Abstract:
The continuous evolution of malware threats demands more advanced detection
techniques, and artificial intelligence (AI) has emerged as a powerful tool in this area.
Traditional security methods often fall short in addressing the increasingly sophisticated
tactics used by cybercriminals. Integrating AI with network traffic analysis strengthens
cybersecurity by allowing for early detection of malicious activities, providing a more
effective defense against potential breaches.
Monitoring network traffic is a proven method for identifying suspicious behavior and
detecting compromised devices before they inflict serious damage. While some
malware is caught by firewalls and other conventional security measures, many threats
slip through due to advanced evasion techniques.
This project explores the use of ML-driven network traffic analysis to enhance the
detection of insider threats, emphasizing the need to establish baseline traffic patterns
to distinguish between normal and anomalous network behavior. By understanding what
typical activity looks like, deviations that could indicate malicious behavior are easier to
detect. Additionally, this project aims to develop a resource-efficient model for IoT
malware detection, ensuring the solution is both effective and lightweight for practical
use in constrained environments.
