On IoT intrusion detection based on data augmentation for enhancing learning on unbalanced samples
Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to enhance human well-being. Machine learning-based intrusion detection systems (IDS) have good protection capabilities. However, it is difficult to identify attack information in massive amounts of data, which...
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| Vydáno v: | Future generation computer systems Ročník 133; s. 213 - 227 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.08.2022
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| ISSN: | 0167-739X, 1872-7115 |
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| Abstract | Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to enhance human well-being. Machine learning-based intrusion detection systems (IDS) have good protection capabilities. However, it is difficult to identify attack information in massive amounts of data, which leads to inefficient model detection when faced with insufficient samples for certain types of attacks. In this regard, this paper fuses deep learning methods and statistical ideas to address the problem of minority samples attack detection, and proposes an intrusion detection method for the IoT based on Improved Conditional Variational Autoencoder (ICVAE) and Borderline Synthetic Minority Oversampling Technique (BSM), called ICVAE-BSM. By introducing an auxiliary network into the Conditional Variational Autoencoder (CVAE) to adjust the output probability distribution of the encoder, learning the posterior distribution of different classes of samples, so that the distributions of samples of the same class are concentrated, and the distributions of different classes of samples are scattered in the latent space; then based on BSM, adaptively synthesize the edge latent variables in the latent space of ICVAE, and feed the new synthetic edge latent variables to the ICVAE’s decoder to generate representative new samples to balance the data set. The output of the encoder is connected to the Softmax classifier at last, and the original samples are mixed with the generated samples to fine-tune it to enhance its generalization ability for intrusion detection of minority samples. We use the NSL-KDD data set, CIC-IDS2017 data set and CSE-CIC-IDS2018 data set to simulate and evaluate the model, the experimental results show that the proposed method can more effectively improve the accuracy of IoT attack detection under the condition of unbalanced samples.
•Different from the traditional unsupervised discriminant model dimensionality reduction, the feature extraction is realized through the supervised generative model ICVAE, and the prior information is given to adjust the probability distribution of the data, avoiding the homogeneity in a single mode.•The proposed feature extraction model introduces an auxiliary network, which makes the probability distribution of the encoder have a pointing effect. The posterior distribution of the sample is close to its exclusive distribution, and alleviates KL-vanishing problem during the CVAE training process.•The proposed scheme has obvious boundaries in the potential space distribution after dimensionality reduction. Based on BSM, the boundary examples in the potential space are oversampled and fed to the decoder to balance the data set, avoiding the generation of noise samples and redundant samples to waste resources and affect the model detection efficiency.•The proposed model can effectively detect minority intrusions from unbalanced samples of IoT. |
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| AbstractList | Internet of things (IoT) security is a prerequisite for the rapid development of the IoT to enhance human well-being. Machine learning-based intrusion detection systems (IDS) have good protection capabilities. However, it is difficult to identify attack information in massive amounts of data, which leads to inefficient model detection when faced with insufficient samples for certain types of attacks. In this regard, this paper fuses deep learning methods and statistical ideas to address the problem of minority samples attack detection, and proposes an intrusion detection method for the IoT based on Improved Conditional Variational Autoencoder (ICVAE) and Borderline Synthetic Minority Oversampling Technique (BSM), called ICVAE-BSM. By introducing an auxiliary network into the Conditional Variational Autoencoder (CVAE) to adjust the output probability distribution of the encoder, learning the posterior distribution of different classes of samples, so that the distributions of samples of the same class are concentrated, and the distributions of different classes of samples are scattered in the latent space; then based on BSM, adaptively synthesize the edge latent variables in the latent space of ICVAE, and feed the new synthetic edge latent variables to the ICVAE’s decoder to generate representative new samples to balance the data set. The output of the encoder is connected to the Softmax classifier at last, and the original samples are mixed with the generated samples to fine-tune it to enhance its generalization ability for intrusion detection of minority samples. We use the NSL-KDD data set, CIC-IDS2017 data set and CSE-CIC-IDS2018 data set to simulate and evaluate the model, the experimental results show that the proposed method can more effectively improve the accuracy of IoT attack detection under the condition of unbalanced samples.
•Different from the traditional unsupervised discriminant model dimensionality reduction, the feature extraction is realized through the supervised generative model ICVAE, and the prior information is given to adjust the probability distribution of the data, avoiding the homogeneity in a single mode.•The proposed feature extraction model introduces an auxiliary network, which makes the probability distribution of the encoder have a pointing effect. The posterior distribution of the sample is close to its exclusive distribution, and alleviates KL-vanishing problem during the CVAE training process.•The proposed scheme has obvious boundaries in the potential space distribution after dimensionality reduction. Based on BSM, the boundary examples in the potential space are oversampled and fed to the decoder to balance the data set, avoiding the generation of noise samples and redundant samples to waste resources and affect the model detection efficiency.•The proposed model can effectively detect minority intrusions from unbalanced samples of IoT. |
| Author | Zhang, Ying Liu, Qiang |
| Author_xml | – sequence: 1 givenname: Ying orcidid: 0000-0002-2637-6765 surname: Zhang fullname: Zhang, Ying email: yingzhang@shmtu.edu.cn organization: College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China – sequence: 2 givenname: Qiang surname: Liu fullname: Liu, Qiang organization: Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China |
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| Cites_doi | 10.1016/j.neucom.2015.04.101 10.1016/j.adhoc.2020.102177 10.1016/j.neunet.2020.10.004 10.1016/j.future.2021.07.013 10.1109/CVPR42600.2020.01259 10.1109/CSCloud.2017.39 10.1016/j.jnca.2018.07.013 10.1007/s10489-020-01886-y 10.1016/j.comnet.2018.11.025 10.1007/s10796-020-10031-6 10.1016/j.jnca.2020.102767 10.1016/j.comnet.2020.107183 10.1109/COMST.2020.2986444 10.1109/ACCESS.2020.2977007 10.1016/j.cose.2008.08.003 10.1109/ACCESS.2020.3048198 10.1109/TC.2016.2519914 10.1109/TKDE.2008.239 10.1016/j.asoc.2018.05.049 10.1109/TSMCC.2008.923876 10.1109/TETCI.2017.2772792 10.1016/j.asoc.2019.105980 10.1007/s12652-021-03082-3 10.1109/ACCESS.2019.2903723 10.1016/j.jnca.2018.07.009 10.1109/FG.2017.117 10.1109/TMI.2019.2914656 10.1109/ACCESS.2020.2980937 10.1007/s11042-021-10647-z 10.1109/JSEN.2020.3008177 10.1016/j.comnet.2020.107784 10.1016/j.inffus.2021.02.007 10.1016/j.comnet.2019.107049 10.1613/jair.953 10.3390/s17091967 10.1109/COMST.2015.2494502 10.1016/j.comnet.2020.107315 10.1109/ACCESS.2019.2928048 10.1016/j.eswa.2020.113696 10.1016/j.future.2019.03.043 10.1109/SPW.2018.00013 10.1007/978-3-030-13453-2_12 10.1016/j.eswa.2019.112963 10.1109/LCOMM.2020.3004877 |
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| References | Yang, Zheng, Wu, Yang (b31) 2019; 19 Saadeh, Sleit, Sabri, Almobaideen (b6) 2018; 121 Ambusaidi, He, Nanda, Tan (b11) 2016; 65 Noor, Hassan (b5) 2019; 148 Smiti, Soui (b44) 2020; 22 Hussain, Hussain, Hassan, Hossain (b1) 2020; 22 Suh, Lee, Lukowicz, Lee (b40) 2021; 133 Haibo, Garcia (b47) 2009; 21 Tama, Comuzzi, Rhee (b50) 2019; 7 Sohn, Yan, Lee (b19) 2015 Violettas, Simoglou, Petridou, Mamatas (b23) 2021; 125 A. Chawla, B. Lee, S. Fallon, P. Jacob, Host Based Intrusion Detection System with Combined CNN/RNN Model, in: ECML PKDD 2018 Workshops, 2019, pp. 149–158. Ali, Shamsuddin, Ralescu (b15) 2015 Prasad, Tripathi, Dahal (b54) 2020; 87 S. Liu, Y. Huang, J. Hu, W. Deng, Learning Local Responses of Facial Landmarks with Conditional Variational Auto-Encoder for Face Alignment, in: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2017, 2017, pp. 947–952. Zhang, Li, Wang (b21) 2019; 7 Gamage, Samarabandu (b51) 2020; 169 Asghari, Rahmani, Javadi (b7) 2018; 120 R. Doshi, N. Apthorpe, N. Feamster, Machine Learning DDoS Detection for Consumer Internet of Things Devices, in: 2018 IEEE Security and Privacy Workshops, SPW, 2018, pp. 29–35. Andresini, Appice, Mauro, Loglisci, Malerba (b53) 2020; 8 Karthik, Krishnan (b46) 2021 R.C. Aygun, A.G. Yavuz, Network Anomaly Detection with Stochastically Improved Autoencoder Based Models, in: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing, CSCloud, 2017, pp. 193–198. Ji, Wang, Li, Sun, Jin, Quek (b33) 2020; 24 Folino, Folino, Guarascio, Pisani, Pontieri (b41) 2021; 72 Pesteie, Abolmaesumi, Rohling (b30) 2019; 38 Chawla, Bowyer, Hall, Kegelmeyer (b16) 2002; 16 Liu, Wang, Lin, Liu (b55) 2021; 9 Zhang, Huang, Wu, Li (b37) 2020; 177 Lopez-Martin, Carro, Sanchez-Esguevillas (b27) 2020; 141 Schwartz, Karlinsky, Shtok, Harary, Marder, Feris, Kumar, Giryes, Bronstein (b34) 2018 Zhong, Chen, Wang, Chen, Wang, Li, Yin, Shi, Yang, Li (b42) 2020; 169 Dixit, Verma (b29) 2020; 20 Dehkordy, Rasoolzadegan (b45) 2021 Lopez-Martin, Carro, Sanchez-Esguevillas, Lloret (b32) 2017; 17 Han, Wang, Mao (b17) 2005 Jiong, Zulkernine, Haque (b8) 2008; 38 Moreno-Barea, Jerez, Franco (b38) 2020; 161 Buczak, Guven (b10) 2016; 18 Kingma, Rezende, Mohamed, Welling (b18) 2014; 2 Ge, Syed, Fu, Baig, Robles-Kelly (b22) 2021; 186 Huang, Lei (b36) 2020; 105 Khammassi, Krichen (b52) 2020; 172 Ahmad, Shahid Khan, Wai Shiang, Abdullah, Ahmad (b3) 2020 Bedi, Gupta, Jindal (b14) 2020; 51 A. Li, W. Huang, X. Lan, J. Feng, Z. Li, L. Wang, Boosting few-shot learning with adaptive margin loss, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 12576–12584. Shone, Ngoc, Phai, Shi (b25) 2018; 2 Zhang, Zhang, Chen, Wang (b26) 2021 Rathore, Park (b49) 2018; 72 Policar, Strazar, Zupan (b48) 2019 Abusitta, Bellaiche, Dagenais, Halabi (b43) 2019; 98 Sun, Liu, Li, Liu, Lu, Hao, Chen (b20) 2020; 2020 Saied, Overill, Radzik (b12) 2016; 172 Gamage, Samarabandu (b2) 2020; 169 García-Teodoro, Díaz-Verdejo, Maciá-Fernández, Vázquez (b4) 2009; 28 Yang, Zheng, Wu, Yang, Access (b39) 2020; 8 Violettas (10.1016/j.future.2022.03.007_b23) 2021; 125 Gamage (10.1016/j.future.2022.03.007_b51) 2020; 169 Shone (10.1016/j.future.2022.03.007_b25) 2018; 2 Andresini (10.1016/j.future.2022.03.007_b53) 2020; 8 Sun (10.1016/j.future.2022.03.007_b20) 2020; 2020 10.1016/j.future.2022.03.007_b35 Hussain (10.1016/j.future.2022.03.007_b1) 2020; 22 Liu (10.1016/j.future.2022.03.007_b55) 2021; 9 Ge (10.1016/j.future.2022.03.007_b22) 2021; 186 Chawla (10.1016/j.future.2022.03.007_b16) 2002; 16 Noor (10.1016/j.future.2022.03.007_b5) 2019; 148 Zhang (10.1016/j.future.2022.03.007_b37) 2020; 177 Policar (10.1016/j.future.2022.03.007_b48) 2019 Lopez-Martin (10.1016/j.future.2022.03.007_b32) 2017; 17 10.1016/j.future.2022.03.007_b28 Suh (10.1016/j.future.2022.03.007_b40) 2021; 133 Folino (10.1016/j.future.2022.03.007_b41) 2021; 72 10.1016/j.future.2022.03.007_b24 Gamage (10.1016/j.future.2022.03.007_b2) 2020; 169 García-Teodoro (10.1016/j.future.2022.03.007_b4) 2009; 28 Khammassi (10.1016/j.future.2022.03.007_b52) 2020; 172 Dehkordy (10.1016/j.future.2022.03.007_b45) 2021 Saadeh (10.1016/j.future.2022.03.007_b6) 2018; 121 Asghari (10.1016/j.future.2022.03.007_b7) 2018; 120 Ahmad (10.1016/j.future.2022.03.007_b3) 2020 Zhong (10.1016/j.future.2022.03.007_b42) 2020; 169 Zhang (10.1016/j.future.2022.03.007_b26) 2021 Dixit (10.1016/j.future.2022.03.007_b29) 2020; 20 Yang (10.1016/j.future.2022.03.007_b31) 2019; 19 Schwartz (10.1016/j.future.2022.03.007_b34) 2018 Sohn (10.1016/j.future.2022.03.007_b19) 2015 Huang (10.1016/j.future.2022.03.007_b36) 2020; 105 Buczak (10.1016/j.future.2022.03.007_b10) 2016; 18 Ji (10.1016/j.future.2022.03.007_b33) 2020; 24 Karthik (10.1016/j.future.2022.03.007_b46) 2021 Prasad (10.1016/j.future.2022.03.007_b54) 2020; 87 10.1016/j.future.2022.03.007_b13 Zhang (10.1016/j.future.2022.03.007_b21) 2019; 7 Tama (10.1016/j.future.2022.03.007_b50) 2019; 7 Moreno-Barea (10.1016/j.future.2022.03.007_b38) 2020; 161 Jiong (10.1016/j.future.2022.03.007_b8) 2008; 38 10.1016/j.future.2022.03.007_b9 Pesteie (10.1016/j.future.2022.03.007_b30) 2019; 38 Ali (10.1016/j.future.2022.03.007_b15) 2015 Han (10.1016/j.future.2022.03.007_b17) 2005 Bedi (10.1016/j.future.2022.03.007_b14) 2020; 51 Abusitta (10.1016/j.future.2022.03.007_b43) 2019; 98 Rathore (10.1016/j.future.2022.03.007_b49) 2018; 72 Haibo (10.1016/j.future.2022.03.007_b47) 2009; 21 Yang (10.1016/j.future.2022.03.007_b39) 2020; 8 Kingma (10.1016/j.future.2022.03.007_b18) 2014; 2 Lopez-Martin (10.1016/j.future.2022.03.007_b27) 2020; 141 Smiti (10.1016/j.future.2022.03.007_b44) 2020; 22 Saied (10.1016/j.future.2022.03.007_b12) 2016; 172 Ambusaidi (10.1016/j.future.2022.03.007_b11) 2016; 65 |
| References_xml | – volume: 121 start-page: 1 year: 2018 end-page: 19 ident: b6 article-title: Hierarchical architecture and protocol for mobile object authentication in the context of IoT smart cities publication-title: J. Netw. Comput. Appl. – volume: 21 start-page: 1263 year: 2009 end-page: 1284 ident: b47 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. – volume: 125 start-page: 698 year: 2021 end-page: 714 ident: b23 article-title: A softwarized intrusion detection system for the RPL-based internet of things networks publication-title: Future Gener. Comput. Syst. – volume: 2 start-page: 41 year: 2018 end-page: 50 ident: b25 article-title: A deep learning approach to network intrusion detection publication-title: IEEE Trans. Emerg. Top. Comput. Intell. – volume: 22 start-page: 1686 year: 2020 end-page: 1721 ident: b1 article-title: Machine learning in IoT security: Current solutions and future challenges publication-title: IEEE Commun. Surv. Tutor. – reference: R.C. Aygun, A.G. Yavuz, Network Anomaly Detection with Stochastically Improved Autoencoder Based Models, in: 2017 IEEE 4th International Conference on Cyber Security and Cloud Computing, CSCloud, 2017, pp. 193–198. – volume: 120 start-page: 61 year: 2018 end-page: 77 ident: b7 article-title: Service composition approaches in IoT: A systematic review publication-title: J. Netw. Compu. Appl. – volume: 186 year: 2021 ident: b22 article-title: Towards a deep learning-driven intrusion detection approach for internet of things publication-title: Comput. Netw. – volume: 8 start-page: 42169 year: 2020 end-page: 42184 ident: b39 article-title: Network intrusion detection based on supervised adversarial variational auto-encoder with regularization publication-title: IEEE Access – volume: 169 year: 2020 ident: b51 article-title: Deep learning methods in network intrusion detection: A survey and an objective comparison publication-title: J. Netw. Comput. Appl. – volume: 38 start-page: 2807 year: 2019 end-page: 2820 ident: b30 article-title: Adaptive augmentation of medical data using independently conditional variational auto-encoders publication-title: IEEE Trans. Med. Imaging – volume: 72 start-page: 79 year: 2018 end-page: 89 ident: b49 article-title: Semi-supervised learning based distributed attack detection framework for IoT publication-title: Appl. Soft. Comput. – volume: 16 start-page: 321 year: 2002 end-page: 357 ident: b16 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J. Artif. Intell. Res. – volume: 9 start-page: 7550 year: 2021 end-page: 7563 ident: b55 article-title: Intrusion detection of imbalanced network traffic based on machine learning and deep learning publication-title: IEEE Access – volume: 19 year: 2019 ident: b31 article-title: Improving the classification effectiveness of intrusion detection by using improved conditional variational AutoEncoder and deep neural network publication-title: Sensors-Basel – year: 2020 ident: b3 article-title: Network intrusion detection system: A systematic study of machine learning and deep learning approaches publication-title: T Emerg. Telecommun. T – volume: 72 start-page: 48 year: 2021 end-page: 69 ident: b41 article-title: On learning effective ensembles of deep neural networks for intrusion detection publication-title: Inform. Fusion – volume: 169 year: 2020 ident: b2 article-title: Deep learning methods in network intrusion detection: A survey and an objective comparison publication-title: J. Netw. Comput. Appl. – reference: A. Chawla, B. Lee, S. Fallon, P. Jacob, Host Based Intrusion Detection System with Combined CNN/RNN Model, in: ECML PKDD 2018 Workshops, 2019, pp. 149–158. – reference: A. Li, W. Huang, X. Lan, J. Feng, Z. Li, L. Wang, Boosting few-shot learning with adaptive margin loss, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 12576–12584. – volume: 141 year: 2020 ident: b27 article-title: Application of deep reinforcement learning to intrusion detection for supervised problems publication-title: Expert Syst. Appl. – volume: 7 start-page: 94497 year: 2019 end-page: 94507 ident: b50 article-title: TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system publication-title: IEEE Access – volume: 148 start-page: 283 year: 2019 end-page: 294 ident: b5 article-title: Current research on internet of things (IoT) security: A survey publication-title: Comput. Netw. – volume: 98 start-page: 308 year: 2019 end-page: 318 ident: b43 article-title: A deep learning approach for proactive multi-cloud cooperative intrusion detection system publication-title: Future Gener. Comput. Syst. – volume: 169 year: 2020 ident: b42 article-title: HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning publication-title: Comput. Netw. – year: 2021 ident: b45 article-title: A new machine learning-based method for android malware detection on imbalanced dataset publication-title: Multimed. Tools Appl. – volume: 172 start-page: 385 year: 2016 end-page: 393 ident: b12 article-title: Detection of known and unknown DDoS attacks using artificial neural networks publication-title: Neurocomputing – volume: 2 start-page: 3581 year: 2014 end-page: 3589 ident: b18 article-title: Semi-supervised learning with deep generative models publication-title: Proceedings of the 27th International Conference on Neural Information Processing Systems – volume: 20 start-page: 14337 year: 2020 end-page: 14346 ident: b29 article-title: Intelligent condition-based monitoring of rotary machines with few samples publication-title: IEEE Sens. J. – volume: 133 start-page: 69 year: 2021 end-page: 86 ident: b40 article-title: CEGAN: Classification enhancement generative adversarial networks for unraveling data imbalance problems publication-title: Neural Netw. – volume: 28 start-page: 18 year: 2009 end-page: 28 ident: b4 article-title: Anomaly-based network intrusion detection: Techniques, systems and challenges publication-title: Comput. Secur. – start-page: 3483 year: 2015 end-page: 3491 ident: b19 article-title: Learning structured output representation using deep conditional generative models publication-title: Proceedings of the 28th International Conference on Neural Information Processing Systems - Vol. 2 – volume: 17 year: 2017 ident: b32 article-title: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT publication-title: Sensors (Basel) – volume: 18 start-page: 1153 year: 2016 end-page: 1176 ident: b10 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. – reference: R. Doshi, N. Apthorpe, N. Feamster, Machine Learning DDoS Detection for Consumer Internet of Things Devices, in: 2018 IEEE Security and Privacy Workshops, SPW, 2018, pp. 29–35. – year: 2015 ident: b15 article-title: Classification with class imbalance problem: A review – year: 2019 ident: b48 article-title: OpenTSNE: a modular python library for t-SNE dimensionality reduction and embedding – volume: 177 year: 2020 ident: b37 article-title: An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset publication-title: Comput. Netw. – volume: 172 year: 2020 ident: b52 article-title: A NSGA2-LR wrapper approach for feature selection in network intrusion detection publication-title: Comput. Netw. – volume: 38 start-page: 649 year: 2008 end-page: 659 ident: b8 article-title: Random-forests- based network intrusion detection systems publication-title: IEEE Trans. Syst., Man, Cybern. C (Applications and Reviews) – volume: 8 start-page: 53346 year: 2020 end-page: 53359 ident: b53 article-title: Multi-channel deep feature learning for intrusion detection publication-title: IEEE Access – volume: 24 start-page: 2191 year: 2020 end-page: 2195 ident: b33 article-title: Data-limited modulation classification with a CVAE-enhanced learning model publication-title: IEEE Commun. Lett. – start-page: 878 year: 2005 end-page: 887 ident: b17 article-title: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning publication-title: 2005 International conference on intelligent computing – volume: 87 year: 2020 ident: b54 article-title: An efficient feature selection based Bayesian and rough set approach for intrusion detection publication-title: Appl. Soft Comput. – volume: 7 start-page: 31711 year: 2019 end-page: 31722 ident: b21 article-title: Intrusion detection for IoT based on improved genetic algorithm and deep belief network publication-title: IEEE Access – volume: 105 year: 2020 ident: b36 article-title: IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks publication-title: Ad Hoc Netw. – year: 2021 ident: b46 article-title: Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks publication-title: J. Ambient Intell. Humaniz. Comput. – volume: 65 start-page: 2986 year: 2016 end-page: 2998 ident: b11 article-title: Building an intrusion detection system using a filter-based feature selection algorithm publication-title: IEEE T Comput. – start-page: 1 year: 2021 ident: b26 article-title: Reinforcement learning-based opportunistic routing protocol for underwater acoustic sensor networks publication-title: IEEE Trans. Veh. Technol. – volume: 161 year: 2020 ident: b38 article-title: Improving classification accuracy using data augmentation on small data sets publication-title: Expert Syst. Appl. – volume: 51 start-page: 1133 year: 2020 end-page: 1151 ident: b14 article-title: I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems publication-title: Appl. Intell. – reference: S. Liu, Y. Huang, J. Hu, W. Deng, Learning Local Responses of Facial Landmarks with Conditional Variational Auto-Encoder for Face Alignment, in: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition, FG 2017, 2017, pp. 947–952. – year: 2018 ident: b34 article-title: Delta-encoder: an effective sample synthesis method for few-shot object recognition – volume: 22 start-page: 1067 year: 2020 end-page: 1083 ident: b44 article-title: Bankruptcy prediction using deep learning approach based on borderline SMOTE publication-title: Inf. Syst. Front. – volume: 2020 start-page: 1 year: 2020 end-page: 11 ident: b20 article-title: DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system publication-title: Secur. Commun. Netw. – volume: 2020 start-page: 1 year: 2020 ident: 10.1016/j.future.2022.03.007_b20 article-title: DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system publication-title: Secur. Commun. Netw. – volume: 2 start-page: 3581 year: 2014 ident: 10.1016/j.future.2022.03.007_b18 article-title: Semi-supervised learning with deep generative models – volume: 172 start-page: 385 year: 2016 ident: 10.1016/j.future.2022.03.007_b12 article-title: Detection of known and unknown DDoS attacks using artificial neural networks publication-title: Neurocomputing doi: 10.1016/j.neucom.2015.04.101 – year: 2015 ident: 10.1016/j.future.2022.03.007_b15 – volume: 105 year: 2020 ident: 10.1016/j.future.2022.03.007_b36 article-title: IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks publication-title: Ad Hoc Netw. doi: 10.1016/j.adhoc.2020.102177 – volume: 133 start-page: 69 year: 2021 ident: 10.1016/j.future.2022.03.007_b40 article-title: CEGAN: Classification enhancement generative adversarial networks for unraveling data imbalance problems publication-title: Neural Netw. doi: 10.1016/j.neunet.2020.10.004 – volume: 125 start-page: 698 year: 2021 ident: 10.1016/j.future.2022.03.007_b23 article-title: A softwarized intrusion detection system for the RPL-based internet of things networks publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2021.07.013 – ident: 10.1016/j.future.2022.03.007_b35 doi: 10.1109/CVPR42600.2020.01259 – ident: 10.1016/j.future.2022.03.007_b24 doi: 10.1109/CSCloud.2017.39 – volume: 120 start-page: 61 year: 2018 ident: 10.1016/j.future.2022.03.007_b7 article-title: Service composition approaches in IoT: A systematic review publication-title: J. Netw. Compu. Appl. doi: 10.1016/j.jnca.2018.07.013 – year: 2020 ident: 10.1016/j.future.2022.03.007_b3 article-title: Network intrusion detection system: A systematic study of machine learning and deep learning approaches publication-title: T Emerg. Telecommun. T – volume: 51 start-page: 1133 year: 2020 ident: 10.1016/j.future.2022.03.007_b14 article-title: I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systems publication-title: Appl. Intell. doi: 10.1007/s10489-020-01886-y – volume: 148 start-page: 283 year: 2019 ident: 10.1016/j.future.2022.03.007_b5 article-title: Current research on internet of things (IoT) security: A survey publication-title: Comput. Netw. doi: 10.1016/j.comnet.2018.11.025 – year: 2019 ident: 10.1016/j.future.2022.03.007_b48 – year: 2018 ident: 10.1016/j.future.2022.03.007_b34 – volume: 22 start-page: 1067 year: 2020 ident: 10.1016/j.future.2022.03.007_b44 article-title: Bankruptcy prediction using deep learning approach based on borderline SMOTE publication-title: Inf. Syst. Front. doi: 10.1007/s10796-020-10031-6 – volume: 169 year: 2020 ident: 10.1016/j.future.2022.03.007_b51 article-title: Deep learning methods in network intrusion detection: A survey and an objective comparison publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2020.102767 – volume: 172 year: 2020 ident: 10.1016/j.future.2022.03.007_b52 article-title: A NSGA2-LR wrapper approach for feature selection in network intrusion detection publication-title: Comput. Netw. doi: 10.1016/j.comnet.2020.107183 – volume: 22 start-page: 1686 year: 2020 ident: 10.1016/j.future.2022.03.007_b1 article-title: Machine learning in IoT security: Current solutions and future challenges publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2020.2986444 – volume: 8 start-page: 42169 year: 2020 ident: 10.1016/j.future.2022.03.007_b39 article-title: Network intrusion detection based on supervised adversarial variational auto-encoder with regularization publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2977007 – volume: 28 start-page: 18 year: 2009 ident: 10.1016/j.future.2022.03.007_b4 article-title: Anomaly-based network intrusion detection: Techniques, systems and challenges publication-title: Comput. Secur. doi: 10.1016/j.cose.2008.08.003 – volume: 9 start-page: 7550 year: 2021 ident: 10.1016/j.future.2022.03.007_b55 article-title: Intrusion detection of imbalanced network traffic based on machine learning and deep learning publication-title: IEEE Access doi: 10.1109/ACCESS.2020.3048198 – volume: 65 start-page: 2986 year: 2016 ident: 10.1016/j.future.2022.03.007_b11 article-title: Building an intrusion detection system using a filter-based feature selection algorithm publication-title: IEEE T Comput. doi: 10.1109/TC.2016.2519914 – volume: 21 start-page: 1263 year: 2009 ident: 10.1016/j.future.2022.03.007_b47 article-title: Learning from imbalanced data publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2008.239 – volume: 72 start-page: 79 year: 2018 ident: 10.1016/j.future.2022.03.007_b49 article-title: Semi-supervised learning based distributed attack detection framework for IoT publication-title: Appl. Soft. Comput. doi: 10.1016/j.asoc.2018.05.049 – volume: 38 start-page: 649 year: 2008 ident: 10.1016/j.future.2022.03.007_b8 article-title: Random-forests- based network intrusion detection systems publication-title: IEEE Trans. Syst., Man, Cybern. C (Applications and Reviews) doi: 10.1109/TSMCC.2008.923876 – volume: 2 start-page: 41 year: 2018 ident: 10.1016/j.future.2022.03.007_b25 article-title: A deep learning approach to network intrusion detection publication-title: IEEE Trans. Emerg. Top. Comput. Intell. doi: 10.1109/TETCI.2017.2772792 – volume: 87 year: 2020 ident: 10.1016/j.future.2022.03.007_b54 article-title: An efficient feature selection based Bayesian and rough set approach for intrusion detection publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2019.105980 – start-page: 878 year: 2005 ident: 10.1016/j.future.2022.03.007_b17 article-title: Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning – start-page: 3483 year: 2015 ident: 10.1016/j.future.2022.03.007_b19 article-title: Learning structured output representation using deep conditional generative models – year: 2021 ident: 10.1016/j.future.2022.03.007_b46 article-title: Hybrid random forest and synthetic minority over sampling technique for detecting internet of things attacks publication-title: J. Ambient Intell. Humaniz. Comput. doi: 10.1007/s12652-021-03082-3 – volume: 19 year: 2019 ident: 10.1016/j.future.2022.03.007_b31 article-title: Improving the classification effectiveness of intrusion detection by using improved conditional variational AutoEncoder and deep neural network publication-title: Sensors-Basel – volume: 169 year: 2020 ident: 10.1016/j.future.2022.03.007_b2 article-title: Deep learning methods in network intrusion detection: A survey and an objective comparison publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2020.102767 – volume: 7 start-page: 31711 year: 2019 ident: 10.1016/j.future.2022.03.007_b21 article-title: Intrusion detection for IoT based on improved genetic algorithm and deep belief network publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2903723 – volume: 121 start-page: 1 year: 2018 ident: 10.1016/j.future.2022.03.007_b6 article-title: Hierarchical architecture and protocol for mobile object authentication in the context of IoT smart cities publication-title: J. Netw. Comput. Appl. doi: 10.1016/j.jnca.2018.07.009 – ident: 10.1016/j.future.2022.03.007_b28 doi: 10.1109/FG.2017.117 – volume: 38 start-page: 2807 year: 2019 ident: 10.1016/j.future.2022.03.007_b30 article-title: Adaptive augmentation of medical data using independently conditional variational auto-encoders publication-title: IEEE Trans. Med. Imaging doi: 10.1109/TMI.2019.2914656 – volume: 8 start-page: 53346 year: 2020 ident: 10.1016/j.future.2022.03.007_b53 article-title: Multi-channel deep feature learning for intrusion detection publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2980937 – start-page: 1 year: 2021 ident: 10.1016/j.future.2022.03.007_b26 article-title: Reinforcement learning-based opportunistic routing protocol for underwater acoustic sensor networks publication-title: IEEE Trans. Veh. Technol. – year: 2021 ident: 10.1016/j.future.2022.03.007_b45 article-title: A new machine learning-based method for android malware detection on imbalanced dataset publication-title: Multimed. Tools Appl. doi: 10.1007/s11042-021-10647-z – volume: 20 start-page: 14337 year: 2020 ident: 10.1016/j.future.2022.03.007_b29 article-title: Intelligent condition-based monitoring of rotary machines with few samples publication-title: IEEE Sens. J. doi: 10.1109/JSEN.2020.3008177 – volume: 186 year: 2021 ident: 10.1016/j.future.2022.03.007_b22 article-title: Towards a deep learning-driven intrusion detection approach for internet of things publication-title: Comput. Netw. doi: 10.1016/j.comnet.2020.107784 – volume: 72 start-page: 48 year: 2021 ident: 10.1016/j.future.2022.03.007_b41 article-title: On learning effective ensembles of deep neural networks for intrusion detection publication-title: Inform. Fusion doi: 10.1016/j.inffus.2021.02.007 – volume: 169 year: 2020 ident: 10.1016/j.future.2022.03.007_b42 article-title: HELAD: A novel network anomaly detection model based on heterogeneous ensemble learning publication-title: Comput. Netw. doi: 10.1016/j.comnet.2019.107049 – volume: 16 start-page: 321 year: 2002 ident: 10.1016/j.future.2022.03.007_b16 article-title: SMOTE: Synthetic minority over-sampling technique publication-title: J. Artif. Intell. Res. doi: 10.1613/jair.953 – volume: 17 year: 2017 ident: 10.1016/j.future.2022.03.007_b32 article-title: Conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in IoT publication-title: Sensors (Basel) doi: 10.3390/s17091967 – volume: 18 start-page: 1153 year: 2016 ident: 10.1016/j.future.2022.03.007_b10 article-title: A survey of data mining and machine learning methods for cyber security intrusion detection publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2015.2494502 – volume: 177 year: 2020 ident: 10.1016/j.future.2022.03.007_b37 article-title: An effective convolutional neural network based on SMOTE and Gaussian mixture model for intrusion detection in imbalanced dataset publication-title: Comput. Netw. doi: 10.1016/j.comnet.2020.107315 – volume: 7 start-page: 94497 year: 2019 ident: 10.1016/j.future.2022.03.007_b50 article-title: TSE-IDS: A two-stage classifier ensemble for intelligent anomaly-based intrusion detection system publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2928048 – volume: 161 year: 2020 ident: 10.1016/j.future.2022.03.007_b38 article-title: Improving classification accuracy using data augmentation on small data sets publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2020.113696 – volume: 98 start-page: 308 year: 2019 ident: 10.1016/j.future.2022.03.007_b43 article-title: A deep learning approach for proactive multi-cloud cooperative intrusion detection system publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.03.043 – ident: 10.1016/j.future.2022.03.007_b9 doi: 10.1109/SPW.2018.00013 – ident: 10.1016/j.future.2022.03.007_b13 doi: 10.1007/978-3-030-13453-2_12 – volume: 141 year: 2020 ident: 10.1016/j.future.2022.03.007_b27 article-title: Application of deep reinforcement learning to intrusion detection for supervised problems publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2019.112963 – volume: 24 start-page: 2191 year: 2020 ident: 10.1016/j.future.2022.03.007_b33 article-title: Data-limited modulation classification with a CVAE-enhanced learning model publication-title: IEEE Commun. Lett. doi: 10.1109/LCOMM.2020.3004877 |
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