RESEARCH ON TECHNIQUES TO ENHANCE DDoS ATTACK PREVENTION USING CUMULATIVE SUM AND BACKPROPAGATION ALGORITHMS

Abstract

This paper focuses on enhancing DDoS attack prevention capabilities through the combination of the Cumulative Sum (CUSUM) algorithm and the Backpropagation method, aiming to detect attack indicators early and accurately. The CUSUM algorithm is used to monitor and analyze network traffic over time, identifying unusual fluctuations in traffic without requiring prior knowledge of attack types. Meanwhile, the Backpropagation method is applied to optimize neural networks, enabling the system to learn from previous traffic data and distinguish clearly between legitimate traffic and attack traffic. Compared to previous research methods, this combined approach offers several significant advantages. First, CUSUM provides high-accuracy attack detection, allowing the system to respond promptly. Second, Backpropagation enables the system to improve automatically over time, reducing false alarm rates and enhancing prevention effectiveness. Finally, the feasibility and effectiveness of the solution are demonstrated through real-world experiments, showing improved detection rates and faster response times compared to traditional methods.

Keywords

• network attack
• cusum algorithm
• backpropagation algorithm
• anti-spoofing
• ddos attack