Smart Jammer Detection for Self-Aware Cognitive UAV Radios

Abstract

Cellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause spectrum scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, the broadcasting nature of CR and the dominant line-of-sight links of UAV makes the Cognitive-UAV-Radios susceptible to jamming attacks. In this paper, we propose a framework to detect smart jammer, which locates and attacks the UAV commands with low Jamming-to-Signal-Power-Ratio (JSR). Smart jammer is more challenging than the types of jammers that always require high power values. Our work focuses on learning a Dynamic Bayesian Network (DBN) to model and analyze the signals' behaviour statistically. A Markov Jump Particle Filter (MJPF) is employed to perform predictions and consequently detect jamming signals. The results are satisfactory in terms of detection probability and false alarm rate that outperform the conventional Energy Detector approach.

A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks

“…The discrete state equations in (15a) and (15b) illustrate how words and generalized letters change over time at various temporal scales. f (1) and f (2) are nonlinear functions that experience random fluctuations in the states influenced by higher levels and characterized by η T ∼ N (0, Q) and η T,e m ∼ N (0, Q). Going down the hierarchy, Equations (15c) and (15d) stand for the continuous state equation and the observation model, explaining the continuous state dynamic evolution and the mapping from the continuous state space to the measurement space, respectively.…”

Jammer Detection in Vehicular V2X Networks

“…A Multi-Domain (Power and spectral) anti jamming scheme is proposed in [10]. Novel Jammer detection methodology using the dynamic Bayesian model framework is proposed in [11] to identify attacks at a symbol level over varying jammer power levels. Malebary et al in [12], highlights different jamming models and their effectiveness in VANETs based on vehicle mobility patterns and jamming behaviour characteristics.…”

“…A whitebox based approach for better explainabilty is an important step in designing advanced jamming detection schemes in tandem with enhanced resource allocation designs. This paper as in [11] proposes a jammer detection paradigm at the PHY layer applying a dynamic Bayesian framework adapted to meet the mobility based demands of the V2X scenario.…”

Integrating cognitive radio with unmanned aerial vehicles

“…They used SDR as a way to enhance UAVs safety by avoiding location jamming, to cause jamming to UAVs remote controls, and to execute jamming attacks to traditional combat net radios using SDR-based UAVs, respectvely. On the other hand, the authors in (Krayani et al, 2020) focused on jamming attacks executed to disable UAV controls. rather than location jamming.…”

“…(ANDRYEYEV; ARTEMENKO; MITSCHELE-THIEL, 2015) (SALEEM; REHMANI; ZEADALLY, 2015) (VONEHR et al, 2016) (Sommer et al, 2020) (Liu et al, 2020)(Krayani et al, 2020) …”