Abstract: With the rapid growth of digital infrastructure and network-based services, organizations are increasingly vulnerable to cyber threats such as unauthorized access, malware injection, and denial-of-service attacks. Traditional security mechanisms often fail to provide real-time detection and continuous monitoring of network activities. This paper presents CyberFlux, a intrusion detection and monitoring system designed to identify malicious network behavior and provide timely alerts to system administrators. The proposed system analyzes network traffic patterns and system logs to detect anomalies and classify potential intrusions. CyberFlux integrates a centralized monitoring dashboard that enables real-time visualization of security events and intrusion reports. The system is implemented using a scalable backend architecture and evaluated under simulated attack scenarios to validate its effectiveness. Experimental results demonstrate improved detection accuracy and faster response times compared to conventional rule-based systems. The proposed solution is suitable for deployment in small- and medium-scale organizational environments to enhance cybersecurity resilience.

Keywords: Intrusion Detection, Cybersecurity, Machine Learning, Deep Learning, Network Security, Monitoring System.

Downloads: | DOI: 10.17148/IARJSET.2025.121241