Enhancing Cyber Security Attack Prediction: A Weighted Optimized Ensemble Approach Using DTO+DE Algorithm

Abstract

In the rapidly evolving landscape of cybersecurity, the perpetual challenge lies in staying one step ahead of potential threats. This research embarks on a transformative journey, seeking to fortify the predictive capabilities of cybersecurity systems by amalgamating the Dipper Throated Algorithm (DTO) and the Differential Evolution Algorithm (DE). The envisioned synergy between these two powerful optimization methodologies forms the backbone of an innovative Weighted Optimized Ensemble, seamlessly integrating diverse machine learning techniques. Within this intricate framework, the MLP, KNN, SVR, Decision Tree, Random Fores, and an Average Ensemble coalesce into a formidable defense mechanism against cyber threats. The underlying premise is to capitalize on the individual strengths of these models, enhancing their collective efficacy through the strategic optimization prowess of DTO and DE. The optimization outcomes, as reflected in key performance metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared (R2), spotlight a remarkable achievement—the substantial reduction of RMSE to an impressive 0.00941. This achievement signifies more than just a numerical enhancement; it symbolizes a paradigm shift in the cybersecurity paradigm. The meticulous integration of DTO+DE showcases its potential to fine-tune the ensemble model, leading to a tangible and significant impact on cybersecurity defenses. This not only augurs well for predictive accuracy but also holds the promise of fostering proactive cybersecurity measures, thereby contributing to a safer and more secure digital landscape.