Worm viruses nowadays tend not only to simply attack a host and destroy it but generate high volumes of traffic and cause network failure. This paper proposes a learning-based system for detecting abnormal traffic with a control function for individual hosts included in it for efficient protection against worm viruses and network security on a network level. The system searches, detects and learns abnormal traffic on a network level to prevent factors causing network bottleneck from affecting in advance. This paper also presents a network security management system using the ARP Spoofing attack method to efficiently control the hosts within the network.
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