Fast Detection of Burst Jamming for Delay-Sensitive Internet-of-Things Applications

TL;DR

This paper introduces an online principal direction anomaly detection method for rapid burst jamming detection in delay-sensitive IoT applications, leveraging spatial signal properties for real-time attack identification.

Contribution

The paper presents a novel online detection algorithm that does not require prior attacker knowledge and provides theoretical performance bounds for burst jamming detection in IoT.

Findings

High detection accuracy in simulated scenarios

Real-time detection capability demonstrated

Theoretical bounds support practical effectiveness

Abstract

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.