Research on hybrid intrusion detection based on improved Harris Hawk optimization algorithm

References

• Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2021). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32(1), e4150. https://doi.org/10.1002/ett.v32.1
   Web of Science ®Google Scholar
• Ahmim, A., Derdour, M., & Ferrag, M. A. (2018). An intrusion detection system based on combining probability predictions of a tree of classifiers. International Journal of Communication Systems, 31(9), e3547. https://doi.org/10.1002/dac.v31.9
   Web of Science ®Google Scholar
• Alazzam, H., Sharieh, A., & K. E. Sabri (2020). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Systems with Applications, 148, 113249. https://doi.org/10.1016/j.eswa.2020.113249
   Web of Science ®Google Scholar
• Al-Yaseen, W. L., Othman, Z. A., & Nazri, M. Z. A. (2017). Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system. Expert Systems with Applications, 67, 296–303. https://doi.org/10.1016/j.eswa.2016.09.041
   Web of Science ®Google Scholar
• Alzaqebah, A., Aljarah, I., Al-Kadi, O., & Damaševičius, R. (2022). A modified grey wolf optimization algorithm for an intrusion detection system. Mathematics, 10(6), 999. https://doi.org/10.3390/math10060999
   Web of Science ®Google Scholar
• Alzubi, Q. M., Anbar, M., Sanjalawe, Y., Al-Betar, M. A., & Abdullah, R. (2022). Intrusion detection system based on hybridizing a modified binary grey wolf optimization and particle swarm optimization. Expert Systems with Applications, 204, 117597. https://doi.org/10.1016/j.eswa.2022.117597
   Web of Science ®Google Scholar
• Andresini, G., Appice, A., De Rose, L., & Malerba, D. (2021). GAN augmentation to deal with imbalance in imaging-based intrusion detection. Future Generation Computer Systems, 123, 108–127. https://doi.org/10.1016/j.future.2021.04.017
   Web of Science ®Google Scholar
• Andresini, G., Appice, A., & Malerba, D. (2021). Nearest cluster-based intrusion detection through convolutional neural networks. Knowledge-Based Systems, 216, 106798. https://doi.org/10.1016/j.knosys.2021.106798
   Web of Science ®Google Scholar
• Binbusayyis, A., & Vaiyapuri, T. (2021). Unsupervised deep learning approach for network intrusion detection combining convolutional autoencoder and one-class SVM. Applied Intelligence, 51(10), 7094–7108. https://doi.org/10.1007/s10489-021-02205-9
   Web of Science ®Google Scholar
• Choi, H., Kim, M., Lee, G., & Kim, W. (2019). Unsupervised learning approach for network intrusion detection system using autoencoders. The Journal of Supercomputing, 75(9), 5597–5621. https://doi.org/10.1007/s11227-019-02805-w
   Web of Science ®Google Scholar
• Chuang, P. J., & Wu, D. Y. (2019). Applying deep learning to balancing network intrusion detection datasets. In IEEE 11th International Conference on Advanced Infocomm Technology (ICAIT) (pp. 213–217).
   Google Scholar
• Diao, C., Zhang, D., Liang, W., Li, K. C., Hong, Y., & Gaudiot, J. L. (2022). A novel spatial-temporal multi-scale alignment graph neural network security model for vehicles prediction. IEEE Transactions on Intelligent Transportation Systems, 24(1), 904–914. https://doi.org/10.1109/TITS.2022.3140229
   Web of Science ®Google Scholar
• Gao, X., Shan, C., Hu, C., Niu, Z., & Liu, Z. (2019). An adaptive ensemble machine learning model for intrusion detection. IEEE Access, 7, 82512–82521. https://doi.org/10.1109/Access.6287639
   Google Scholar
• Gavel, S., Raghuvanshi, A. S., & Tiwari, S. (2022). Maximum correlation based mutual information scheme for intrusion detection in the data networks. Expert Systems with Applications, 189, 116089. https://doi.org/10.1016/j.eswa.2021.116089
   Web of Science ®Google Scholar
• Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris Hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028
   Web of Science ®Google Scholar
• Hu, N., Zhang, D., Xie, K., Liang, W., & M. Y. Hsieh (2022). Graph learning-based spatial-temporal graph convolutional neural networks for traffic forecasting. Connection Science, 34(1), 429–448. https://doi.org/10.1080/09540091.2021.2006607
   Web of Science ®Google Scholar
• Huang, S., & Lei, K. (2020). IGAN-IDS: An imbalanced generative adversarial network towards intrusion detection system in ad-hoc networks. Ad Hoc Networks, 105, 102177. https://doi.org/10.1016/j.adhoc.2020.102177
   Web of Science ®Google Scholar
• Ibrahim, R. A., Oliva, D., Ewees, A. A., & Lu, S. (2017). Feature selection based on improved runner-root algorithm using chaotic singer map and opposition-based learning. In Neural information processing (pp. 156–166). Springer International Publishing.
   Google Scholar
• Jiang, K., Wang, W., Wang, A., & Wu, H. (2020). Network intrusion detection combined hybrid sampling with deep hierarchical network. IEEE Access, 8, 32464–32476. https://doi.org/10.1109/Access.6287639
   Google Scholar
• Kanna, P. R., & Santhi, P. (2021). Unified deep learning approach for efficient intrusion detection system using integrated spatial–temporal features. Knowledge-Based Systems, 226, 107132. https://doi.org/10.1016/j.knosys.2021.107132
   Web of Science ®Google Scholar
• Kasongo, S. M., & Sun, Y. (2020). A deep learning method with wrapper based feature extraction for wireless intrusion detection system. Computers & Security, 92, 101752. https://doi.org/10.1016/j.cose.2020.101752
   Web of Science ®Google Scholar
• Khammassi, C., & Krichen, S. (2020). A NSGA2-LR wrapper approach for feature selection in network intrusion detection. Computer Networks, 172, 107183. https://doi.org/10.1016/j.comnet.2020.107183
   Web of Science ®Google Scholar
• Khraisat, A., Gondal, I., Vamplew, P., Kamruzzaman, J., & Alazab, A. (2020). Hybrid intrusion detection system based on the stacking ensemble of C5 decision tree classifier and one class support vector machine. Electronics, 9(1), 173. https://doi.org/10.3390/electronics9010173
   Web of Science ®Google Scholar
• Kunang, Y. N., Nurmaini, S., Stiawan, D., & Suprapto, B. Y. (2021). Attack classification of an intrusion detection system using deep learning and hyperparameter optimization. Journal of Information Security and Applications, 58, 102804. https://doi.org/10.1016/j.jisa.2021.102804
   Google Scholar
• Kunhare, N., Tiwari, R., & Dhar, J. (2020). Particle swarm optimization and feature selection for intrusion detection system. Sādhanā, 45(1), 1–14. https://doi.org/10.1007/s12046-020-1308-5
   Web of Science ®Google Scholar
• Li, X., Chen, W., Zhang, Q., & Wu, L. (2020). Building auto-encoder intrusion detection system based on random forest feature selection. Computers & Security, 95, 101851. https://doi.org/10.1016/j.cose.2020.101851
   Web of Science ®Google Scholar
• Li, X., Yi, P., Wei, W., Jiang, Y., & Tian, L. (2021). LNNLS-KH: A feature selection method for network intrusion detection. Security and Communication Networks, 2021, 1–22. https://doi.org/10.1155/2021/8830431
   Web of Science ®Google Scholar
• Li, Y., Liang, W., Peng, L., Zhang, D., Yang, C., & Li, K. C. (2022). Predicting drug-target interactions via dual-stream graph neural network. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1–11. https://doi.org/10.1109/TCBB.2022.3204188
   Google Scholar
• Li, Y., Ma, R., & Jiao, R. (2015). A hybrid Malicious code detection method based on deep learning. International Journal of Software Engineering and Its Applications, 9(5), 279–288. https://doi.org/10.14257/ijseia
   Google Scholar
• Liang, W., Xie, S., Cai, J., Xu, J., Hu, Y., Xu, Y., & Qiu, M. (2021). Deep neural network security collaborative filtering scheme for service recommendation in intelligent cyber–physical systems. IEEE Internet of Things Journal, 9(22), 22123–22132. https://doi.org/10.1109/JIOT.2021.3086845
   Web of Science ®Google Scholar
• Long, J., Liang, W., Li, K. C., Wei, Y., & Marino, M. D. (2022). A regularized cross-layer ladder network for intrusion detection in industrial internet of things. IEEE Transactions on Industrial Informatics, 19(2), 1747–1755. https://doi.org/10.1109/TII.2022.3204034
   Web of Science ®Google Scholar
• Lopez-Martin, M., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2019). Shallow neural network with kernel approximation for prediction problems in highly demanding data networks. Expert Systems with Applications, 124, 196–208. https://doi.org/10.1016/j.eswa.2019.01.063
   Web of Science ®Google Scholar
• Mohammadi, S., Mirvaziri, H., Ghazizadeh-Ahsaee, M., & Karimipour, H. (2019). Cyber intrusion detection by combined feature selection algorithm. Journal of Information Security and Applications, 44, 80–88. https://doi.org/10.1016/j.jisa.2018.11.007
   Web of Science ®Google Scholar
• Mojtahedi, A., Sorouri, F., Souha, A. N., Molazadeh, A., & Mehr, S. S. (2022). Feature selection-based intrusion detection system using genetic whale optimization algorithm and sample-based classification. arXiv preprint arXiv:2201.00584.
   Google Scholar
• Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In Military Communications and Information Systems Conference (MILCIS) (pp. 1–6).
   Google Scholar
• Nguyen, M. T., & Kim, K. (2020). Genetic convolutional neural network for intrusion detection systems. Future Generation Computer Systems, 113, 418–427. https://doi.org/10.1016/j.future.2020.07.042
   Web of Science ®Google Scholar
• Point, C. (2022). Cyber attack trends: Mid-year report. https://pages.checkpoint.com/cyber-attack-2022-trends.html
   Google Scholar
• Prasad, M., Tripathi, S., & Dahal, K. (2020). Unsupervised feature selection and cluster center initialization based arbitrary shaped clusters for intrusion detection. Computers & Security, 99, 102062. https://doi.org/10.1016/j.cose.2020.102062
   Web of Science ®Google Scholar
• Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1, 108–116. https://doi.org/10.5220/0006639801080116
   Google Scholar
• Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE Access, 7, 53040–53065. https://doi.org/10.1109/Access.6287639
   Google Scholar
• Su, T., Sun, H., Zhu, J., Wang, S., & Li, Y. (2020). BAT: Deep learning methods on network intrusion detection using NSL-KDD dataset. IEEE Access, 8, 29575–29585. https://doi.org/10.1109/Access.6287639
   Google Scholar
• Talita, A., Nataza, O., & Rustam, Z. (2021). Naïve bayes classifier and particle swarm optimization feature selection method for classifying intrusion detection system dataset. Journal of Physics: Conference Series, 1752(1), 012021. https://doi.org/10.1088/1742-6596/1752/1/012021
   Google Scholar
• Tang, C., Luktarhan, N., & Zhao, Y. (2020). An efficient intrusion detection method based on LightGBM and autoencoder. Symmetry, 12(9), 1458. https://doi.org/10.3390/sym12091458
   Google Scholar
• Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA) (pp. 1–6). IEEE.
   Google Scholar
• Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525–41550. https://doi.org/10.1109/Access.6287639
   Google Scholar
• Wang, Z., Liu, Y., He, D., & Chan, S. (2021). Intrusion detection methods based on integrated deep learning model. Computers & Security, 103, 102177. https://doi.org/10.1016/j.cose.2021.102177
   Web of Science ®Google Scholar
• Xu, X., Li, J., Yang, Y., & Shen, F. (2020). Toward effective intrusion detection using log-cosh conditional variational autoencoder. IEEE Internet of Things Journal, 8(8), 6187–6196. https://doi.org/10.1109/JIOT.2020.3034621
   Web of Science ®Google Scholar
• Zhang, X., Yang, F., Hu, Y., Tian, Z., Liu, W., Li, Y., & She, W. (2022). RANet: Network intrusion detection with group-gating convolutional neural network. Journal of Network and Computer Applications, 198, 103266. https://doi.org/10.1016/j.jnca.2021.103266
   Web of Science ®Google Scholar
• Zhang, Z., Li, Y., Dong, H., Gao, H., Jin, Y., & Wang, W. (2021). AESMOTE: Adversarial reinforcement learning with SMOTE for anomaly detection.
   Google Scholar