{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:15:49Z","timestamp":1777601749188,"version":"3.51.4"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372285"],"award-info":[{"award-number":["62372285"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013105","name":"Shanghai Rising-Star Program","doi-asserted-by":"publisher","award":["22QA1403800"],"award-info":[{"award-number":["22QA1403800"]}],"id":[{"id":"10.13039\/501100013105","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007219","name":"Shanghai Natural Science Foundation","doi-asserted-by":"publisher","award":["20ZR1455900"],"award-info":[{"award-number":["20ZR1455900"]}],"id":[{"id":"10.13039\/100007219","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1936213"],"award-info":[{"award-number":["U1936213"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012247","name":"Program of Shanghai Academic Research Leader","doi-asserted-by":"publisher","award":["21XD1421500"],"award-info":[{"award-number":["21XD1421500"]}],"id":[{"id":"10.13039\/501100012247","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Science and Technology Commission Project","award":["20020500600"],"award-info":[{"award-number":["20020500600"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,6,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Security of computer information can be improved with the use of a network intrusion detection system. Since the network environment is becoming more complex, more and more new methods of attacking the network have emerged, making the original intrusion detection methods ineffective. Increased network activity also causes intrusion detection systems to identify errors more frequently. We suggest a new intrusion detection technique in this research that combines a Convolutional Neural Network (CNN) model with a Bi-directional Long Short-term Memory Network (BiLSTM) model for adding attention mechanisms. We distinguish our model from existing methods in three ways. First, we use the NCR-SMOTE algorithm to resample the dataset. Secondly, we use recursive feature elimination method based on extreme random tree to select features. Thirdly, we improve the profitability and accuracy of predictions by adding attention mechanism to CNN-BiLSTM. This experiment uses UNSW-UB15 dataset composed of real traffic, and the accuracy rate of multi-classification is 84.5$\\%$; the accuracy rate of multi-classification in CSE-IC-IDS2018 dataset reached 98.3$\\%$.<\/jats:p>","DOI":"10.1093\/comjnl\/bxad105","type":"journal-article","created":{"date-parts":[[2023,11,7]],"date-time":"2023-11-07T07:48:44Z","timestamp":1699343324000},"page":"1851-1865","source":"Crossref","is-referenced-by-count":8,"title":["An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model"],"prefix":"10.1093","volume":"67","author":[{"given":"Dingyu","family":"Shou","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"State Grid Digital Technology Holding Co., Ltd. , Beijing 100053, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Cheng","sequence":"additional","affiliation":[{"name":"State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd. , Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaobo","family":"Hu","sequence":"additional","affiliation":[{"name":"State Grid Key Laboratory of Power Industrial Chip Design and Analysis Technology, Beijing Smart-Chip Microelectronics Technology Co., Ltd. , Beijing 100192, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mi","family":"Wen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Shanghai University of Electric Power , Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,11,1]]},"reference":[{"key":"2024062312372034100_ref1","first-page":"1","article-title":"A survey on data-driven network intrusion detection","volume":"54","author":"Dylan","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"2024062312372034100_ref2","first-page":"36","article-title":"Provenance-based intrusion detection systems: a survey","volume":"55","author":"Michael","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"2024062312372034100_ref3","first-page":"401\u2013418","article-title":"SRID: state relation based intrusion detection for false data injection attacks in SCADA","volume-title":"ESORICS","author":"Wang","year":"2014"},{"key":"2024062312372034100_ref4","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/MNET.001.1900480","article-title":"A lightweight and intelligent intrusion detection system for integrated electronic systems","volume":"34","author":"He","year":"2020","journal-title":"IEEE Netw."},{"key":"2024062312372034100_ref5","doi-asserted-by":"crossref","first-page":"629","DOI":"10.3390\/s23020629","article-title":"Survey on exact kNN queries over high-dimensional data space","volume":"23","author":"Ukey","year":"2023","journal-title":"Sensors"},{"key":"2024062312372034100_ref6","doi-asserted-by":"crossref","first-page":"2157","DOI":"10.1109\/TIFS.2021.3050605","article-title":"Random partitioning Forest for point-wise and collective anomaly detection\u2014application to network intrusion detection","volume":"16","author":"Marteau","year":"2021","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"2024062312372034100_ref7","doi-asserted-by":"crossref","first-page":"110626","DOI":"10.1016\/j.knosys.2023.110626","article-title":"DI-NIDS: domain invariant network intrusion detection system","volume":"273","author":"Siamak","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"2024062312372034100_ref8","doi-asserted-by":"crossref","first-page":"102614","DOI":"10.1016\/j.simpat.2022.102614","article-title":"STG2P: a two-stage pipeline model for intrusion detection based on improved LightGBM and K-means","volume":"120","author":"Zhang","year":"2022","journal-title":"Simul. Model. Pract. Theory"},{"key":"2024062312372034100_ref9","doi-asserted-by":"crossref","first-page":"21404","DOI":"10.1109\/ACCESS.2023.3251354","article-title":"HC-DTTSVM: a network intrusion detection method based on decision tree twin support vector machine and hierarchical clustering","volume":"11","author":"Zou","year":"2023","journal-title":"IEEE Access"},{"key":"2024062312372034100_ref10","doi-asserted-by":"crossref","first-page":"103144","DOI":"10.1016\/j.cose.2023.103144","article-title":"An intrusion detection method based on stacked sparse autoencoder and improved gaussian mixture model","volume":"128","author":"Zhang","year":"2023","journal-title":"Comput. Secur."},{"key":"2024062312372034100_ref11","doi-asserted-by":"crossref","first-page":"106041","DOI":"10.1016\/j.engappai.2023.106041","article-title":"Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model","volume":"121","author":"Manuel","year":"2023","journal-title":"Eng. Appl. Artif. Intel."},{"key":"2024062312372034100_ref12","doi-asserted-by":"crossref","first-page":"126660","DOI":"10.1016\/j.energy.2023.126660","article-title":"Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand","volume":"268","author":"Charan","year":"2023","journal-title":"Energy"},{"key":"2024062312372034100_ref13","doi-asserted-by":"crossref","first-page":"127865","DOI":"10.1016\/j.energy.2023.127865","article-title":"Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting","volume":"278","author":"Zhang","year":"2023","journal-title":"Energy"},{"key":"2024062312372034100_ref14","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1109\/TSTE.2022.3148718","article-title":"Bayesian CNN-BiLSTM and vine-GMCM based probabilistic forecasting of hour-ahead wind farm power outputs","volume":"13","author":"Zou","year":"2022","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"2024062312372034100_ref15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1186\/s13634-022-00871-6","article-title":"Intrusion detection system combined enhanced Random Forest with SMOTE algorithm","volume":"2022","author":"Wu","year":"2022","journal-title":"EURASIP J. Adv. Signal Process."},{"key":"2024062312372034100_ref16","doi-asserted-by":"crossref","first-page":"32464","DOI":"10.1109\/ACCESS.2020.2973730","article-title":"Network intrusion detection combined hybrid sampling with deep hierarchical network","volume":"8","author":"Jiang","year":"2020","journal-title":"IEEE Access"},{"key":"2024062312372034100_ref17","first-page":"67","article-title":"CWGAN-DNN: a method of conditional Wasserstein generation against network intrusion detection","volume":"22","author":"He","year":"2021","journal-title":"J. Air Force Eng. Univ. (NATURAL SCIENCE EDITION)"},{"key":"2024062312372034100_ref18","first-page":"17","article-title":"Network intrusion detection method based on FCWGAN and BiLSTM","volume":"2022","author":"Ma","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"2024062312372034100_ref19","doi-asserted-by":"crossref","first-page":"3357","DOI":"10.1007\/s13369-018-3507-5","article-title":"Integrated intrusion detection model using Chi-Square feature selection and Ensemble of Classifiers","volume":"44","author":"Thaseen","year":"2019","journal-title":"Arab. J. Sci. Eng."},{"key":"2024062312372034100_ref20","first-page":"9","article-title":"Deep neural network-based intrusion detection system through PCA","volume":"2022","author":"Shoayee","year":"2022","journal-title":"Math. Probl. Eng."},{"key":"2024062312372034100_ref21","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1155\/2020\/2835023","article-title":"An intrusion detection method based on decision tree-recursive feature elimination in ensemble learning","volume":"2020","author":"Lian","year":"2020","journal-title":"Math. Probl. Eng."},{"key":"2024062312372034100_ref22","doi-asserted-by":"crossref","first-page":"834","DOI":"10.3390\/pr9050834","article-title":"HCRNNIDS: hybrid convolutional recurrent neural network-based network intrusion detection system","volume":"9","author":"Muhammad","year":"2021","journal-title":"Processes"},{"key":"2024062312372034100_ref23","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s40537-021-00448-4","article-title":"Intrusion detection systems using long short-term memory (LSTM)","volume":"8","author":"Laghrissi","year":"2021","journal-title":"J. Big Data"},{"key":"2024062312372034100_ref24","doi-asserted-by":"crossref","first-page":"108768","DOI":"10.1016\/j.asoc.2022.108768","article-title":"A two-stage intrusion detection system with auto-encoder and LSTMs","volume":"121","author":"Earum","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"2024062312372034100_ref25","first-page":"1","article-title":"Intelligent intrusion detection method of industrial internet of things based on CNN-BiLSTM","volume":"2022","author":"Li","year":"2022","journal-title":"Secur. Commun. Netw."},{"key":"2024062312372034100_ref26","doi-asserted-by":"crossref","first-page":"4221","DOI":"10.3390\/app9204221","article-title":"AE-CGAN model based high performance network intrusion detection system","volume":"9","author":"Lee","year":"2019","journal-title":"Appl. Sci."},{"key":"2024062312372034100_ref27","doi-asserted-by":"crossref","first-page":"4184","DOI":"10.3390\/app12094184","article-title":"Network intrusion detection model based on CNN and GRU","volume":"12","author":"Cao","year":"2022","journal-title":"Appl. Sci."},{"key":"2024062312372034100_ref28","doi-asserted-by":"crossref","first-page":"7550","DOI":"10.1109\/ACCESS.2020.3048198","article-title":"Intrusion detection of imbalanced network traffic based on machine learning and deep learning","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"2024062312372034100_ref29","doi-asserted-by":"crossref","first-page":"5790","DOI":"10.1109\/TII.2020.3047675","article-title":"Siamese neural network based few-shot learning for anomaly detection in industrial cyber-physical systems","volume":"17","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Industr. Inform."},{"key":"2024062312372034100_ref30","doi-asserted-by":"crossref","first-page":"3469","DOI":"10.1109\/TII.2020.3022432","article-title":"Variational LSTM enhanced anomaly detection for industrial big data","volume":"17","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Industr. Inform."},{"key":"2024062312372034100_ref31","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/j.ins.2021.05.016","article-title":"Autoencoder-based deep metric learning for network intrusion detection","volume":"569","author":"Giuseppina","year":"2021","journal-title":"Inform. Sci."},{"key":"2024062312372034100_ref32","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.neucom.2018.09.040","article-title":"Deep-FS: a feature selection algorithm for deep Boltzmann machines","volume":"322","author":"Aboozar","year":"2018","journal-title":"Neurocomputing"},{"key":"2024062312372034100_ref33","doi-asserted-by":"crossref","first-page":"1218","DOI":"10.1109\/JSAC.2020.2986618","article-title":"A network function virtualization system for detecting malware in large IoT based networks","volume":"38","author":"Guizani","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"2024062312372034100_ref34","doi-asserted-by":"crossref","first-page":"103160","DOI":"10.1016\/j.jnca.2021.103160","article-title":"A novel hybrid model for intrusion detection systems in SDNs based on CNN and a new regularization technique","volume":"191","author":"Mahmoud","year":"2021","journal-title":"J. Netw. Comput. Appl."},{"key":"2024062312372034100_ref35","doi-asserted-by":"crossref","first-page":"118476","DOI":"10.1016\/j.eswa.2022.118476","article-title":"Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features","volume":"210","author":"Subhash","year":"2022","journal-title":"Exp. Syst. Appl."},{"key":"2024062312372034100_ref36","doi-asserted-by":"crossref","first-page":"109409","DOI":"10.1016\/j.knosys.2022.109409","article-title":"Attention-based aspect sentiment classification using enhanced learning through CNN-BiLSTM networks","volume":"252","author":"Eniafe","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"2024062312372034100_ref37","doi-asserted-by":"crossref","first-page":"137","DOI":"10.26599\/AIR.2022.9150009","article-title":"LWD-3D: lightweight detector based on self-attention for 3D object detection","volume":"1","author":"Yang","year":"2022","journal-title":"CAAI Artif. Intell. Res."},{"key":"2024062312372034100_ref38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.23919\/ICN.2023.0001","article-title":"Denoising enabled channel estimation for underwater acoustic communications: a sparsity-aware model-driven learning approach","volume":"4","author":"Liu","year":"2023","journal-title":"Intell. Converg. Netw."},{"key":"2024062312372034100_ref39","doi-asserted-by":"crossref","first-page":"217","DOI":"10.23919\/ICN.2022.0006","article-title":"Research on throughput prediction of 5G network based on LSTM","volume":"3","author":"Li","year":"2022","journal-title":"Intell. Converg. Netw."},{"key":"2024062312372034100_ref40","doi-asserted-by":"crossref","first-page":"204","DOI":"10.23919\/ICN.2022.0014","article-title":"PointGAT: graph attention networks for 3D object detection","volume":"3","author":"Zhou","year":"2022","journal-title":"Intell. Converg. Netw."},{"key":"2024062312372034100_ref41","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1186\/s40537-021-00544-5","article-title":"IDS-attention: an efficient algorithm for intrusion detection systems using attention mechanism","volume":"8","author":"Laghrissi","year":"2021","journal-title":"J. Big Data"},{"key":"2024062312372034100_ref42","doi-asserted-by":"crossref","first-page":"10880","DOI":"10.1109\/TVT.2021.3106940","article-title":"Anomaly detection for in-vehicle network using CNN-LSTM with attention mechanism","volume":"70","author":"Sun","year":"2021","journal-title":"IEEE Trans. Veh. Technol."},{"key":"2024062312372034100_ref43","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40537-023-00684-w","article-title":"The effect of feature extraction and data sampling on credit card fraud detection","volume":"10","author":"Salekshahrezaee","year":"2023","journal-title":"J. Big Data"},{"key":"2024062312372034100_ref44","first-page":"37","article-title":"Machine learning-enabled IoT security: open issues and challenges under advanced persistent threats","volume":"55","author":"Chen","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"2024062312372034100_ref45","doi-asserted-by":"crossref","first-page":"10733","DOI":"10.1007\/s10462-023-10437-z","article-title":"Zero-day attack detection: a systematic literature review","volume":"56","author":"Ahmad","year":"2023","journal-title":"Artif. Intell. Rev."},{"key":"2024062312372034100_ref46","doi-asserted-by":"crossref","first-page":"13251","DOI":"10.1007\/s00521-021-05952-5","article-title":"A novel approach for APT attack detection based on combined deep learning model","volume":"33","author":"Do","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"2024062312372034100_ref47","first-page":"103516","article-title":"An adaptable deep learning-based intrusion detection system to zero-day attacks","volume":"76","author":"Mahdi","year":"2023","journal-title":"J. Inf. Secur. Appl."},{"key":"2024062312372034100_ref48","first-page":"1\u201337","article-title":"Deep learning for zero-day malware detection and classification: a survey","volume":"56","author":"Fatemeh","year":"2023","journal-title":"ACM Comput. Surv."}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/67\/5\/1851\/58308084\/bxad105.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/67\/5\/1851\/58308084\/bxad105.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,23]],"date-time":"2024-06-23T12:39:10Z","timestamp":1719146350000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/67\/5\/1851\/7335815"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,1]]},"references-count":48,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,11,1]]},"published-print":{"date-parts":[[2024,6,22]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxad105","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,5]]},"published":{"date-parts":[[2023,11,1]]}}}