Radio Frequency Classification and Anomaly Detection using Convolutional Neural Networks

Conn, Marvin; Josyula, Darsana P.

doi:10.1109/radar.2019.8835662

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…Authors in [17] proposed an unsupervised anomaly detection method for the CR using LSTM mixture density networks applied to time series data. Deep predictive coding neural networks for radiofrequency anomaly detection in wireless systems have been proposed in [18] and [19].…”

Section: Related Workmentioning

confidence: 99%

“…Prediction at the discrete level can be optimized if the measurement votes for the most probable particles (generated by the PF) all the time as discussed in Subsection IV-C1. During abnormal situation, the most probable particles are not voted by the observation providing by that a high D KL as shown in (19). This means that the dynamic transition matrix (π( Sm t )) must be changed in a way that it will be supported by the observation (λ( St m )) which leads to minimize the KLD N defined in (18), and thus the overall KLDA will decrease.…”

Section: ) Update Transition Matrixmentioning

confidence: 99%

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An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

Krayani

Alam

Nallanathan

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

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In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio's long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer's signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer's activities.

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Section: Related Workmentioning

confidence: 99%

Section: ) Update Transition Matrixmentioning

confidence: 99%

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

Krayani

Alam

Nallanathan

et al. 2022

IEEE Trans. Cogn. Commun. Netw.

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show abstract

“…jammer attacks) is the first essential step to protect the Cognitive-UAV-Radios effectively. Anomaly detection has been addressed in several works through a machine learning data-driven approach [13]- [16]. Authors in [13] proposed an unsupervised anomaly detection method for the CR using long-short-term memory mixture density networks applied to time series data by considering only the In-Phase (I) components of digital radio transmissions.…”

Section: Introductionmentioning

confidence: 99%

“…The work proposed in [14] uses an adversarial autoencoder relying on features (as power spectral density, signal bandwidth and center frequency) which require an additional effort to be extracted and could be inconvenient in the UAV scenario. The methods proposed in [15], [16] are based on video frame predictor and Convolutional Neural Networks (CNN), respectively; this requires the generation of video frames and waveforms images that can be unfeasible at the UAV level, because of the battery and power consumption limitations.…”

Section: Introductionmentioning

confidence: 99%

Smart Jammer Detection for Self-Aware Cognitive UAV Radios

Krayani

Baydoun

Gao

et al. 2020

2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications

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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.

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Anomaly Detection with Autoencoders for Spectrum Sharing and Monitoring

Tschimben

Gifford

2022

2022 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR)

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Radio Frequency Classification and Anomaly Detection using Convolutional Neural Networks

Cited by 8 publications

References 16 publications

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

Smart Jammer Detection for Self-Aware Cognitive UAV Radios

Anomaly Detection with Autoencoders for Spectrum Sharing and Monitoring

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