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Introduction: Cybersecurity has become a paramount concern in the digital age, with an increasing number of cyber threats targeting individuals, organizations, and nations. This paper introduces a proactive approach to address this challenge – Cyber Patrolling Using Machine Learning.

Objective: The primary objective of this research is to develop a robust system that leverages machine learning algorithms to enhance cyber patrolling capabilities. By actively monitoring and analyzing online activities, the proposed system aims to identify and mitigate potential cyber threats before they escalate.

Methodology: Utilizing advanced machine learning techniques, the system will continuously learn and adapt to evolving cyber threats.

Implementation: The system will be implemented through a combination of supervised and unsupervised learning models. Training datasets will include a diverse range of cyber threats, enabling the system to recognize both known and emerging threats.

Results: Preliminary results demonstrate the system’s effectiveness in accurately identifying and responding to potential cyber threats, significantly reducing response times compared to traditional methods. The adaptive nature of machine learning ensures continuous improvement and resilience against evolving cyber threats.

Conclusion: In conclusion, the integration of machine learning into cyber patrolling represents a promising approach to bolstering cybersecurity defenses. The proactive identification and mitigation of threats provide a proactive stance against cyber-attacks, enhancing overall digital security. As technology advances, the proposed system offers a scalable and adaptive solution to safeguard against the ever-evolving landscape of cyber threats.

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