Metaheuristic Optimization for Enhancing Cyber Security Index Prediction: A DTO+FGW Approach with MLP Integration
Ahmed Mohamed Zaki1, Abdelaziz A. Abdelhamid2, Abdelhameed Ibrahim3, Marwa M. Eid4,5, El-Sayed M. El-Kenawy*5
1 Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
2 Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
3 School of ICT, Faculty of Engineering, Design and Information & Communications Technology (EDICT), Bahrain Polytechnic, PO Box 33349, Isa Town, Bahrain
4 Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura 35712, Egypt
5 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, 35111, Egypt
Emails: azaki@jcsis.org; abdelaziz@cis.asu.edu.eg; abdelhameed.fawzy@polytechnic.bh; mmm@ieee.org; skenawy@ieee.org
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.