872
Views
1
CrossRef citations to date
0
Altmetric
Articles

Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System

, , &
Pages 189-207 | Received 07 Jul 2023, Accepted 14 Oct 2023, Published online: 06 Nov 2023

References

  • Abdulhammed, R., Musafer, H., Alessa, A., Faezipour, M., & Abuzneid, A. (2019). Features dimensionality reduction approaches for machine learning based network intrusion detection. Electronics, 8(3), https://doi.org/10.3390/electronics8030322
  • Ambusaidi, M. A., He, X., Member, S., Nanda, P., Member, S., & Tan, Z. (2016). Building an intrusion detection system using a filter-based feature selection algorithm. IEEE Transactions on Computers, 9340(NOVEMBER 2014), 1–13. https://doi.org/10.1109/TC.2016.2519914
  • Aung, Y. Y., & Min, M. M. (2018). Hybrid Intrusion Detection System using K-means and K-Nearest Neighbors Algorithms. Proc - 17th IEEE/ACIS Int Conf Comput Inf Sci ICIS 2018, pp. 34–38. https://doi.org/10.1109/ICIS.2018.8466537
  • Azizan, A. H., Mostafa, S. A., Mustapha, A., Foozy, C. F. M., Wahab, M. H. A., Mohammed, M. A., & Khalaf, B. A. (2021). A machine learning approach for improving the performance of network intrusion detection systems. Annals of Emerging Technologies in Computing, 5(Special issue 5), 201–208. https://doi.org/10.33166/AETiC.2021.05.025
  • Çavuşoğlu, Ü. (2019). A new hybrid approach for intrusion detection using machine learning methods. Applied Intelligence, 49(7), 2735–2761. https://doi.org/10.1007/s10489-018-01408-x
  • Christiana, A. O., Oluwatimilehin, F. R., & Yakub, S. (2019). Hybridized Huffman algorithm with block truncate coding For image compression. Journal of Computer Science and Control Systems, 12(2), 5–8. [Online]. Available: http://electroinf.uoradea.ro/images/articles/CERCETARE/Reviste/JCSCS/JCSC_V12_N2_oct2019/JCSCS VOL 12 NO 2 OCTOBER 2019 Abikoye_Hybridized.pdf
  • Dey, A. K., Gupta, G. P., & Sahu, S. P. (2023). Hybrid Meta-Heuristic based feature selection mechanism for cyber-attack detection in IoT-enabled networks. Procedia Computer Science, 218, 318–327. https://doi.org/10.1016/j.procs.2023.01.014
  • Gan, X. S., Duanmu, J. S., Wang, J. F., & Cong, W. (2013). Anomaly intrusion detection based on PLS feature extraction and core vector machine. Knowledge-Based Systems, 40, 1–6. https://doi.org/10.1016/j.knosys.2012.09.004
  • Godala, S., & Vaddella, R. P. V. (2020). A study on intrusion detection system in wireless sensor networks. International Journal of Communication Networks and Information Security, 12(1), 127–141.
  • Gu, J., & Lu, S. (2021). An effective intrusion detection approach using SVM with naïve Bayes feature embedding. Computers & Security, 103, 102158. https://doi.org/10.1016/j.cose.2020.102158
  • Gu, J., Wang, L., Wang, H., & Wang, S. (2019). A novel approach to intrusion detection using SVM ensemble with feature augmentation. Computers & Security, 86, 53–62. https://doi.org/10.1016/j.cose.2019.05.022
  • Harish, B. S., & Kumar, S. V. A. (2017). Anomaly based intrusion detection using modified fuzzy clustering. International Journal of Interactive Multimedia and Artificial Intelligence, 4(6), 54. https://doi.org/10.9781/ijimai.2017.05.002
  • Hosseini Bamakan, S. M., Wang, H., Yingjie, T., & Shi, Y. (2016). An effective intrusion detection framework based on MCLP/SVM optimized by time-varying chaos particle swarm optimization. Neurocomputing, 199, 90–102. https://doi.org/10.1016/j.neucom.2016.03.031
  • Hou, Y., Garg, S., Hui, L., Jayakody, D. N. K., Jin, R., & Hossain, M. S. (2020). A data security enhanced access control mechanism in mobile edge computing. IEEE Access, 8, 136119–136130. https://doi.org/10.1109/ACCESS.2020.3011477
  • Ibrahimi, K., & Ouaddane, M. (2017). Management of intrusion detection systems based-KDD99: Analysis with LDA and PCA. Proc - 2017 Int Conf Wirel Networks Mob Commun WINCOM 2017. https://doi.org/10.1109/WINCOM.2017.8238171
  • Li, Y., Wang, J. L., Tian, Z. H., Lu, T. B., & Young, C. (2009). Building lightweight intrusion detection system using wrapper-based feature selection mechanisms. Computers & Security, 28(6), 466–475. https://doi.org/10.1016/j.cose.2009.01.001
  • Moustafa, N., Creech, G., & Slay, J. (2017). Big Data Analytics for Intrusion Detection System: Statistical Decision-Making Using Finite Dirichlet Mixture Models. pp. 127–156. https://doi.org/10.1007/978-3-319-59439-2_5.
  • Moustafa, N., & Slay, J. (2017). A hybrid feature selection for network intrusion detection systems: Central points. pp. 5–13. https://doi.org/10.4225/75/57a84d4fbefbb
  • Mukkamala, S., & Sung, A. H. (2003). Feature selection for intrusion detection with neural networks and support vector machines. Transportation Research Record: Journal of the Transportation Research Board, 1822(1), 33–39. https://doi.org/10.3141/1822-05
  • Peng, K., Leung, V. C. M., & Huang, Q. (2018). Clustering approach based on mini batch Kmeans for intrusion detection system over Big data. IEEE Access, 6(c), 11897–11906. https://doi.org/10.1109/ACCESS.2018.2810267
  • Prasad, M., Tripathi, S., & Dahal, K. (2020). An efficient feature selection based Bayesian and rough set approach for intrusion detection. Applied Soft Computing, 87, 105980. https://doi.org/10.1016/j.asoc.2019.105980
  • Reddy, M. D., & Vijaya Kumar, N. V. (2012). Optimal capacitor placement for loss reduction in distribution systems using fuzzy and harmony search algorithm. ARPN Journal of Engineering and Applied Sciences, 7(1), 15–19.
  • Saheed, Y. K. (2022). Machine learning-based blockchain technology for protection and privacy against intrusion attacks in intelligent transportation systems. In Machine Learning, Blockchain Technologies and Big Data Analytics for IoTs: Methods, Technologies and Applications, IET.
  • Saheed, Y. K. (2022). A binary firefly algorithm based feature selection method on high dimensional intrusion detection data. In S. Misra, & C. Arumugam (Eds.), Illumination of artificial intelligence in cybersecurity and forensics. Lecture notes on data engineering and communications technologies (Vol. 109, pp. 323–341). Springer Cham. https://doi.org/10.1007/978-3-030-93453-8_12
  • Saheed, Y. K., Ayobami, R. M., & Orje-Ishegh, T. (2022a). A comparative study of regression analysis for modelling and prediction of bitcoin price. In S. Misra, & A. Kumar Tyagi (Eds.), Blockchain applications in the smart Era. EAI/springer innovations in communication and computing (pp. 187–210). Springer Cham.
  • Saheed, Y. K., Baba, U. A., & Raji, M. A. (2022b). Big data analytics for credit card fraud detection using supervised machine learning models. In K. Sood, B. Balusamy, S. Grima, & P. Marano (Eds.), Big data analytics in the insurance market (emerald studies in finance, insurance, and risk management) (pp. 31–56). Emerald Publishing Limited.
  • Saheed, Y. K., & Gbolagade, K. A. (2016). Efficient image encryption scheme based on the moduli Set {2n - 1, 2n, 2n +1}. Al-Hikmah Journal of Pure & Applied Sciences, 3, 15–21.
  • Saheed, Y. K., & Gbolagade, K. A. (2017). Efficient RSA cryptosystem decryption based on Chinese remainder theorem and strong prime. Anale SeriaInformatică, XV(2), 1–5.
  • Saheed, Y. K., & Raji, M. (2022). Effectiveness of deep learning long short-term memory network for stock price prediction on graphics processing unit. In 2022 International Conference on Decision Aid Sciences and Applications (DASA), Chiangrai, Thailand, pp. 1665–1671. https://doi.org/10.1109/DASA54658.2022.9765181.
  • Salih, A. A., & Abdulrazaq, M. B. (2019). Combining best features selection using three classifiers in intrusion detection system. 2019 Int Conf Adv Sci Eng ICOASE 2019, pp. 94–99. https://doi.org/10.1109/ICOASE.2019.8723671
  • Smmarwar, S. K., Gupta, G. P., & Kumar, S. (2022). A hybrid feature selection approach-based Android Malware Detection Framework Using Machine Learning Techniques. https://doi.org/10.1007/978-981-16-8664-1_30
  • Sung, A. H., & Mukkamala, S. (2003). Identifying important features for intrusion detection using support vector machines and neural networks department of computer science New Mexico institute of mining and technology. Symp A Q J Mod Foreign Lit, 1822(1), 3–10.
  • Taşpınar, F. (2015). Improving artificial neural network model predictions of daily average PM10 concentrations by applying principle component analysis and implementing seasonal models. Journal of the Air & Waste Management Association, 65(7), 800–809. https://doi.org/10.1080/10962247.2015.1019652
  • Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. IEEE Symp Comput Intell Secur Def Appl CISDA 2009, no. June 2014. https://doi.org/10.1109/CISDA.2009.5356528
  • Valueva, M. V., Nagornov, N. N., Lyakhov, P. A., Valuev, G. V., & Chervyakov, N. I. (2020). Application of the residue number system to reduce hardware costs of the convolutional neural network implementation. Mathematics and Computers in Simulation, 177, 232–243. https://doi.org/10.1016/j.matcom.2020.04.031
  • Wang, H., Gu, J., & Wang, S. (2017). An effective intrusion detection framework based on SVM with feature augmentation. Knowledge-Based Systems, 136, 130–139. https://doi.org/10.1016/j.knosys.2017.09.014
  • Yang, X. S. (2010). A new metaheuristic Bat-inspired algorithm. Studies in Computational Intelligence, 284, 65–74. https://doi.org/10.1007/978-3-642-12538-6_6
  • Yong, J. S., He, F. Z., Li, H. R., & Zhou, W. Q. (2019). A novel Bat algorithm based on cross boundary learning and uniform explosion strategy. Applied Mathematics-A Journal of Chinese Universities, 34(4), 480–502. https://doi.org/10.1007/s11766-019-3714-1
  • Yu, H., Kang, C., Xiao, Y., & Ting, Y. (2023). Network intrusion detection method based on hybrid improved residual network blocks and bidirectional gated recurrent units. IEEE Access, PP, 1. https://doi.org/10.1109/ACCESS.2023.3271866
  • Zhaofeng, M., Lingyun, W., Xiaochang, W., Zhen, W., & Weizhe, Z. (2020). Blockchain-Enabled decentralized trust management and secure usage control of IoT Big data. IEEE Internet of Things Journal, 7(5), 4000–4015. https://doi.org/10.1109/JIOT.2019.2960526