Intelligent Cyber Security Framework for Threat Detection using Ensemble Learning Techniques

Abstract

Cyber security is critical in today’s fast-paced digital landscape. As AI-driven solutions become indispensable for safeguarding enterprises, the escalating volume and complexity of cyber threats frequently overwhelm conventional security measures, resulting in significant financial and reputational risks. To address this challenge, this study proposes an advanced cyber security framework based on an ensemble learning model that combines machine learning and deep learning algorithms. Using the HIKARI-2021 dataset (Kaggle), we evaluated and compared multiple classifiers, including Random Forest, Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, Logistic Regression, Multi-Layer Perceptron, and Convolutional Neural Network. By integrating these models through an ensemble approach, we leveraged their complementary strengths, achieving a notable 96.32% accuracy—a significant improvement over individual models. Beyond accuracy, the ensemble method enhances adaptability, enabling more dynamic and resilient security frameworks. Our findings highlight the efficacy of ensemble learning in cyber security, demonstrating its potential to fortify digital enterprises against evolving threats. This research not only advances practical solutions but also paves the way for future studies on AI-integrated cyber security, fostering innovation and robust digital infrastructure globally.