  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.
