{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T20:02:57Z","timestamp":1758398577218},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T00:00:00Z","timestamp":1703894400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,1]]},"DOI":"10.1007\/s10489-023-05176-1","type":"journal-article","created":{"date-parts":[[2023,12,30]],"date-time":"2023-12-30T02:01:47Z","timestamp":1703901707000},"page":"1179-1217","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A multi-constraint transfer approach with additional auxiliary domains for IoT intrusion detection under unbalanced samples distribution"],"prefix":"10.1007","volume":"54","author":[{"given":"Ruiqi","family":"Liu","sequence":"first","affiliation":[]},{"given":"Wengang","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Jin","family":"Guo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,30]]},"reference":[{"key":"5176_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-023-04449-z","author":"X Xie","year":"2023","unstructured":"Xie X, Li X, Xu L (2023) HaarAE: an unsupervised anomaly detection model for IOT devices based on Haar wavelet transform. Appl Intell. https:\/\/doi.org\/10.1007\/s10489-023-04449-z","journal-title":"Appl Intell"},{"key":"5176_CR2","doi-asserted-by":"publisher","first-page":"9320","DOI":"10.1007\/s10489-021-02848-8","volume":"52","author":"M Sadiq","year":"2022","unstructured":"Sadiq M, Shi D, Liang J (2022) A robust occlusion-adaptive attention-based deep network for facial landmark detection. Appl Intell 52:9320\u20139333. https:\/\/doi.org\/10.1007\/s10489-021-02848-8","journal-title":"Appl Intell"},{"key":"5176_CR3","doi-asserted-by":"publisher","first-page":"116748","DOI":"10.1016\/j.eswa.2022.116748","volume":"197","author":"H Polat","year":"2022","unstructured":"Polat H, T\u00fcrkolu M, Polat O (2022) A novel approach for accurate detection of the DDoS attacks in SDN-based SCADA systems based on deep recurrent neural networks. Expert Syst Appl 197:116748. https:\/\/doi.org\/10.1016\/j.eswa.2022.116748","journal-title":"Expert Syst Appl"},{"key":"5176_CR4","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.jpdc.2022.01.030","volume":"164","author":"R Kumar","year":"2022","unstructured":"Kumar R, Kumar P, Tripathi R (2022) A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network. J Parallel Distrib Comput 164:55\u201368. https:\/\/doi.org\/10.1016\/j.jpdc.2022.01.030","journal-title":"J Parallel Distrib Comput"},{"key":"5176_CR5","doi-asserted-by":"publisher","first-page":"103444","DOI":"10.1016\/j.jnca.2022.103444","volume":"205","author":"NM Yungaicela-Naula","year":"2022","unstructured":"Yungaicela-Naula NM, Vargas-Rosales C, P\u00e9rez-D\u00edaz JA, Carrera DF (2022) A flexible SDN-based framework for slow-rate DDoS attack mitigation by using deep reinforcement learning. J Netw Comput Appl 205:103444. https:\/\/doi.org\/10.1016\/j.jnca.2022.103444","journal-title":"J Netw Comput Appl"},{"key":"5176_CR6","doi-asserted-by":"publisher","first-page":"103426","DOI":"10.1016\/j.jnca.2022.103426","volume":"205","author":"S Bokhari","year":"2022","unstructured":"Bokhari S, Hamrioui S, Aider M (2022) Cybersecurity strategy under uncertainties for an IoE environment. J Netw Comput Appl 205:103426. https:\/\/doi.org\/10.1016\/j.jnca.2022.103426","journal-title":"J Netw Comput Appl"},{"issue":"28","key":"5176_CR7","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.neucom.2021.01.095","volume":"438","author":"C Chen","year":"2021","unstructured":"Chen C, Fragonara LZ, Tsourdos A (2021) GAPointNet: graph attention based point neural network for exploiting local feature of point cloud. Neurocomputing 438(28):122\u2013132. https:\/\/doi.org\/10.1016\/j.neucom.2021.01.095","journal-title":"Neurocomputing"},{"key":"5176_CR8","doi-asserted-by":"publisher","first-page":"107446","DOI":"10.1016\/j.patcog.2020.107446","volume":"107","author":"M Feng","year":"2020","unstructured":"Feng M, Zhang L, Lin X, Gilani SZ, Mian A (2020) Point attention network for semantic segmentation of 3D point clouds. Pattern Recogn 107:107446. https:\/\/doi.org\/10.1016\/j.patcog.2020.107446","journal-title":"Pattern Recogn"},{"key":"5176_CR9","doi-asserted-by":"publisher","unstructured":"Al-Haija Q A, Zein-Sabatto S (2020) An efficient deep-learning-based detection and classification system for cyber-attacks in IoT communication networks, multidisciplinary digital publishing institute. 12. https:\/\/doi.org\/10.3390\/electronics9122152","DOI":"10.3390\/electronics9122152"},{"key":"5176_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TMM.2020.3009499","volume":"99","author":"C Chen","year":"2020","unstructured":"Chen C, Qian S, Fang Q (2020) HAPGN: hierarchical attentive pooling graph network for point cloud segmentation. IEEE Trans Multimed 99:1\u20131. https:\/\/doi.org\/10.1109\/TMM.2020.3009499","journal-title":"IEEE Trans Multimed"},{"key":"5176_CR11","doi-asserted-by":"publisher","unstructured":"Mushtaq E, Zameer A, Umer M (2022) A two-stage intrusion detection system with auto-encoder and LSTMs. Appl Soft Comput 121. https:\/\/doi.org\/10.1016\/j.asoc.2022.108768","DOI":"10.1016\/j.asoc.2022.108768"},{"key":"5176_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108295","author":"A Ap","year":"2022","unstructured":"Ap A, Vd B (2022) An intrusion detection approach using ensemble support vector machine based chaos game optimization algorithm in big data platform. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2021.108295","journal-title":"Appl Soft Comput"},{"issue":"1","key":"5176_CR13","doi-asserted-by":"publisher","first-page":"107378","DOI":"10.1016\/j.asoc.2021.107378","volume":"107","author":"H Du","year":"2021","unstructured":"Du H, Zhang Y, Gang K (2021) Online ensemble learning algorithm for imbalanced data stream. Appl Soft Comput 107(1):107378. https:\/\/doi.org\/10.1016\/j.asoc.2021.107378","journal-title":"Appl Soft Comput"},{"issue":"2","key":"5176_CR14","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1109\/TAI.2021.3054609","volume":"1","author":"S Niu","year":"2020","unstructured":"Niu S, Liu Y, Wang J (2020) A decade survey of transfer learning (2010\u20132020). IEEE Trans Artif Intell 1(2):151\u2013166. https:\/\/doi.org\/10.1109\/TAI.2021.3054609","journal-title":"IEEE Trans Artif Intell"},{"issue":"1","key":"5176_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00779-019-01332-y","volume":"2","author":"JH Lee","year":"2019","unstructured":"Lee JH, Park KH (2019) GAN-based imbalanced data intrusion detection system. Pers Ubiquit Comput 2(1):1\u20138. https:\/\/doi.org\/10.1007\/s00779-019-01332-y","journal-title":"Pers Ubiquit Comput"},{"issue":"20","key":"5176_CR16","doi-asserted-by":"publisher","first-page":"4221","DOI":"10.3390\/app9204221","volume":"9","author":"JH Lee","year":"2019","unstructured":"Lee JH, Park KH (2019) AE-CGAN model based high performance network intrusion detection system. Appl Sci 9(20):4221\u20134235. https:\/\/doi.org\/10.3390\/app9204221","journal-title":"Appl Sci"},{"key":"5176_CR17","doi-asserted-by":"publisher","first-page":"116334","DOI":"10.1016\/j.eswa.2021.116334","volume":"192","author":"VF Arruda","year":"2022","unstructured":"Arruda VF, Berriel RF, Paixo TM (2022) Cross-domain object detection using unsupervised image translation. Expert Syst Appl 192:116334. https:\/\/doi.org\/10.1016\/j.eswa.2021.116334","journal-title":"Expert Syst Appl"},{"issue":"13","key":"5176_CR18","first-page":"456","volume":"5","author":"SP Sithungu","year":"2022","unstructured":"Sithungu SP, Ehlers EM (2022) GAAINet: a generative adversarial artificial immune network model for intrusion detection in industrial IoT systems. J Adv Inf Technol 5(13):456\u2013461","journal-title":"J Adv Inf Technol"},{"issue":"8","key":"5176_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107304","volume":"229","author":"AU Hassan","year":"2021","unstructured":"Hassan AU, Ahmed H, Choi J (2021) Unpaired font family synthesis using conditional generative adversarial networks. Knowl-Based Syst 229(8):107304. https:\/\/doi.org\/10.1016\/j.knosys.2021.107304","journal-title":"Knowl-Based Syst"},{"key":"5176_CR20","doi-asserted-by":"publisher","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets, ar Xiv preprint ar Xiv:1411.1784: 1-7. https:\/\/doi.org\/10.48550\/arXiv.1411.1784","DOI":"10.48550\/arXiv.1411.1784"},{"key":"5176_CR21","doi-asserted-by":"publisher","unstructured":"Nagarajan V, Kolter JZ (2017) Gradient descent gan optimization is locally stable. Advances in Neural Information Processing Systems (Neur IPS), California, pp 5585\u20135595. https:\/\/doi.org\/10.48550\/arXiv.1706.04156","DOI":"10.48550\/arXiv.1706.04156"},{"key":"5176_CR22","doi-asserted-by":"publisher","unstructured":"Yang Y, Fu H, Gao S (2022) Intrusion detection: a model based on the improved vision transformer. Trans Emerg Telecommun Technol 33(9). https:\/\/doi.org\/10.1002\/ett.4522","DOI":"10.1002\/ett.4522"},{"key":"5176_CR23","unstructured":"Phan D, Khoa NH, Hiep H (2021) A deep transfer learning approach for flow-based intrusion detection in SDN-enabled network. The 20th international conference on intelligent software methodologies, tools, and techniques (SOMET 2021)"},{"key":"5176_CR24","doi-asserted-by":"publisher","unstructured":"Aldhyani THH (2022) Performance investigation of principal component analysis for intrusion detection system using different support vector machine kernels. Electronics 11. https:\/\/doi.org\/10.3390\/electronics11213571","DOI":"10.3390\/electronics11213571"},{"issue":"7","key":"5176_CR25","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.3390\/en12071223","volume":"12","author":"JL Gao","year":"2019","unstructured":"Gao JL, Chai SC, Zhang BH (2019) Research on network intrusion detection based on incremental extreme learning machine and adaptive principal component analysis. Energies 12(7):1223. https:\/\/doi.org\/10.3390\/en12071223","journal-title":"Energies"},{"issue":"17","key":"5176_CR26","doi-asserted-by":"publisher","first-page":"6325","DOI":"10.1016\/j.matpr.2021.04.643","volume":"47","author":"GP Dubey","year":"2021","unstructured":"Dubey GP, Bhujade RK (2021) Optimal feature selection for machine learning based intrusion detection system by exploiting attribute dependence. Mater Today 47(17):6325\u20136331. https:\/\/doi.org\/10.1016\/j.matpr.2021.04.643","journal-title":"Mater Today"},{"key":"5176_CR27","doi-asserted-by":"publisher","first-page":"101685.1","DOI":"10.1016\/j.phycom.2022.101685","volume":"52","author":"T Gaber","year":"2022","unstructured":"Gaber T, El-Ghamry A, Hassanien AE (2022) Injection attack detection using machine learning for smart IoT applications. Phys Commun 52:101685.1-101685.14. https:\/\/doi.org\/10.1016\/j.phycom.2022.101685","journal-title":"Phys Commun"},{"key":"5176_CR28","doi-asserted-by":"publisher","unstructured":"Rajpoot V, Agrawal R (2022) ITSA-KNN: Feature selection model based on improved tree-seed algorithm and K-nearest neighbor for network intrusion detection. Adv Data Inf Sci 1\u201313. https:\/\/doi.org\/10.1007\/978-981-16-5689-7_1","DOI":"10.1007\/978-981-16-5689-7_1"},{"key":"5176_CR29","doi-asserted-by":"publisher","unstructured":"Zhang XY, Li J, Zhang DJ (2020) Research on feature selection for cyber intrusion detection in industrial Internet of things. Proceedings of the 2020 international conference on cyberspace innovation of advanced technologies. ACM Press, New York, pp 256\u2013262. https:\/\/doi.org\/10.1145\/3444370.3444581","DOI":"10.1145\/3444370.3444581"},{"key":"5176_CR30","doi-asserted-by":"crossref","unstructured":"Cheng XX, Li W, Xiao Z (2020) Intrusion detection system based on QBSO-FS. Proceedings of 2020 international conference on artificial intelligence and computer engineering (ICAICE). IEEE Press, Piscataway, pp 372\u2013377","DOI":"10.1109\/ICAICE51518.2020.00078"},{"issue":"53370","key":"5176_CR31","doi-asserted-by":"publisher","first-page":"53378","DOI":"10.1109\/ACCESS.2021.3068756","volume":"9","author":"LD Fu","year":"2021","unstructured":"Fu LD, Zhang WB, Tan XB (2021) An algorithm for detection of traffic attribute exceptions based on cluster algorithm in industrial Internet of things. IEEE Access 9(53370):53378. https:\/\/doi.org\/10.1109\/ACCESS.2021.3068756","journal-title":"IEEE Access"},{"issue":"1","key":"5176_CR32","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1109\/TSUSC.2018.2809665","volume":"4","author":"A Azmoodeh","year":"2019","unstructured":"Azmoodeh A, Dehghantanha A, Choo KKR (2019) Robust malware detection for internet of (Battlefield) things devices using deep eigenspace learning. IEEE Trans Sustain Comput 4(1):88\u201395. https:\/\/doi.org\/10.1109\/TSUSC.2018.2809665","journal-title":"IEEE Trans Sustain Comput"},{"key":"5176_CR33","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1016\/j.future.2021.09.025","volume":"127","author":"J Haseeb","year":"2022","unstructured":"Haseeb J, Mansoori M, Hirose Y (2022) Autoencoder-based feature construction for IoT attacks clustering. Future Gener Comput Syst 127:487\u2013502. https:\/\/doi.org\/10.1016\/j.future.2021.09.025","journal-title":"Future Gener Comput Syst"},{"issue":"5","key":"5176_CR34","doi-asserted-by":"publisher","first-page":"8169","DOI":"10.1109\/JIOT.2019.2927379","volume":"6","author":"N Wang","year":"2019","unstructured":"Wang N, Wang P, Alipour-Fanid A (2019) Physical-layer security of 5G wireless networks for IoT: challenges and opportunities. IEEE Internet Things J 6(5):8169\u20138181. https:\/\/doi.org\/10.1109\/JIOT.2019.2927379","journal-title":"IEEE Internet Things J"},{"key":"5176_CR35","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/3424819","author":"A Alferaidi","year":"2022","unstructured":"Alferaidi A, Yadav K, Alharbi Y (2022) Distributed deep CNN-LSTM model for intrusion detection method in IoT-based vehicles. Math Probl Eng. https:\/\/doi.org\/10.1155\/2022\/3424819","journal-title":"Math Probl Eng"},{"key":"5176_CR36","doi-asserted-by":"publisher","unstructured":"Radhakrishnan G, Srinivasan K, Kaneswaran S (2021) A deep-RNN and meta-heuristic feature selection approach for IoT malware detection. Mater Today (7). https:\/\/doi.org\/10.1016\/j.matpr.2021.01.207","DOI":"10.1016\/j.matpr.2021.01.207"},{"issue":"8","key":"5176_CR37","doi-asserted-by":"publisher","first-page":"6247","DOI":"10.1109\/JIOT.2020.3024800","volume":"8","author":"DAP Freitas","year":"2021","unstructured":"Freitas DAP, Kaddoum G, Campelo DR (2021) Intrusion detection for cyber-physical systems using generative adversarial networks in fog environment. IEEE Internet Things J 8(8):6247\u20136256. https:\/\/doi.org\/10.1109\/JIOT.2020.3024800","journal-title":"IEEE Internet Things J"},{"issue":"91","key":"5176_CR38","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1016\/j.future.2020.03.042","volume":"110","author":"N Koroniotis","year":"2020","unstructured":"Koroniotis N, Moustafa N, Sitnikova E (2020) A new network forensic framework based on deep learning for Internet of things networks: a particle deep framework. Futur Gener Comput Syst 110(91):106. https:\/\/doi.org\/10.1016\/j.future.2020.03.042","journal-title":"Futur Gener Comput Syst"},{"key":"5176_CR39","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/j.future.2020.07.020","volume":"113","author":"A Bhuvaneswari","year":"2020","unstructured":"Bhuvaneswari A, Selvakumar S (2020) Anomaly detection framework for Internet of things traffic using vector convolutional deep learning approach in fog environment. Futur Gener Comput Syst 113:255\u2013265. https:\/\/doi.org\/10.1016\/j.future.2020.07.020","journal-title":"Futur Gener Comput Syst"},{"key":"5176_CR40","doi-asserted-by":"publisher","unstructured":"Saharkhizan M, Azmoodeh A, Dehghantanha A,\u00a0Choo K-KR, Parizi RM\u00a0(2020) An ensemble of deep recurrent neural networks for detecting IoT cyber intrusions using network traffic. IEEE Intern Things J 7(9):8852\u20138859. https:\/\/doi.org\/10.1109\/JIOT.2020.2996425","DOI":"10.1109\/JIOT.2020.2996425"},{"key":"5176_CR41","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/j.comcom.2020.07.006","volume":"160","author":"S Liaqat","year":"2020","unstructured":"Liaqat S, Akhunzada A, Shaikh FS (2020) SDN orchestration to combat evolving cyber threats in Internet of medical things (IoMT). Comput Commun 160:697\u2013705. https:\/\/doi.org\/10.1016\/j.comcom.2020.07.006","journal-title":"Comput Commun"},{"key":"5176_CR42","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/j.future.2022.08.011","volume":"138","author":"I Debicha","year":"2023","unstructured":"Debicha I, Bauwens R, Debatty T, Dricot J-M, Kenaza T, Mees W (2023) TAD: Transfer learning-based multi-adversarial detection of evasion attacks against network intrusion detection systems. Futur Gener Comput Syst 138:185\u2013197. https:\/\/doi.org\/10.1016\/j.future.2022.08.011","journal-title":"Futur Gener Comput Syst"},{"key":"5176_CR43","doi-asserted-by":"publisher","first-page":"107810","DOI":"10.1016\/j.compeleceng.2022.107810","volume":"99","author":"T Saba","year":"2022","unstructured":"Saba T, Rehman A, Sadad T (2022) Anomaly-based intrusion detection system for IoT networks through deep learning model. Comput Electr Eng 99:107810. https:\/\/doi.org\/10.1016\/j.compeleceng.2022.107810","journal-title":"Comput Electr Eng"},{"issue":"11","key":"5176_CR44","doi-asserted-by":"publisher","first-page":"7704","DOI":"10.1109\/TII.2020.3025755","volume":"17","author":"M Abdel-Basset","year":"2021","unstructured":"Abdel-Basset M, Chang V, Hawash H (2021) Deep-IFS: intrusion detection approach for industrial Internet of things traffic in fog environment. IEEE Trans Industr Inf 17(11):7704\u201357715. https:\/\/doi.org\/10.1109\/TII.2020.3025755","journal-title":"IEEE Trans Industr Inf"},{"key":"5176_CR45","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.knosys.2022.108505","volume":"23","author":"Y Chen","year":"2022","unstructured":"Chen Y, Lin Q, Ji J (2022) Intrusion detection using multi-objective evolutionary convolutional neural network for Internet of Things in Fog computing. Knowl-Based Syst 23:244. https:\/\/doi.org\/10.1016\/j.knosys.2022.108505","journal-title":"Knowl-Based Syst"},{"issue":"8","key":"5176_CR46","doi-asserted-by":"publisher","first-page":"5790","DOI":"10.1109\/TII.2020.3047675","volume":"17","author":"XK Zhou","year":"2020","unstructured":"Zhou XK, Liang W, Shimizu S (2020) Siamese neural network based few-shot learning for anomaly detection in industrial cyber- physical systems. IEEE Trans Industr Inf 17(8):5790\u20135798. https:\/\/doi.org\/10.1109\/TII.2020.3047675","journal-title":"IEEE Trans Industr Inf"},{"issue":"3","key":"5176_CR47","doi-asserted-by":"publisher","first-page":"790","DOI":"10.1109\/JAS.2020.1003099","volume":"7","author":"H Zhang","year":"2020","unstructured":"Zhang H, Li Y, Lv Z (2020) A real-time and ubiquitous network intrusion detection based on deep belief network and support vector machine. IEEE\/CAA J Autom Sin 7(3):790\u2013799. https:\/\/doi.org\/10.1109\/JAS.2020.1003099","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"5176_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.03.060","author":"X Kan","year":"2021","unstructured":"Kan X, Fan Y, Fang Z, Cao L, Li X (2021) A novel IoT network intrusion detection approach based on adaptive particle swarm optimization convolutional neural network. Inf Sci. https:\/\/doi.org\/10.1016\/j.ins.2021.03.060","journal-title":"Inf Sci"},{"issue":"2","key":"5176_CR49","doi-asserted-by":"publisher","first-page":"100391","DOI":"10.1016\/j.iot.2021.100391","volume":"14","author":"E Tsogbaatar","year":"2021","unstructured":"Tsogbaatar E (2021) DeL-IoT: a deep ensemble learning approach to uncover anomalies in IoT. Internet of Things 14(2):100391. https:\/\/doi.org\/10.1016\/j.iot.2021.100391","journal-title":"Internet of Things"},{"key":"5176_CR50","doi-asserted-by":"publisher","first-page":"1077571","DOI":"10.1016\/j.knosys.2021.107757","volume":"236","author":"Y Yao","year":"2022","unstructured":"Yao Y, Ma J, Ye Y (2022) KfreqGAN: Unsupervised detection of sequence anomaly with adversarial learning and frequency domain information. Knowl-Based Syst 236:1077571\u201310775713. https:\/\/doi.org\/10.1016\/j.knosys.2021.107757","journal-title":"Knowl-Based Syst"},{"key":"5176_CR51","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1016\/j.eswa.2017.09.030","volume":"91","author":"G Douzas","year":"2018","unstructured":"Douzas G, Bacao F (2018) Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst Appl 91:464\u2013471. https:\/\/doi.org\/10.1016\/j.eswa.2017.09.030","journal-title":"Expert Syst Appl"},{"key":"5176_CR52","doi-asserted-by":"publisher","unstructured":"Ngo CP, Winarto AA, Li CKK (2019) Fence GAN: towards better anomaly detection, ar Xiv preprint ar Xiv:1904.01209: 1-13. https:\/\/doi.org\/10.1109\/ICTAI.2019.00028","DOI":"10.1109\/ICTAI.2019.00028"},{"key":"5176_CR53","doi-asserted-by":"publisher","unstructured":"Zhang H, Yu X, Ren P (2019) Deep adversarial learning in intrusion detection: a data augmentation enhanced framework, ar Xiv Preprint ar Xiv:1901.07949: 1-10. https:\/\/doi.org\/10.13140\/RG.2.2.19731.73762","DOI":"10.13140\/RG.2.2.19731.73762"},{"issue":"01","key":"5176_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1142\/S2196888822500257","volume":"10","author":"M Szczepa\u0144ski","year":"2023","unstructured":"Szczepa\u0144ski M, Pawlicki M, Kozik R (2023) The application of deep learning imputation and other advanced methods for handling missing values in network intrusion detection. Vietnam J Comput Sci 10(01):1\u201323. https:\/\/doi.org\/10.1142\/S2196888822500257","journal-title":"Vietnam J Comput Sci"},{"key":"5176_CR55","doi-asserted-by":"publisher","unstructured":"Tang Y, Gu L, Wang L (2021) Deep stacking network for intrusion detection. Sensors 22. https:\/\/doi.org\/10.3390\/s22010025","DOI":"10.3390\/s22010025"},{"key":"5176_CR56","doi-asserted-by":"publisher","unstructured":"Yang J, Liang G, Li B (2021) A deep-learning- and reinforcement-learning-based system for encrypted network malicious traffic detection. Electron Lett 57(9). https:\/\/doi.org\/10.1049\/ell2.12125","DOI":"10.1049\/ell2.12125"},{"key":"5176_CR57","doi-asserted-by":"publisher","unstructured":"Wang Y, Sun G (2021) Oversampling method for intrusion detection based on clustering and instance hardness. J Comput Appl 41(6):1709\u20131714. https:\/\/doi.org\/10.11772\/j.issn.1001-9081.2020091378","DOI":"10.11772\/j.issn.1001-9081.2020091378"},{"key":"5176_CR58","doi-asserted-by":"publisher","unstructured":"Folino F, Folino G, Guarascio M, Pisani FS, Pontieri L (2021) On learning effective ensembles of deep neural networks for intrusion detection. Inf Fusion 72(1):48\u201369. https:\/\/doi.org\/10.1016\/j.inffus.2021.02.007","DOI":"10.1016\/j.inffus.2021.02.007"},{"key":"5176_CR59","doi-asserted-by":"publisher","unstructured":"Singh NB, Singh MM, Sarkar A (2021) A novel wide & deep transfer learning stacked GRU framework for network intrusion detection. J Inf Secur Appl 61. https:\/\/doi.org\/10.1016\/j.jisa.2021.102899","DOI":"10.1016\/j.jisa.2021.102899"},{"key":"5176_CR60","doi-asserted-by":"publisher","unstructured":"Idrissi I, Azizi M, Moussaoui O (2021) Accelerating the update of a DL-based IDS for IoT using deep transfer learning. Indones J Electric Eng Comput Sci (2). https:\/\/doi.org\/10.11591\/IJEECS.V23.I2.PP1059-1067","DOI":"10.11591\/IJEECS.V23.I2.PP1059-1067"},{"key":"5176_CR61","doi-asserted-by":"publisher","unstructured":"Zhu J-Y, Park T, Isola P (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE international conference on computer vision (CVPR), Hawaii, pp 2223\u20132232. https:\/\/doi.org\/10.1109\/ICCV.2017.244","DOI":"10.1109\/ICCV.2017.244"},{"key":"5176_CR62","unstructured":"http:\/\/archive.ics.uci.edu\/ml\/datasets\/detection of IoT botnet intrusions N BaIoT. Accessed\u00a09\/10\/2022"},{"key":"5176_CR63","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1016\/j.future.2019.05.041","volume":"100","author":"N Koroniotis","year":"2019","unstructured":"Koroniotis N, Moustafa N, Sitnikova E (2019) Towards the development of realistic botnet dataset in the Internet of things for network forensic analytics: Bot-IoT dataset. Futur Gener Comput Syst 100:779\u2013796. https:\/\/doi.org\/10.1016\/j.future.2019.05.041","journal-title":"Futur Gener Comput Syst"},{"key":"5176_CR64","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3022862","author":"A Alsaedi","year":"2020","unstructured":"Alsaedi A (2020) TON_IoT telemetry dataset: a new generation dataset of IoT and IIoT for data-driven intrusion detection systems. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2020.3022862","journal-title":"IEEE Access"},{"issue":"1","key":"5176_CR65","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.enganabound.2020.07.012","volume":"119","author":"O Verhnjak","year":"2020","unstructured":"Verhnjak O, Hriber\u0161ek M, Steinmann P, Ravnik J (2020) A novel two-way coupling model for Euler-Lagrange simulations of multiphase flow. Eng Anal Boundary Elem 119(1):119\u2013132. https:\/\/doi.org\/10.1016\/j.enganabound.2020.07.012","journal-title":"Eng Anal Boundary Elem"},{"issue":"4","key":"5176_CR66","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1109\/TC.2020.2992113","volume":"70","author":"MS Ansari","year":"2020","unstructured":"Ansari MS, Cockburn BF, Han J (2020) An improved logarithmic multiplier for energy-efficient neural computing. IEEE Trans Comput 70(4):614\u2013625. https:\/\/doi.org\/10.1109\/TC.2020.2992113","journal-title":"IEEE Trans Comput"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05176-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-023-05176-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-023-05176-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,13]],"date-time":"2024-01-13T10:29:54Z","timestamp":1705141794000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-023-05176-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,30]]},"references-count":66,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,1]]}},"alternative-id":["5176"],"URL":"https:\/\/doi.org\/10.1007\/s10489-023-05176-1","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,30]]},"assertion":[{"value":"13 November 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 December 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interest"}}]}}