The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. They also tend to ignore attacker localization in the collaborative authentication process. To overcome these challenges, a novel collaborative authentication scheme is proposed, where multiple edge devices act as cooperative peers to assist the service provider in distributively authenticating its users by estimating their received signal strength indicator (RSSI) and mobility trajectory (TRA). More explicitly, a distributed learning-based collaborative authentication algorithm is conceived, where the cooperative peers update their authentication models locally, thus the network congestion and response time remain low. Moreover, a situation-aware secure group update algorithm is proposed for autonomously refreshing the set of cooperative peers in the dynamic environment. We also develop an algorithm for localizing a malicious user by the cooperative peers once it is identified. The simulation results demonstrate that the proposed scheme is eminently suitable for both indoor and outdoor communication scenarios, and outperforms some existing benchmark schemes.
翻译:传统的无线网络设备认证通常依赖于安全服务器和集中处理,导致延迟很长并存在单点故障的风险。虽然协作认证方案可以缓解这些挑战,但其性能仍受到数据收集和聚合结果的僵化限制。他们也往往忽略了在协作认证过程中对攻击者的本地化定位。为了克服这些挑战,提出了一种新颖的协作认证方案,其中多个边缘设备作为合作节点协助服务提供商通过估计接收信号强度指示器(RSSI)和移动轨迹(TRA)来分布式认证其用户。更明确地说,构思了一种基于分布式学习的协作认证算法,其中合作性节点在本地更新其验证模型,因此网络拥堵和反应时间保持较低。此外,还提出了一种情境感知安全组更新算法,用于在动态环境中自动刷新协作节点集合。我们还开发了一种算法,通过协作节点对恶意用户进行本地化定位一旦识别出来。仿真结果表明,所提出的方案非常适用于室内和室外通信场景,并且优于一些现有的基准方案。