The Role of Machine Learning in Network Anomaly Detection for Cybersecurity

Abstract

This research introduces a theoretical framework for network anomaly detection in cybersecurity, emphasizing the integration of adaptive machine learning models, ensemble techniques, and advanced feature engineering. The adaptability of machine learning models enables dynamic responsiveness to emerging cyber threats, forming a foundation for a resilient anomaly detection system. Ensemble techniques, particularly the incorporation of Random Forests, enhance the framework's robustness by amalgamating strengths from diverse models, mitigating false positives and negatives. Advanced feature engineering, coupled with deep learning architectures, contributes to a nuanced understanding of intricate patterns within network traffic. The theoretical exploration encounters challenges in quantifying performance gains, integration complexities, and data privacy concerns. Addressing these challenges is critical for refining and fortifying the proposed framework, ensuring its applicability and effectiveness in real-world cybersecurity scenarios. The significance of the framework lies in addressing existing gaps in network anomaly detection theories and advancing the understanding of machine learning's role in cybersecurity. Future directions include refining adaptive models, enhancing ensemble techniques, and addressing data privacy concerns. Adapting theoretical approaches to meet emerging cyber threats is paramount for the continual evolution of theoretical frameworks in cybersecurity. This research underscores the importance of ongoing theoretical advancements for practical applications, fostering optimism for the continual growth of frameworks that effectively combat the ever-changing landscape of cybersecurity challenges.