This cybersecurity project focuses on developing a real-time threat detection and prevention system using machine learning algorithms. The goal is to enhance digital security by proactively identifying and mitigating potential vulnerabilities across networks, applications, and endpoints. It includes modules for intrusion detection, malware analysis, and secure communication protocols.
The main challenge was to design a system that could efficiently detect zero-day attacks without compromising system performance. Balancing security and usability was critical, especially while implementing strong encryption and maintaining low-latency communication between clients and servers.
The system successfully detected over 98% of simulated attacks in a controlled environment and reduced response time to incidents by 65%. It enhanced overall network security posture and provided valuable insights into user behavior, helping organizations proactively address potential threats.
