【24h】

IoT Eye An Efficient System for Dynamic IoT Devices Auto-discovery on Organization Level

机译:物联网眼:组织层面动态物联网设备自动发现的高效系统

获取原文

摘要

Internet of Things (IoT) serves not only as an essential part of the new generation information technology but as an important development stage in the information era. IoT devices such as unmanned aerial vehicles, robots and wearable equipments have been widely used in recent years. For most organizations' inner networks, innumerable dynamic connections with Internet accessible IoT devices occur at many parts all the time. It is usually these temporal links that arise potential threats to the security of the whole intranet. In this paper, we propose a new system named IoT Eye, which automatically discovers the IoT devices in real time. The IoT Eye detects all the potential IoT target hosts using an innovative two-stage architecture: (1) Scanning suspicious IP segments with stateless TCP SYN scan model and zero copy TCP stack; (2) Identifying each IoT device on various protocols using PI-AC, which is a novel high-performance multi-pattern matching algorithm. The preceding model ensures the IoT Eye searching each newly connected device out in rather small time delay, which minimizes the missing and wrong detection rates. Related intelligence on the active IoT devices linked with the organization's intranets are of great importance to the professionals. Since it can help them: (1) re-examine the borders of large intranets; (2) reduce non-essential device access; (3) fix security vulnerabilities timely.
机译:物联网(IoT)不仅是新一代信息技术的重要组成部分,而且是信息时代的重要发展阶段。近年来,诸如无人机,机器人和可穿戴设备等物联网设备已被广泛使用。对于大多数组织的内部网络,与Internet可访问的IoT设备的无数动态连接始终在许多地方发生。通常是这些时间链接对整个Intranet的安全性造成潜在威胁。在本文中,我们提出了一个名为IoT Eye的新系统,该系统可以实时自动发现IoT设备。 IoT Eye使用创新的两阶段架构检测所有潜在的IoT目标主机:(1)使用无状态TCP SYN扫描模型和零拷贝TCP堆栈扫描可疑IP段; (2)使用PI-AC识别各种协议下的每个IoT设备,这是一种新颖的高性能多模式匹配算法。先前的模型可确保IoT Eye在相当短的时间延迟内搜索出每个新连接的设备,从而最大程度地减少了漏检率和误检率。与组织的Intranet链接的活动IoT设备上的相关智能对专业人员而言非常重要。因为它可以帮助他们:(1)重新检查大型内部网的边界; (2)减少不必要的设备访问; (3)及时修复安全漏洞。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号