An Enhanced Sine Cosine Algorithm for Feature Selection in Network Intrusion Detection

Computer and Knowledge Engineering

مقاله 2، دوره 7، شماره 2 - شماره پیاپی 14، اسفند 2024، صفحه 17-26   

نوع مقاله: Original Article

شناسه دیجیتال (DOI): 10.22067/cke.2024.83460.1097

نویسندگان

Zahra Asgari Varzaneh¹؛ Soodeh Hosseini^{* 2}

¹Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran,

²Department of Computer Science, Faculty of Mathematics and Computer, Shahid Bahonar University of Kerman, Kerman, Iran.

چکیده

For computer networks to remain secure, intrusion detection is essential. Analyzing network traffic data is part of this activity to spot possible cyber threats. However, the curse of dimensionality presents a challenge because there are so many dimensions in the data. To overcome this challenge, feature selection is essential to creating a successful intrusion detection system. It involves removing irrelevant and redundant features, which enhances the classification model's accuracy and lowers the dimensionality of the feature space. Metaheuristic algorithms are optimization techniques inspired by nature and are well-suited to choose features for network intrusion detection. They are effective in exploring large search spaces and have been widely used for this purpose. In this study, we improve the Sine Cosine Algorithm named ISCA for feature selection by introducing a controlling parameter to balance exploration and exploitation. Based on the NSL-KDD dataset, the results show that compared to other competing algorithms, the ISCA performs better than other metaheuristic algorithms in terms of both the number of features selected and the accuracy of classification.

کلیدواژه‌ها

Sine Cosine Algorithm؛ Meta-heuristic algorithms؛ feature selection؛ network intrusion detection

مراجع

[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