Cloud Hosting

Wang, G. Comparative study on different neural networks for network security situation prediction. Secur. Priv. 4(1), 138. https://doi.org/10.1002/spy2.138 (2021).

Article CAS Google Scholar

Hesselman, C. et al. A responsible internet to increase trust in the digital world. J. Netw. Syst. Manage 28, 882922. https://doi.org/10.1007/s10922-020-09564-7 (2020).

Article Google Scholar

Bhuyan, M. H., Bhattacharyya, D. K. & Kalita, J. K. Network anomaly detection: Methods, systems and tools. IEEE Commun. Surv. Tutor. 16(1), 303336. https://doi.org/10.1109/SURV.2013.052213.00046 (2014).

Article Google Scholar

Tapiador, J. E., Orfila, A., Ribagorda, A. & Ramos, B. Key-recovery attacks on KIDS, a keyed anomaly detection system. IEEE Trans. Dependable Secure Comput. 12(3), 312325. https://doi.org/10.1109/TDSC.2013.39 (2015).

Article Google Scholar

Buczak, A. L. & Guven, E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 11531176. https://doi.org/10.1109/COMST.2015.2494502 (2016).

Article Google Scholar

Mishra, P., Varadharajan, V., Tupakula, U. & Pilli, E. S. A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutor. 21(1), 686728. https://doi.org/10.1109/COMST.2018.2847722 (2019).

Article Google Scholar

Lopez-Martin, M., Carro, B. & Sanchez-Esguevillas, A. Application of deep reinforcement learning to intrusion detection for supervised problems. Expert Syst. Appl. 141, 112963. https://doi.org/10.1016/j.eswa.2019.112963 (2020).

Article Google Scholar

Wang, W., Liu, J., Pitsilis, G. & Zhang, X. Abstracting massive data for lightweight intrusion detection in computer networks. Inf. Sci. 433434, 417430. https://doi.org/10.1016/j.ins.2016.10.023 (2018).

Article MathSciNet ADS Google Scholar

He, J. & Zheng, S.-H. Intrusion detection model with twin support vector machines. J. Shanghai Jiaotong Univ. Sci. 19, 448454. https://doi.org/10.1007/s12204-014-1524-4 (2014).

Article Google Scholar

Lin, S., Ying, K., Lee, C. & Lee, Z. An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection. Appl. Soft Comput. 12(10), 32853290. https://doi.org/10.1016/j.asoc.2012.05.004 (2012).

Article Google Scholar

Shang, W., Li, L., Wan, M. and Zeng, P. Industrial communication intrusion detection algorithm based on improved one-class SVM. 2015 World Congress on Industrial Control Systems Security (WCICSS), London, 2125, (2015). https://doi.org/10.1109/WCICSS.2015.7420317

Khreich, W., Khosravifar, B., Hamou-Lhadj, A. & Talhi, C. An anomaly detection system based on variable N-gram features and one-class SVM. Inf. Softw. Technol. 91, 186197. https://doi.org/10.1016/j.infsof.2017.07.009 (2017).

Article Google Scholar

lvarez, J., Szabo, C. & Falkner, K. Adaptive performance anomaly detection in distributed systems using online SVMs. IEEE Trans. Dependable Secure Comput. 17(5), 928941. https://doi.org/10.1109/TDSC.2018.2821693 (2020).

Article Google Scholar

Teng, S., Wu, N., Zhu, H., Teng, L. & Zhang, W. SVM-DT-based adaptive and collaborative intrusion detection. IEEE/CAA J. Automatica Sinica 5(1), 108118. https://doi.org/10.1109/JAS.2017.7510730 (2018).

Article Google Scholar

Hu, W., Gao, J., Wang, Y., Wu, O. & Maybank, S. Online adaboost-based parameterized methods for dynamic distributed network intrusion detection. IEEE Transact. Cybern. 44(1), 6682. https://doi.org/10.1109/TCYB.2013.2247592 (2014).

Article Google Scholar

Aburomman, A. A. & Ibne Reaz, M. B. A novel SVM-kNN-PSO ensemble method for intrusion detection system. Appl. Soft Comput. 38, 360372. https://doi.org/10.1016/j.asoc.2015.10.011 (2016).

Article Google Scholar

Wu, Y., Lee, W., Xu, Z. & Ni, M. Large-scale and robust intrusion detection model combining improved deep belief network with feature-weighted SVM. IEEE Access 8, 9860098611. https://doi.org/10.1109/ACCESS.2020.2994947 (2020).

Article Google Scholar

Anil, S. and Remya, R. A hybrid method based on genetic algorithm, self-organised feature map, and support vector machine for better network anomaly detection. 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India, 15, (2013). https://doi.org/10.1109/ICCCNT.2013.6726604

Yi, Y., Wu, J. & Xu, W. Incremental SVM based on reserved set for network intrusion detection. Expert Syst. Appl. 38(6), 76987707. https://doi.org/10.1016/j.eswa.2010.12.141 (2011).

Article Google Scholar

Chitrakar, R. & Huang, C. Selection of candidate support vectors in incremental SVM for network intrusion detection. Comput. Secur. 45, 231241. https://doi.org/10.1016/j.cose.2014.06.006 (2014).

Article Google Scholar

Sumaiya Thaseen, I. & Aswani Kumar, C. Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J. King Saud Univ. Comput. Inform. Sci. 29(4), 462472. https://doi.org/10.1016/j.jksuci.2015.12.004 (2017).

Article Google Scholar

Kuang, F. et al. A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection. Soft Comput. 19, 11871199. https://doi.org/10.1007/s00500-014-1332-7 (2015).

Article MATH Google Scholar

Jaber, A. N. & Rehman, S. U. FCMSVM based intrusion detection system for cloud computing environment. Cluster Comput. 23, 32213231. https://doi.org/10.1007/s10586-020-03082-6 (2020).

Article Google Scholar

Safaldin, M., Otair, M. & Abualigah, L. Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. J. Ambient Intell. Human Comput. 12, 15591576. https://doi.org/10.1007/s12652-020-02228-z (2021).

Article Google Scholar

Cheng, C., Bao, L., Bao, C. Network intrusion detection with bat algorithm for synchronization of feature selection and support vector machines. In: Cheng, L., Liu, Q., Ronzhin, A. (eds) Advances in Neural Networks ISNN 2016. ISNN 2016. Lecture Notes in Computer Science(), vol 9719. (Springer, Cham, 2016) https://doi.org/10.1007/978-3-319-40663-3_46

Gauthama Raman, M., Somu, N., Kirthivasan, K., Liscano, R. & Shankar Sriram, V. An efficient intrusion detection system based on hypergraphgenetic algorithm for parameter optimization and feature selection in support vector machine. Knowl.-Based Syst. 134, 112. https://doi.org/10.1016/j.knosys.2017.07.005 (2017).

Article Google Scholar

Kalita, D. J., Singh, V. P., Kumar, V. SVM hyper-parameters optimization using multi-PSO for intrusion detection. Shukla, R., Agrawal, J., Sharma, S., Chaudhari, N., Shukla, K. (eds) Social Networking and Computational Intelligence. Lecture Notes in Networks and Systems, 100. (Springer, Singapore, 2020). https://doi.org/10.1007/978-981-15-2071-6_19

Li, L., Zhang, S., Zhang, Y., Chang, L. and Gu, T. The intrusion detection model based on parallel multi - artificial bee colony and support vector machine. 2019 Eleventh International Conference on Advanced Computational Intelligence (ICACI), Guilin, China, 308313, (2019). https://doi.org/10.1109/ICACI.2019.8778482

Mehmod, T., & Rais, H. B. M. Ant colony optimization and feature selection for intrusion detection. Soh, P., Woo, W., Sulaiman, H., Othman, M., Saat, M. (eds) Advances in Machine Learning and Signal Processing. Lecture notes in electrical engineering, 387, (Springer, Cham, 2016). https://doi.org/10.1007/978-3-319-32213-1_27

Acharya, N. & Singh, S. An IWD-based feature selection method for intrusion detection system. Soft Comput. 22, 44074416. https://doi.org/10.1007/s00500-017-2635-2 (2018).

Article Google Scholar

Li, J., Wang, H. and Yan, B. Application of velocity adaptive shuffled frog leaping bat algorithm in ICS intrusion detection. 2017 29th Chinese Control And Decision Conference (CCDC), Chongqing, 36303635, (2017). https://doi.org/10.1109/CCDC.2017.7979135

Bostani, H. & Sheikhan, M. Hybrid of binary gravitational search algorithm and mutual information for feature selection in intrusion detection systems. Soft. Comput. 21, 23072324. https://doi.org/10.1007/s00500-015-1942-8 (2017).

Article Google Scholar

Kabir, E., Hu, J., Wang, H. & Zhuo, G. A novel statistical technique for intrusion detection systems. Futur. Gener. Comput. Syst. 79, 303318. https://doi.org/10.1016/j.future.2017.01.029 (2018).

Article Google Scholar

Saleh, A. I., Talaat, F. M. & Labib, L. M. A hybrid intrusion detection system (HIDS) based on prioritized k-nearest neighbors and optimized SVM classifiers. Artif. Intell. Rev. 51, 403443. https://doi.org/10.1007/s10462-017-9567-1 (2019).

Article Google Scholar

Nskh, P., Varma, M. N. and Naik, R. R. Principle component analysis based intrusion detection system using support vector machine. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), Bangalore, India, 13441350, (2016). https://doi.org/10.1109/RTEICT.2016.7808050

Wang, H., Xiao, Y. and Long, Y. Research of intrusion detection algorithm based on parallel SVM on spark. 2017 7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), Macau, China, 153156, (2017) https://doi.org/10.1109/ICEIEC.2017.8076533

Khraisat, A. et al. Survey of intrusion detection systems: Techniques, datasets and challenges. Cybersecur 2, 20. https://doi.org/10.1186/s42400-019-0038-7 (2019).

Article Google Scholar

Meng, W., Tischhauser, E. W., Wang, Q., Wang, Y. & Han, J. When intrusion detection meets blockchain technology: A review. IEEE Access 6, 1017910188. https://doi.org/10.1109/ACCESS.2018.2799854 (2018).

Article Google Scholar

Rajagopal, S., Hareesha, K. S., Kundapur, P. P. Feature relevance analysis and feature reduction of UNSW NB-15 using neural networks on MAMLS. Pati, B., Panigrahi, C., Buyya, R., Li, KC. (eds) advanced computing and intelligent engineering. Advances in Intelligent Systems and Computing, 1082. (Springer, Singapore, 2020). https://doi.org/10.1007/978-981-15-1081-6_27

Test, E., Zigic, L. and Kecman, V. Feature ranking using Gini index, scatter ratios, and nonlinear SVM RFE. 2013 Proceedings of IEEE Southeastcon, Jacksonville, FL, USA, 15, (2013). https://doi.org/10.1109/SECON.2013.6567380

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