shakya, Robert Bestak, Ram Palanisamy, and Khaled\u00a0A. Kamel (Eds.). Springer Singapore , Singapore, 385\u2013396. P.\u00a0G.\u00a0V. Suresh\u00a0Kumar and Shaheda Akthar. 2022. Execution Improvement of Intrusion Detection System Through Dimensionality Reduction for UNSW-NB15 Information. In Mobile Computing and Sustainable Informatics, Subarna Shakya, Robert Bestak, Ram Palanisamy, and Khaled\u00a0A. Kamel (Eds.). Springer Singapore, Singapore, 385\u2013396."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2928048"},{"key":"e_1_3_2_1_40_1","volume-title":"2019 IEEE 44th Conference on Local Computer Networks (LCN). 291\u2013299","author":"Tan Mengxuan","year":"2019","unstructured":"Mengxuan Tan , Alfonso Iacovazzi , Ngai- Man\u00a0Man Cheung , and Yuval Elovici . 2019 . A Neural Attention Model for Real-Time Network Intrusion Detection . In 2019 IEEE 44th Conference on Local Computer Networks (LCN). 291\u2013299 . https:\/\/doi.org\/10.1109\/LCN44214.2019.8990890 10.1109\/LCN44214.2019.8990890 Mengxuan Tan, Alfonso Iacovazzi, Ngai-Man\u00a0Man Cheung, and Yuval Elovici. 2019. A Neural Attention Model for Real-Time Network Intrusion Detection. In 2019 IEEE 44th Conference on Local Computer Networks (LCN). 291\u2013299. https:\/\/doi.org\/10.1109\/LCN44214.2019.8990890"},{"key":"e_1_3_2_1_41_1","volume-title":"Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks. In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft).","author":"Tang A","year":"2018","unstructured":"Tuan\u00a0 A Tang , Lotfi Mhamdi , Des McLernon , Syed Ali\u00a0Raza Zaidi , and Mounir Ghogho . 2018 . Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks. In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). Tuan\u00a0A Tang, Lotfi Mhamdi, Des McLernon, Syed Ali\u00a0Raza Zaidi, and Mounir Ghogho. 2018. Deep Recurrent Neural Network for Intrusion Detection in SDN-based Networks. In 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft)."},{"key":"e_1_3_2_1_42_1","volume-title":"2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). 1\u20136. https:\/\/doi.org\/10","author":"Verma Abhishek","year":"2019","unstructured":"Abhishek Verma and Virender Ranga . 2019 . ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things . In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). 1\u20136. https:\/\/doi.org\/10 .1109\/IoT-SIU.2019.8777504 10.1109\/IoT-SIU.2019.8777504 Abhishek Verma and Virender Ranga. 2019. ELNIDS: Ensemble Learning based Network Intrusion Detection System for RPL based Internet of Things. In 2019 4th International Conference on Internet of Things: Smart Innovation and Usages (IoT-SIU). 1\u20136. https:\/\/doi.org\/10.1109\/IoT-SIU.2019.8777504"},{"key":"#cr-split#-e_1_3_2_1_43_1.1","doi-asserted-by":"crossref","unstructured":"Ravi Vinayakumar Kp Soman and Prabaharan Poornachandran. 2017. Applying convolutional neural network for network intrusion detection. 1222-1228. https:\/\/doi.org\/10.1109\/ICACCI.2017.8126009 10.1109\/ICACCI.2017.8126009","DOI":"10.1109\/ICACCI.2017.8126009"},{"key":"#cr-split#-e_1_3_2_1_43_1.2","doi-asserted-by":"crossref","unstructured":"Ravi Vinayakumar Kp Soman and Prabaharan Poornachandran. 2017. Applying convolutional neural network for network intrusion detection. 1222-1228. https:\/\/doi.org\/10.1109\/ICACCI.2017.8126009","DOI":"10.1109\/ICACCI.2017.8126009"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","first-page":"106853","DOI":"10.1016\/j.compag.2022.106853","article-title":"An efficient attention module for instance segmentation network in pest monitoring","volume":"195","author":"Wang Hanxiang","year":"2022","unstructured":"Hanxiang Wang , Yanfen Li , L.\u00a0 Minh Dang , and Hyeonjoon Moon . 2022 . An efficient attention module for instance segmentation network in pest monitoring . Computers and Electronics in Agriculture 195 (2022), 106853 . https:\/\/doi.org\/10.1016\/j.compag.2022.106853 10.1016\/j.compag.2022.106853 Hanxiang Wang, Yanfen Li, L.\u00a0Minh Dang, and Hyeonjoon Moon. 2022. An efficient attention module for instance segmentation network in pest monitoring. Computers and Electronics in Agriculture 195 (2022), 106853. https:\/\/doi.org\/10.1016\/j.compag.2022.106853","journal-title":"Computers and Electronics in Agriculture"},{"key":"e_1_3_2_1_45_1","volume-title":"IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset. arXiv:2203.16365v1","author":"Yin Yuhua","year":"2022","unstructured":"Yuhua Yin , Julian Jang-Jaccard , Wen Xu , Amardeep Singh , Jinting Zhu , Fariza Sabrina , and Jin Kwak . 2022. IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset. arXiv:2203.16365v1 ( 2022 ). Yuhua Yin, Julian Jang-Jaccard, Wen Xu, Amardeep Singh, Jinting Zhu, Fariza Sabrina, and Jin Kwak. 2022. IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset. arXiv:2203.16365v1 (2022)."}],"event":{"name":"ACSW 2023: 2023 Australasian Computer Science Week","location":"Melbourne VIC Australia","acronym":"ACSW 2023"},"container-title":["2023 Australasian Computer Science Week"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579375.3579376","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3579375.3579376","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:40Z","timestamp":1750178800000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3579375.3579376"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":47,"alternative-id":["10.1145\/3579375.3579376","10.1145\/3579375"],"URL":"https:\/\/doi.org\/10.1145\/3579375.3579376","relation":{},"subject":[],"published":{"date-parts":[[2023,1,30]]},"assertion":[{"value":"2023-03-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}