{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T16:24:57Z","timestamp":1776443097296,"version":"3.51.2"},"reference-count":50,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772327"],"award-info":[{"award-number":["61772327"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["H2019-275"],"award-info":[{"award-number":["H2019-275"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["H2020-216"],"award-info":[{"award-number":["H2020-216"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Grid Gansu Electric Power Company Electric Power Research Institute","award":["61772327"],"award-info":[{"award-number":["61772327"]}]},{"name":"State Grid Gansu Electric Power Company Electric Power Research Institute","award":["H2019-275"],"award-info":[{"award-number":["H2019-275"]}]},{"name":"State Grid Gansu Electric Power Company Electric Power Research Institute","award":["H2020-216"],"award-info":[{"award-number":["H2020-216"]}]},{"name":"Shanghai Engineering Research Center on Big Data Management System","award":["61772327"],"award-info":[{"award-number":["61772327"]}]},{"name":"Shanghai Engineering Research Center on Big Data Management System","award":["H2019-275"],"award-info":[{"award-number":["H2019-275"]}]},{"name":"Shanghai Engineering Research Center on Big Data Management System","award":["H2020-216"],"award-info":[{"award-number":["H2020-216"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2021,4,20]]},"abstract":"<jats:p>Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.<\/jats:p>","DOI":"10.1155\/2021\/6631075","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T21:09:01Z","timestamp":1619039341000},"page":"1-21","source":"Crossref","is-referenced-by-count":21,"title":["An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2071-1366","authenticated-orcid":true,"given":"Guanyu","family":"Lu","sequence":"first","affiliation":[{"name":"College of Computer Technology and Science, Shanghai University of Electric Power, Shanghai 200090, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3862-9803","authenticated-orcid":true,"given":"Xiuxia","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Computer Technology and Science, Shanghai University of Electric Power, Shanghai 200090, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/jsyst.2013.2294120"},{"key":"2","doi-asserted-by":"crossref","article-title":"Cyber Security Issues for Advanced Metering Infrasttructure (AMI)","author":"F. M. Cleveland","DOI":"10.1109\/PES.2008.4596535"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2909807"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.procs.2020.03.330"},{"issue":"4","key":"5","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/JSYST.2013.2257594","article-title":"Intrusion detection in cyber-physical systems: techniques and challenges","volume":"8","author":"S. Han","year":"2014","journal-title":"IEEE Systems Journal"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-019-03008-x"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8824163"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-25975-3_15"},{"key":"9","doi-asserted-by":"crossref","article-title":"Machine learning algorithms in context of intrusion detection","author":"T. Mehmood","DOI":"10.1109\/ICCOINS.2016.7783243"},{"key":"10","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/978-981-15-7530-3_46","article-title":"A deep learning approach for anomaly-based network intrusion detection","volume":"1210","author":"N. Altwaijry","year":"2020","journal-title":"Big Data and Security"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1007\/s10586-017-1117-8"},{"key":"12","doi-asserted-by":"crossref","article-title":"Real-time network intrusion detection system based on deep learning","author":"Y. Dong","DOI":"10.1109\/ICSESS47205.2019.9040718"},{"key":"13","doi-asserted-by":"crossref","article-title":"LSTM for anomaly-based network intrusion detection","author":"S. A. Althubiti","DOI":"10.1109\/ATNAC.2018.8615300"},{"issue":"2","key":"14","doi-asserted-by":"crossref","first-page":"12","DOI":"10.5121\/ijnsa.2010.2202","article-title":"Combining naive bayes and decision tree for adaptive intrusion detection","volume":"2","author":"D. M. Farid","year":"2010","journal-title":"International Journal of Network Security & Its Applications"},{"key":"15","doi-asserted-by":"crossref","article-title":"An anomaly-based intrusion detection system for the smart grid based on cart decision tree","author":"P. I. Radoglou-Grammatikis","DOI":"10.1109\/GIIS.2018.8635743"},{"issue":"4","key":"16","first-page":"746","article-title":"Intrusion detection system using fuzzy Rough set feature selection and modified KNN classifier","volume":"16","author":"B. Senthilnayaki","year":"2019","journal-title":"International Arab Journal of Information Technology"},{"key":"17","doi-asserted-by":"crossref","article-title":"Support vector machine based intrusion detection system with reduced input features for advanced metering infrastructure of smart grid","author":"R. Vijayanand","DOI":"10.1109\/ICACCS.2017.8014590"},{"key":"18","doi-asserted-by":"crossref","article-title":"Hmm-based intrusion detection system for software defined networking","author":"T. Hurley","DOI":"10.1109\/ICMLA.2016.0108"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.09.041"},{"issue":"11","key":"20","doi-asserted-by":"crossref","first-page":"28960","DOI":"10.3390\/s151128960","article-title":"An intrusion detection system based on multi-level clustering for hierarchical wireless sensor networks","volume":"15","author":"B. Ismail","year":"2015","journal-title":"Sensors"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2018.11.010"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1109\/access.2018.2848210"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2933304"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.3028690"},{"key":"25","article-title":"LSTM for SCADA intrusion detection,","author":"J. Gao"},{"key":"26","article-title":"A neural network architecture combining gated recurrent unit (GRU) and support vector machine (SVM) for intrusion detection in network traffic data","author":"A. F. M. Agarap"},{"key":"27","article-title":"A deep learning approach for intrusion detection in internet of things using bi-directional long short-term memory recurrent neural network","author":"B. Roy"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.3390\/sym11040583"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-020-05017-0"},{"issue":"1","key":"30","first-page":"12","article-title":"Generative adversarial networks for attack generation against intrusion detection","volume":"2","author":"Z. Lin","year":"2018","journal-title":"CoRR Abs"},{"key":"31","article-title":"Greedy layer-wise training of deep networks, in Advances in neural information processing systems 19","author":"Y. Bengio"},{"issue":"04","key":"32","first-page":"199","article-title":"Image denoising based on improved stacked sparse denoising autoencoder","volume":"54","author":"H. Ma","year":"2018","journal-title":"Computer Engineering and Applications"},{"key":"33","doi-asserted-by":"crossref","article-title":"Nonlinear dimensionality reduction for intrusion detection using auto-encoder bottleneck features","author":"B. Abolhasanzadeh","DOI":"10.1109\/IKT.2015.7288799"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/8838571"},{"issue":"16","key":"35","doi-asserted-by":"crossref","first-page":"3414","DOI":"10.3390\/app9163414","article-title":"An LSTM-based deep learning approach for classifying malicious Traffic at the packet level","volume":"9","author":"R. Hwang","year":"2019","journal-title":"Applied Sciences"},{"key":"36","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4842-4470-8","volume-title":"Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners","author":"E. Bisong","year":"2019"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.11.044"},{"key":"38","doi-asserted-by":"crossref","article-title":"Deep dictionary learning vs deep belief network vs stacked autoencoder: an empirical analysis","author":"V. Singhal","DOI":"10.1007\/978-3-319-46681-1_41"},{"key":"39","article-title":"Batch normalization: accelerating deep network training by reducing internal covariate shift","author":"S. Ioffe","year":"2015"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93659-8_57"},{"key":"41","doi-asserted-by":"crossref","article-title":"Remote sensing semantic segmentation with convolution neural network using attention mechanism","author":"N. Xianyang","DOI":"10.1109\/ICEMI46757.2019.9101788"},{"key":"42","article-title":"The emerging enernet: convergence of the smart grid with the Internet of Things","author":"L. Shen"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.1109\/tip.2019.2957850"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.1080\/19393555.2015.1125974"},{"key":"45","doi-asserted-by":"crossref","article-title":"UNSW-NB15: A comprehensive dataset for network intrusion detection systems (UNSW-NB15 network dataset)","author":"N. Moustafa","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.1007\/bf01617722"},{"key":"47","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7130868"},{"key":"48","article-title":"Time-related network intrusion detection model: a deep learning method","author":"Y. Lin"},{"key":"49","doi-asserted-by":"crossref","article-title":"A hybrid cloud intrusion detection method based on SDAE and SVM","author":"W. Wang","DOI":"10.1109\/ICICTA49267.2019.00064"},{"key":"50","doi-asserted-by":"crossref","article-title":"A detailed analysis of the KDD CUP 99 data set","author":"M. Tavallaee","DOI":"10.1109\/CISDA.2009.5356528"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6631075.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6631075.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2021\/6631075.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,29]],"date-time":"2024-08-29T00:17:37Z","timestamp":1724890657000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2021\/6631075\/"}},"subtitle":[],"editor":[{"given":"Jinguang","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2021,4,20]]},"references-count":50,"alternative-id":["6631075","6631075"],"URL":"https:\/\/doi.org\/10.1155\/2021\/6631075","relation":{},"ISSN":["1939-0122","1939-0114"],"issn-type":[{"value":"1939-0122","type":"electronic"},{"value":"1939-0114","type":"print"}],"subject":[],"published":{"date-parts":[[2021,4,20]]}}}