Abstract:
"In today's ever-evolving landscape of cyber threats, traditional security measures face many
challenges from sophisticated malware capable of evading detection through multiple kinds of
dynamic behaviors. ""CySentinel"" proposes an innovative solution to improve network security
through behavior-based malware detection. This project introduces a cutting-edge machine
learning model proficient at analyzing network traffic patterns and anomalies, enabling early threat
identification to prevent potential damage. By prioritizing software and network interaction
behaviors over signature-based detection, CySentinel offers a proactive cybersecurity approach.
Trained on an extensive dataset encompassing benign and malicious network behaviors, the system
accurately detects between normal operations and security threats. This methodology not only
heightens detection rates for new and unknown malware but also minimizes false positives,
thereby fortifying organizational security postures. CySentinel seamlessly integrates with existing
network infrastructures, furnishing a resilient defense layer against evolving threats. This endeavor
underscores the significance of behavior-based analytics in combatting cybercrime, promising a
safer digital landscape for businesses and individuals alike."
