首页> 外国专利> H-IoT THE METHOD OF DEFENSE AGAINST DISTRIBUTED DENIAL-OF-SERVICE ATTACK ON THE HETEROGENEOUS IOT NETWORK AND THE SYSTEM THEREOF

H-IoT THE METHOD OF DEFENSE AGAINST DISTRIBUTED DENIAL-OF-SERVICE ATTACK ON THE HETEROGENEOUS IOT NETWORK AND THE SYSTEM THEREOF

机译:H-IoT一种针对异构物联网的分布式拒绝服务攻击的防御方法及其系统

摘要

The present invention relates to a method for defending a distributed denial of service (DDOS) attack on a heterogeneous internet of things (H-IoT) network, which is capable of efficiently managing the DDOS attack and a system thereof. According to one embodiment of the present invention, the method comprises the steps of: allowing an end-point defender placed at an edge node of an IoT network to receive traffic information from the IoT network; allowing the end-point defender to perform machine learning for the traffic information; allowing the end-point defender to generate a traffic statistic report based on the machine-learned traffic information; allowing an orchestrator to receive the traffic statistic report from the end-point defender to analyze a DDOS attack; allowing the orchestrator to generate and transmit a policy to the end-point defender when the DDOS attack is detected; and allowing the end-point defender to extract traffic features upon the DDOS attack based on the received policy to apply the traffic features to the machine learning.
机译:本发明涉及一种用于在异构物联网(H-IoT)网络上防御分布式拒绝服务(DDOS)攻击的方法,该方法能够有效地管理DDOS攻击。根据本发明的一个实施例,该方法包括以下步骤:允许放置在IoT网络的边缘节点处的端点防御者从IoT网络接收流量信息;以及允许端点防御者对交通信息进行机器学习;允许端点防御者根据机器学习的流量信息生成流量统计报告;允许协调器从端点防御者接收流量统计报告以分析DDOS攻击;当检测到DDOS攻击时,允许协调器生成策略并将其传输到端点防御者;并且允许端点防御者根据接收到的策略在DDOS攻击中提取流量特征,以将流量特征应用于机器学习。

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