To maintain high-quality and uninterrupted online services, and reduce intangible losses caused by network interruptions, the researchers of the present study developed a network monitoring mechanism, highlighting the importance of preventing malicious connections and attack behavior. To enable the early detection of network threats and reduce the load of end-terminal infiltration detection systems, the present study designed a mechanism that prevents malicious connections and attack behavior by monitoring network traffic volume. The mechanism is based on fuzzy control theory and supplemented by statistical theory. The traffic volume behavior and normal connection behavior of individual data transmissions are compared to establish normal transmission behavioral models, which serve as the network monitoring and warning functions of the proposed mechanism.
The present study improves upon the internal network outcomes reported in studies presenting similar mechanisms. In a reinforced external network, the researchers conducted an experiment to improve the practicality of the proposed mechanism. Finally, the researchers adopted a software-defined networking defensive method for fending off traffic volume attacks, which achieved a 95% defense rate against traffic volume attacks, effectively mitigating the attack rate of distributed denial of service attacks.
