In this paper, we investigate the design of a burst jamming detection method for delay-sensitive Internet-of-Things (IoT) applications. In order to obtain a timely detection of burst jamming, we propose an online principal direction anomaly detection (OPDAD) method. We consider the one-ring scatter channel model, where the base station equipped with a large number of antennas is elevated at a high altitude. In this case, since the angular spread of the legitimate IoT transmitter or the jammer is restricted within a narrow region, there is a distinct difference of the principal direction of the signal space between the jamming attack and the normal state. Unlike existing statistical features based batching methods, the proposed OPDAD method adopts an online iterative processing mode, which can quickly detect the exact attack time block instance by analyzing the newly coming signal. In addition, our detection method does not rely on the prior knowledge of the attacker, because it only cares the abrupt change in the principal direction of the signal space. Moreover, based on the high spatial resolution and the narrow angular spread, we provide the convergence rate estimate and derive a nearly optimal finite sample error bound for the proposed OPDAD method. Numerical results show the excellent real time capability and detection performance of our proposed method.
翻译:在本文中,我们调查了延迟敏感互联网干扰应用的爆裂干扰探测方法的设计。 为了及时探测爆裂干扰, 我们建议了一种在线主方向异常探测方法。 我们考虑一环散射频道模型, 配备大量天线的基地台在高空升起。 在这种情况下, 由于合法的IOT发射机或干扰器的角扩散限制在一个狭小区域内, 信号空间的主要方向在干扰攻击和正常状态之间存在明显差异。 与现有的基于批量的统计特征不同, 拟议的OPDAD方法采用了在线迭代处理模式, 通过分析即将到来的信号, 能够快速检测准确的攻击时段实例。 此外, 我们的探测方法并不依赖于攻击器的先前知识, 因为它只关心信号空间主方向的突变。 此外, 根据高空间分辨率和窄角扩散, 我们提供了趋同率的估计数, 并得出了一种近乎最佳的准确度的测试结果, 用于提议的OPDAD 方法。