Performance Comparison of Whale and Harris Hawks Optimizers with Network Intrusion Prevention Systems

Mosleh M. Abualhaj, Sumaya N Al-Khatib, Mohammad A Alsharaiah, Mohammad O Hiari
Pages 1530-1538
Abstract Views 660
PDF Downloads 563
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DOI https://doi.org/10.47738/jads.v5i4.323

Abstract


Digital technology has permeated every aspect of our daily lives. Processing and evaluating information are highly demanding in all fields, including cybersecurity. Cybersecurity engineers widely use the Network Intrusion Prevention System (NIPS) to safeguard against cyberattacks. To avoid cyberattacks, the NIPS must deal with a large amount of data, which degrades its performance. This paper uses the whale optimization algorithm (WOA) and the Harris Hawks optimization method (HHO) to diminish the large amount of data that the NIPS needs to deal with. Subsequently, the Gradient Boosting Machine (GBM) is employed to determine the accuracy achieved when employing WOA and HHO. The GBM classifier is widely regarded as a sophisticated and straightforward classifier in data mining. Regardless of the premise of feature independence, it outperforms all other classification algorithms by delivering excellent performance. When using GBM, the findings indicate that the accuracy achieved with HHO is 89.81%, but the accuracy attained with WOA is 94.3%.

Keywords


Gradient Boosting Machine; Whale Optimizers; Harris Hawks Optimizers; Intrusion Prevention Systems

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References


M. Bang and H. Saraswat, "Building an effective and efficient continuous web application security program," 2016 International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA), 2016, pp. 1-4, DOI: 10.1109/CyberSA.2016.7503287.

Abualhaj, M., A. Abu-Shareha, Q. Shambour, Adeeb Alsaaidah, S. Al-Khatib, and Mohammed Anbar. "Customized K-nearest neighbors’ algorithm for malware detection." International Journal of Data and Network Science 8, no. 1 (2024): 431-438.

H. Mendes, S. E. Quincozes, and V. E. Quincozes, “A Web User Interface Tool for Metaheuristics-Based Feature Selection Assessment for IDSs,” Oct. 2022, doi: https://doi.org/10.1109/csnet56116.2022.9955616.

Kolhar, Manjur, Fadi Al-Turjman, Abdalla Alameen, and Mosleh M. Abualhaj. "A three layered decentralized IoT biometric architecture for city lockdown during COVID-19 outbreak." Ieee Access 8 (2020): 163608-163617.

M. Alazab, R. Abu Khurma, P. Castillo, B. Abu-Salih, A. Martín, and D. Camacho. “An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron”, Egyptian Informatics Journal, vol. 25, no. 1, pp. 1-9, Mar. 2024, doi:10.1016/j.eij.2023.100423.

S. Mirjalili and A. Lewis, “The Whale Optimization Algorithm,” Advances in Engineering Software, vol. 95, pp. 51–67, May 2016, doi: https://doi.org/10.1016/j.advengsoft.2016.01.008.

Sayed A.Aziz, Amira, Sanaa EL-Ola Hanafi, and Aboul Ella Hassanien. (2017) “Comparison of classification techniques applied for network intrusion detection and classification.” Journal of Applied Logic 24: 109–118.

Vinutha, H. P, and B. Poornima. (2018)" An ensemble classifier approach on different feature selection methods for intrusion detection. " Information systems design and intelligent applications 443–451.

Lee, Jinlee, Dooho Park, and Changhoon. Lee. ( 2017 )“Feature selection algorithm for intrusions detection system using sequential forward search and random forest classifier.” Transactions On Internet And Information Systems 11 (10): 5132–5148.

Gaikwad, D. P, and Ravindra C. Thool. ( 2015 )“Intrusion detection system using Bagging with Partial Decision Tree base classifier.” Procedia Computer Science 49(1): 92–98.

Ingre, Bhupendra, and Anamika Yadav. (2015)“Performance analysis of NSL-KDD dataset using ANN. ” Communication. Engneering. System IEEE pp. 92–96.

Al-Mimi, Hani, Nesreen A. Hamad, Mosleh M. Abualhaj, Mohammad Sh Daoud, Ali Al-dahoud, and Mohammad Rasmi. "An Enhanced Intrusion Detection System for Protecting HTTP Services from Attacks." International Journal of Advances in Soft Computing & Its Applications 15, no. 3 (2023).

Çavuşoğlu, Ü. A new hybrid approach for intrusion detection using machine learning methods. Appl Intell 49, 2735–2761 (2019). https://doi.org/10.1007/s10489-018-01408-x

H. Al-Mimi, N. A. Hamad and M. M. Abualhaj, "A Model for the Disclosure of Probe Attacks Based on the Utilization of Machine Learning Algorithms," 2023 10th International Conference on Electrical and Electronics Engineering (ICEEE), Istanbul, Turkiye, 2023, pp. 241-247, doi: 10.1109/ICEEE59925.2023.00051.

Ruba Abu Khurma, M. A. Awadallah, and Ibrahim Aljarah, “Binary Harris Hawks Optimisation Filter Based Approach for Feature Selection,” Sep. 2021, doi: https://doi.org/10.1109/picict53635.2021.00022.

Yu Z, Shi X, Zhou J, Chen X, Qiu X. Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm. Applied Sciences. 2020; 10(4):1403. https://doi.org/10.3390/app10041403

Basu Dev Shivahare, M. Singh, A. Gupta, S. Ranjan, D. N. Pareta, and Biswa Mohan Sahu, “Survey Paper: Whale optimization algorithm and its variant applications,” Feb. 2021, doi: https://doi.org/10.1109/iciptm52218.2021.9388344.

Nadimi-Shahraki, M., Zamani, H., Asghari Varzaneh, Z. et al. A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations. Arch Computat Methods Eng 30, 4113–4159 (2023). https://doi.org/10.1007/s11831-023-09928-7.

Diop, Lamine, Saeed Samadianfard, Ansoumana Bodian, Zaher Mundher Yaseen, Mohammad Ali Ghorbani, and Hana Salimi. "Annual rainfall forecasting using hybrid artificial intelligence model: integration of multilayer perceptron with whale optimization algorithm." Water Resources Management 34 (2020): 733-746.

R. Wen and K. Zhang, "Research on Automated Classification Method of Network Attacking Based on Gradient Boosting Decision Tree," 2022 International Conference on Machine Learning and Knowledge Engineering (MLKE), Guilin, China, 2022, pp. 72-76, doi: 10.1109/MLKE55170.2022.00019.

W. El Elhola and K. Bozed, "Performance Analysis of Brain Tumor Detection Based on Gradient Boosting Machine -CNN model," 2022 IEEE 2nd International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and Computer Engineering (MI-STA), Sabratha, Libya, 2022, pp. 544-547, doi: 10.1109/MI-STA54861.2022.9837738.

Al Saaidah, A., Abualhaj, M.M., Shambour, Q.Y. et al. Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18914-5

Al-Mimi, H. M., Hamad, N. A., Abualhaj, M. M., Al-Khatib, S. N. & Hiari, M. O. (2023). Improved Intrusion Detection System to Alleviate Attacks on DNS Service. Journal of Computer Science, 19(12), 1549-1560. https://doi.org/10.3844/jcssp.2023.1549.1560




DOI: https://doi.org/10.47738/jads.v5i4.323

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Journal of Applied Data Sciences

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