Real-Time Traffic Analysis and Filtering for Dos Attacks Detection and Mitigation in Cloud Environment

Abstract

This paper proposed a system that detects and mitigates DoS attacks in the cloud in real time. Known DoS attacks in the Internet generally conquer the target by exhausting its resources, that can be anything related to network computing and service performance. This necessitates robust mitigation strategies capable of defending against a diverse array of attack vectors in real time. This response significantly reduces the impact of DOS attacks and safeguards cloud resources from resource exhaustion attacks. The system uses statistical analysis, payload and signature-based capturing and pattern matching algorithms to recognize malicious activities within the network traffic. Modular system architecture enables dynamic adaptation to new attack methods through Python's filtering capabilities. A user-friendly web interface empowers security personnel with real-time attack monitoring and visualization tools. By integrating real-time analysis, advanced filtering, user-centric design, and scalability, this comprehensive DoS mitigation system offers a robust defense against evolving threats, ensuring the uninterrupted operation of critical cloud services.