Computer and Knowledge Engineering

Article 2, Volume 7, Issue 2 - Serial Number 14, January 0, Pages 17-26   

Document Type: Original Article

DOI: 10.22067/cke.2024.83460.1097

References

[1]    G. A. Marin. (2005, Dec.). Network security basics. IEEE security & privacy. [Online]. 3(6), pp. 68-72. Available: https://doi.org/10.1109/MSP.2005.153

[2]    Computer and information security handbook, 2rd ed., J. R. Vacca Co., 2012.

[3]    M. Ahmed, A. N. Mahmood, and J. Hu. (2016, Jan.). A survey of network anomaly detection techniques. Journal of Network and Computer Applications. [Online]. 60, pp. 19-31. Available: https://doi.org/10.1016/j.jnca.2015.11.016

[4]    V. Z. Asghari and M. Kuchaki Rafsanjani. (2021, Jun.). Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm.  Intelligent Decision Technologies. [Online]. 15(2), pp. 231-237. Available: https://doi.org/10.3233/IDT-200036 

[5]    H.-J. Liao, C.-H. R. Lin, Y.-C. Lin, and K.-Y. Tung. (2013, Jan.). Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications. [Online]. 36(1), pp. 16-24. Available: https://doi.org/10.1016/j.jnca.2012.09.004

[6]    Handbook of research on modern cryptographic solutions for computer and cyber security, B. Gupta, D. P. Agrawal, and S. Yamaguchi Co., IGI global, 2016.

[7]    J. M. Vidal, M. A. S. Monge, and S. M. M. Monterrubio. (2020). Anomaly-based intrusion detection: adapting to present and forthcoming communication environments. in Handbook of Research on Machine and Deep Learning Applications for Cyber Security. [Online]. pp. 195-218. Available: https://doi.org/10.4018/978-1-5225-9611-0.ch010

[8]    M. K. Rafsanjani and Z. A. Varzaneha. (2013). Intrusion detection by data mining algorithms: a review. Journal of New Results in Science. [Online]. 2(2), pp. 76-91. Available: https://dergipark.org.tr/en/pub/jnrs/issue/27513/123346

[9]     S. Axelsson, "Intrusion detection systems: A survey and taxonomy," Department of Computer Engineering Chalmers University of Technology Göteborg, 2000.

[10]  M. A. Ambusaidi, X. He, P. Nanda, and Z. Tan. (2016, Jan.). Building an intrusion detection system using a filter-based feature selection algorithm. IEEE transactions on computers. [Online]. 65(10), pp. 2986-2998. Available:  https://doi.org/10.1109/TC.2016.2519914

[11]  A. Alazab, M. Hobbs, J. Abawajy, and M. Alazab, "Using feature selection for intrusion detection system," in 2012 international symposium on communications and information technologies (ISCIT), Gold Coast, QLD, Australia, 2012, pp. 296-301.

[12]  S. Aljawarneh, M. Aldwairi, and M. B. Yassein. (2018, Mar.). Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model. Journal of Computational Science. [Online]. 25, pp. 152-160. Available: https://doi.org/10.1016/j.jocs.2017.03.006

[13]  Z. A. Varzaneh, S. Hossein, S. E. Mood, and M. M. Javidi. (2022, Sep.). A new hybrid feature selection based on Improved Equilibrium Optimization. Chemometrics and Intelligent Laboratory Systems. [Online]. 228, p. 104618. Available: https://doi.org/10.1016/j.chemolab.2022.104618

[14]  J. Huang, Y. Chen, A. A. Heidari, L. Liu, H. Chen, and G. Liang. (2024, Jun.). Improved Runge Kutta Optimization Using Compound Mutation Strategy in Reinforcement Learning Decision Making for Feature Selection. Journal of Bionic Engineering. [Online]. 21, pp. 1-37. Available: https://doi.org/10.1007/s42235-024-00555-x

[15]  A. I. Hammouri, M. A. Awadallah, M. S. Braik, M. A. Al-Betar, and M. Beseiso. (2024, May.). Improved Dwarf Mongoose Optimization Algorithm for Feature Selection: Application in Software Fault Prediction Datasets. Journal of Bionic Engineering. [Online]. 21, pp. 1-34. Available: https://doi.org/10.1007/s42235-024-00524-4

[16]  M. H. Nadimi-Shahraki, H. Zamani, Z. A. Varzaneh, A. S. Sadiq, and S. Mirjalili. (2024, Jul.). A Systematic Review of Applying Grey Wolf Optimizer, its Variants, and its Developments in Different Internet of Things Applications. Internet of Things. [Online]. 26, p. 101135. Available: https://doi.org/10.1016/j.iot.2024.101135

[17]  M. Abdel-Basset, L. Abdel-Fatah, and A. K. Sangaiah. (2018, Jan.). Metaheuristic algorithms: A comprehensive review. Computational intelligence for multimedia big data on the cloud with engineering applications. [Online]. pp. 185-231. Available: https://doi.org/10.1016/B978-0-12-813314-9.00010-4

[18]  X.-S. Yang, "Nature-inspired metaheuristic algorithms," in Luniver press, 2^nd ed., United Kindom, 2010.

[19]  G. Hu, Y. Guo, and G. Sheng. (2024, May.). Salp Swarm Incorporated Adaptive Dwarf Mongoose Optimizer with Lévy Flight and Gbest-Guided Strategy, Journal of Bionic Engineering. [Online]. 21, pp. 1-35. Available: https://doi.org/10.1007/s42235-024-00545-z

[20]  S. Mirjalili. (2016, Mar.). SCA: a sine cosine algorithm for solving optimization problems. Knowledge-based systems. [Online]. 96, pp. 120-133. Available: https://doi.org/10.1016/j.knosys.2015.12.022

[21]  M. H. Nadimi-Shahraki, H. Zamani, Z. Asghari Varzaneh, and S. Mirjalili. (2023, May.). A systematic review of the whale optimization algorithm: theoretical foundation, improvements, and hybridizations. Archives of Computational Methods in Engineering. [Online]. 30(7), pp. 4113-4159. Available: https://doi.org/10.1007/s11831-023-09928-7

[22]     H. Alazzam, A. Sharieh, and K. E. Sabri. (2020, Jun.). A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert systems with applications. [Online]. 148, p. 113249. Available: https://doi.org/10.1016/j.eswa.2020.113249

[23]  J. R. Beulah and D. S. Punithavathani. (2018, Sep.). A hybrid feature selection method for improved detection of wired/wireless network intrusions. Wireless Personal Communications. [Online]. 98(2), pp. 1853-1869. Available: https://doi.org/10.1007/s11277-017-4949-x

[24]  R. Zhao, Y. Mu, L. Zou, and X. Wen. (2022, Jun.). A hybrid intrusion detection system based on feature selection and weighted stacking classifier. IEEE Access. [Online]. 10, pp. 71414-71426. Available: https://doi.org/10.1109/ACCESS.2022.3186975

[25]  R. Vijayanand and D. Devaraj. (2020, Mar.). A novel feature selection method using whale optimization algorithm and genetic operators for intrusion detection system in wireless mesh network. IEEE Access. [Online]. 8, pp. 56847-56854. Available: https://doi.org/10.1109/ACCESS.2020.2978035

[26]  A. K. Shukla. (2020, Nov.). Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm. Neural Computing and Applications. [Online]. 33(13), pp. 7541-7561. Available: https://doi.org/10.1007/s00521-020-05500-7

[27]  F. Salo, A. B. Nassif, and A. Essex. (2019, Jan.). Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection. Computer network. [Online]. 148, pp. 164-175. Available: https://doi.org/10.1016/j.comnet.2018.11.010

[28]  I. Ahmed, A. Dahou, S. A. Chelloug, M. A. Al-qaness, and M. A. Elaziz. (2022, Mar.). Feature selection model based on gorilla troops optimizer for intrusion detection systems. Journal of Sensors. [Online]. (1), pp. 1-12. Available: https://doi.org/10.1155/2022/6131463

[29]  M. Safaldin, M. Otair, and L. Abualigah. (2020, Jun.). Improved binary gray wolf optimizer and SVM for intrusion detection system in wireless sensor networks. Journal of ambient intelligence and humanized computing. [Online]. 12, pp. 1559-1576. Available: https://doi.org/10.1007/s12652-020-02228-z

[30]  D. Jayalatchumy, R. Ramalingam, A. Balakrishnan, M. Safran, and S. Alfarhood. (2024, Mar.). Improved Crow Search-Based Feature Selection and Ensemble Learning for IoT Intrusion Detection. IEEE Access. [Online]. 12, pp. 33218 - 33235. Available: https://doi.org/10.1109/ACCESS.2024.3372859

[31]  U. Nandhini and S. K. SVN. (2024, Jun.). An improved Harris Hawks optimizer based feature selection technique with effective two-staged classifier for network intrusion detection system. Peer-to-Peer Networking and Applications. [Online]. 17, pp. 1-35. Available: https://doi.org/10.1007/s12083-024-01727-6

[32]  E. Rashedi, H. Nezamabadi-Pour, and S. Saryazdi. (2010, Dec.). BGSA: binary gravitational search algorithm, Natural computing. [Online]. 9, pp. 727-745. Available: https://doi.org/10.1007/s11047-009-9175-3

[33]  P. Cunningham and S. J. Delany. (2021, Jul.). k-Nearest neighbour classifiers-A Tutorial. ACM computing surveys (CSUR). [Online]. 54(6), pp. 1-25. Available: https://doi.org/10.1145/3459665

[34]  N. Al-Madi, H. Faris, and S. Mirjalili. (2019, Dec.). Binary multi-verse optimization algorithm for global optimization and discrete problems. International Journal of Machine Learning and Cybernetics. [Online]. 10(12), pp. 3445-3465. Available: https://doi.org/10.1007/s13042-019-00931-8

[35]  A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen. (2019, Aug.). Harris hawks optimization: Algorithm and applications. Future generation computer systems. [Online]. 97, pp. 849-872. https://doi.org/10.1016/j.future.2019.02.028

[36]  S. Mirjalili, A. H. Gandomi, S. Z. Mirjalili, S. Saremi, H. Faris, and S. M. Mirjalili. (2017, Dec.). Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems. Advances in engineering software. [Online]. 114, pp. 163-191. Available: https://doi.org/10.1016/j.advengsoft.2017.07.002

[37]  S. Mirjalili, S. M. Mirjalili, and A. Lewis. (2014, Mar.). Grey wolf optimizer. Advances in engineering software. [Online]. 69, pp. 46-61. Available: https://doi.org/10.1016/j.advengsoft.2013.12.007

[38]  R. Storn and K. Price. (1997, Dec.). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization. [Online]. 11, pp. 341-359. Available: https://doi.org/10.1023/A:1008202821328

[39]  S. Revathi and A. Malathi. (2013, Dec.). A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. International Journal of Engineering Research & Technology (IJERT). [Online]. 2(12), pp. 1848-1853.

[40]  S. García, J. Luengo, and F. Herrera, Data preprocessing in data mining. Cham, Switzerland: Springer International Publishing, 2015, pp. 59-139.

[41]  S. K. Sahu, S. Sarangi, and S. K. Jena, "A detail analysis on intrusion detection datasets," in 2014 IEEE international advance computing conference (IACC), Gurgaon, India, 2014, pp. 1348-1353.

[42]  Z. Asghari Varzaneh, S. Hosseini, and M. M. Javidi. (2023, Mar.). A novel binary horse herd optimization algorithm for feature selection problem. Multimedia Tools and Applications. [Online]. 82(26), pp. 1-35. Available: https://doi.org/10.1007/s11042-023-15023-7