307 193

Title

Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration

  Ahmed Mohamed Zaki 1 * ,   Abdelaziz A. Abdelhamid 2 ,   Abdelhameed Ibrahim 3 ,   Marwa M. Eid 4 ,   El-Sayed M. El-Kenawy 5

1  Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
    (azaki@jcsis.org)

2  Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
    (abdelaziz@cis.asu.edu.eg)

3  School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
    (abdelhameed.fawzy@polytechnic.bh)

4  Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt; Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
    (mmm@ieee.org)

5  Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
    (skenawy@ieee.org)


Doi   :   https://doi.org/10.54216/IJAACI.040202

Received: April 13, 2023 Revised: June 22, 2023 Accepted: August, 10, 2023

Abstract :

In the realm of cybersecurity, the evaluation and enhancement of cyber resilience are paramount to safeguarding nations and organizations against evolving digital threats. This paper introduces a novel approach that integrates the Dipper Throated Algorithm (DTO) and the Grey Wolf Optimizer (GWO) to fortify the analysis of Cyber Security Indexes. These indexes encompass vital metrics, including the Cybersecurity Exposure Index (CEI), Global Cyber Security Index (GCI), National Cyber Security Index (NCSI), and Digital Development Level (DDL). Leveraging the adaptive nature of DTO and the collaborative hunting strategies of GWO, the proposed DTO+GWO algorithm aims to optimize the evaluation of cyber readiness, exposure levels, and global commitments to cybersecurity. The Cyber Security Indexes dataset, featuring indicators from 193 countries, serves as the testing ground. This study contributes to advancing cyber threat assessment methodologies, fostering a proactive stance in the face of cyber risks globally. Through rigorous optimization, the DTO+GWO algorithm exhibits promising potential to elevate the precision and efficacy of cybersecurity evaluations. The optimization results demonstrate a notable achievement, with an RMSE of 0.0090, reflecting the algorithm's enhanced performance in fine-tuning the assessment of cybersecurity indexes.

Keywords :

DTO Algorithm; Gray Wolf Algorithm; Cyber Security Indexes; Metaheuristic Optimization; Machine Learning; Cyber Threat Assessment.

References :

[1]     Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020). Cybersecurity data science: An overview from machine learning perspective. Journal of Big Data, 7(1), 41. https://doi.org/10.1186/s40537-020-00318-5

[2]     Martínez Torres, J., Iglesias Comesaña, C., & García-Nieto, P. J. (2019). Review: Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), 2823–2836. https://doi.org/10.1007/s13042-018-00906-1

[3]     Lu, H., Zhang, G., & Shen, Y. (2020). Cyber Security Situation Prediction Model Based on GWO-SVM. In L. Barolli, F. Xhafa, & O. K. Hussain (Eds.), Innovative Mobile and Internet Services in Ubiquitous Computing (pp. 162–171). Springer International Publishing. https://doi.org/10.1007/978-3-030-22263-5_16

[4]     Alzaqebah, A., Aljarah, I., Al-Kadi, O., & Damaševičius, R. (2022). A Modified Grey Wolf Optimization Algorithm for an Intrusion Detection System. Mathematics, 10(6), Article 6. https://doi.org/10.3390/math10060999

[5]     Yu, T., Da, K., Wang, Z., Ling, Y., Li, X., Bin, D., & Yang, C. (2022). An Advanced Accurate Intrusion Detection System for Smart Grid Cybersecurity Based on Evolving Machine Learning. Frontiers in Energy Research, 10. https://www.frontiersin.org/articles/10.3389/fenrg.2022.903370

[6]     Aldea, C. L., Bocu, R., & Vasilescu, A. (2023). Relevant Cybersecurity Aspects of IoT Microservices Architectures Deployed over Next-Generation Mobile Networks. Sensors, 23(1), Article 1. https://doi.org/10.3390/s23010189

[7]     Zaki, A. M., Towfek, S. K., Gee, W., Zhang, W., & Soliman, M. A. (2023). Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration. Journal of Artificial Intelligence and Metaheuristics, Volume 6(Issue 2), 16–25. https://doi.org/10.54216/JAIM.060202

[8]     Diao, X., Zhao, Y., Smidts, C., Vaddi, P. K., Li, R., Lei, H., Chakhchoukh, Y., Johnson, B., & Blanc, K. L. (2024). Dynamic probabilistic risk assessment for electric grid cybersecurity. Reliability Engineering & System Safety, 241, 109699. https://doi.org/10.1016/j.ress.2023.109699

[9]     Kävrestad, J., Rambusch, J., & Nohlberg, M. (2024). Design principles for cognitively accessible cybersecurity training. Computers & Security, 137, 103630. https://doi.org/10.1016/j.cose.2023.103630

[10] Wang, J., Ho, C. Y. (Chloe), & Shan, Y. G. (2024). Does cybersecurity risk stifle corporate innovation activities? International Review of Financial Analysis, 91, 103028. https://doi.org/10.1016/j.irfa.2023.103028

[11] Banaie-Dezfouli, M., Nadimi-Shahraki, M. H., & Beheshti, Z. (2023). BE-GWO: Binary extremum-based grey wolf optimizer for discrete optimization problems. Applied Soft Computing, 146, 110583. https://doi.org/10.1016/j.asoc.2023.110583

[12] Abdelhamid, A. A., El-Kenawy, E.-S. M., Ibrahim, A., Eid, M. M., Khafaga, D. S., Alhussan, A. A., Mirjalili, S., Khodadadi, N., Lim, W. H., & Shams, M. Y. (2023). Innovative Feature Selection Method Based on Hybrid Sine Cosine and Dipper Throated Optimization Algorithms. IEEE Access, 11, 79750–79776. https://doi.org/10.1109/ACCESS.2023.3298955

[13] Zaki, A. M., Khodadadi, N., Lim, W. H., & Towfek, S. K. (2023). Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions. American Journal of Business and Operations Research, Volume 11(Issue 1), 79–88. https://doi.org/10.54216/AJBOR.110110

[14] Yang, Z. (2024). Competing leaders grey wolf optimizer and its application for training multi-layer perceptron classifier. Expert Systems with Applications, 239, 122349. https://doi.org/10.1016/j.eswa.2023.122349

[15] Cyber Security Indexes. (n.d.). [dataset]. Retrieved January 4, 2024, from https://www.kaggle.com/datasets/katerynameleshenko/cyber-security-indexes

[16] Ahmad, A., Xiao, X., Mo, H., & Dong, D. (2024). Tuning data preprocessing techniques for improved wind speed prediction. Energy Reports, 11, 287–303. https://doi.org/10.1016/j.egyr.2023.11.056

[17] Da Poian, V., Theiling, B., Clough, L., McKinney, B., Major, J., Chen, J., & Hörst, S. (2023). Exploratory data analysis (EDA) machine learning approaches for ocean world analog mass spectrometry. Frontiers in Astronomy and Space Sciences, 10. https://www.frontiersin.org/articles/10.3389/fspas.2023.1134141

[18] Ali, T. E., Chong, Y.-W., & Manickam, S. (2023). Machine Learning Techniques to Detect a DDoS Attack in SDN: A Systematic Review. Applied Sciences, 13(5), Article 5. https://doi.org/10.3390/app13053183

[19] Rizk, F. H., Arkhstan, S., Zaki, A. M., Kandel, M. A., & Towfek, S. K. (2023). Integrated CNN and Waterwheel Plant Algorithm for Enhanced Global Traffic Detection. Journal of Artificial Intelligence and Metaheuristics, Volume 6(Issue 2), 36–45. https://doi.org/10.54216/JAIM.060204

[20] Uddin, M. J., Ahamad, M. M., Hoque, M. N., Walid, M. A. A., Aktar, S., Alotaibi, N., Alyami, S. A., Kabir, M. A., & Moni, M. A. (2023). A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh. Information, 14(7), Article 7. https://doi.org/10.3390/info14070376

[21] Shah, N., Arshad, A., Mazer, M. B., Carroll, C. L., Shein, S. L., & Remy, K. E. (2023). The use of machine learning and artificial intelligence within pediatric critical care. Pediatric Research, 93(2), Article 2. https://doi.org/10.1038/s41390-022-02380-6

[22] Chkeir, S., Anesiadou, A., Mascitelli, A., & Biondi, R. (2023). Nowcasting extreme rain and extreme wind speed with machine learning techniques applied to different input datasets. Atmospheric Research, 282, 106548. https://doi.org/10.1016/j.atmosres.2022.106548


Cite this Article as :
Style #
MLA Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy. "Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration." International Journal of Advances in Applied Computational Intelligence, Vol. 4, No. 2, 2023 ,PP. 15-25 (Doi   :  https://doi.org/10.54216/IJAACI.040202)
APA Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy. (2023). Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration. Journal of International Journal of Advances in Applied Computational Intelligence, 4 ( 2 ), 15-25 (Doi   :  https://doi.org/10.54216/IJAACI.040202)
Chicago Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy. "Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration." Journal of International Journal of Advances in Applied Computational Intelligence, 4 no. 2 (2023): 15-25 (Doi   :  https://doi.org/10.54216/IJAACI.040202)
Harvard Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy. (2023). Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration. Journal of International Journal of Advances in Applied Computational Intelligence, 4 ( 2 ), 15-25 (Doi   :  https://doi.org/10.54216/IJAACI.040202)
Vancouver Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy. Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration. Journal of International Journal of Advances in Applied Computational Intelligence, (2023); 4 ( 2 ): 15-25 (Doi   :  https://doi.org/10.54216/IJAACI.040202)
IEEE Ahmed Mohamed Zaki, Abdelaziz A. Abdelhamid, Abdelhameed Ibrahim, Marwa M. Eid, El-Sayed M. El-Kenawy, Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration, Journal of International Journal of Advances in Applied Computational Intelligence, Vol. 4 , No. 2 , (2023) : 15-25 (Doi   :  https://doi.org/10.54216/IJAACI.040202)