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1. A Machine Learning Approach for Detecting and Classifying Jamming Attacks Against OFDM-based UAVs

   https://doi.org/10.1145/3468218.3469049  

   Pawlak, Jered; Li, Yuchen; Price, Joshua; Wright, Matthew; Al Shamaileh, Khair; Niyaz, Quamar; Devabhaktuni, Vijay (June 2021, Proceedings of the 3rd ACM Workshop on Wireless Security and Machine Learning)

   null (Ed.)

   In this paper, a machine learning (ML) approach is proposed to detect and classify jamming attacks on unmanned aerial vehicles (UAVs). Four attack types are implemented using software-defined radio (SDR); namely, barrage, single-tone, successive-pulse, and protocol-aware jamming. Each type is launched against a drone that uses orthogonal frequency division multiplexing (OFDM) communication to qualitatively analyze its impacts considering jamming range, complexity, and severity. Then, an SDR is utilized in proximity to the drone and in systematic testing scenarios to record the radiometric parameters before and after each attack is launched. Signal-to-noise ratio (SNR), energy threshold, and several OFDM parameters are exploited as features and fed to six ML algorithms to explore and enable autonomous jamming detection/classification. The algorithms are quantitatively evaluated with metrics including detection and false alarm rates to evaluate the received signals and facilitate efficient decision-making for improved reception integrity and reliability. The resulting ML approach detects and classifies jamming with an accuracy of 92.2% and a false-alarm rate of 1.35%. 

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2. Software Defined Radio Injection-Locking using a GPS signal for multichannel coherent receivers

   https://doi.org/10.1109/AERO55745.2023.10115547  

   Some, Evariste; Gasiewski, Albin J (March 2023, IEEE)

   This paper explores the potential benefits of combining the use of injection-locking techniques with GPS signals as a common clock source when applied to a low-cost Software Defined Radio (SDR) to improve the accuracy of coherent multiple receivers. Coherent systems impose severe requirements on the frequency stability of the signal source at the receiver. In this work, injection-locked oscillators are used as local clock receivers, which inherently synchronizes the SDR analog digital converter (ADCs) sampling times and keeps the local oscillator locked on to the GPS stimulus periodic signal. This paper illustrates the hardware modifications needed for to the injection locking oscillators of eight RTL-SDR radios and the theory behind it, and experimentally measures the degree of coherency in the frequency, phase and time synchronization to verify the proposed method. The coherency demonstrated in the results prove the feasibility of using beamforming, multiple input multiple output (MIMO) and RF transmitter geo-localization. 

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3. Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis

   Kouchaki, Mohammadreza; Zhang, Minglong; Abdalla, Aly S; Lan, Guangchen; Brinton, Christopher G; Marojevic, Vuk (December 2024, 2024 22nd International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt))

   In the rapidly evolving landscape of 5G technology, safeguarding Radio Frequency (RF) environments against sophisticated intrusions is paramount, especially in dynamic spectrum access and management. This paper presents an enhanced experimental model that integrates a self-attention mechanism with a Recurrent Neural Network (RNN)-based autoencoder for the detection of anomalous spectral activities in 5G networks at the waveform level. Our approach, grounded in time-series analysis, processes in-phase and quadrature (I/Q) samples to identify irregularities that could indicate potential jamming attacks. The model's architecture, augmented with a self-attention layer, extends the capabilities of RNN autoen-coders, enabling a more nuanced understanding of temporal dependencies and contextual relationships within the RF spectrum. Utilizing a simulated 5G Radio Access Network (RAN) test-bed constructed with srsRAN 5G and Software Defined Radios (SDRs), we generated a comprehensive stream of data that reflects real-world RF spectrum conditions and attack scenarios. The model is trained to reconstruct standard signal behavior, establishing a normative baseline against which deviations, indicative of security threats, are identified. The proposed architecture is designed to balance between detection precision and computational efficiency, so the LSTM network, enriched with self-attention, continues to optimize for minimal execution latency and power consumption. Conducted on a real-world SDR-based testbed, our results demonstrate the model's improved performance and accuracy in threat detection. 

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4. Covert Millimeter-Wave Communication via a Dual-Beam Transmitter

   https://doi.org/10.1109/GLOBECOM38437.2019.9013359  

   Jamali, Mohammad Vahid; Mahdavifar, Hessam (December 2019, 2019 IEEE Global Communications Conference (GLOBECOM))

   In this paper, we investigate covert communication over millimeter-wave (mmWave) frequencies. In particular, a dual-beam mmWave transmitter, comprised of two independent antenna arrays, attempts to reliably communicate to a receiver Bob when hiding the existence of transmission from a warden Willie. In this regard, operating over mmWave bands not only increases the covertness thanks to directional beams, but also increases the transmission data rates given much more available bandwidths and enables ultra-low form factor transceivers due to the lower wavelengths used compared to the conventional radio frequency (RF) counterpart. We assume that the transmitter Alice employs one of its antenna arrays to form a directive beam for transmission to Bob. The other antenna array is used by Alice to generate another beam toward Willie as a jamming signal with its transmit power changing independently from a transmission block to another block. We characterize Willie's detection performance with the optimal detector and the closed-form of its expected value from Alice's perspective. We further derive the closed-form expression for the outage probability of the Alice-Bob link, which enables characterizing the optimal covert rate that can be achieved using the proposed setup. Our results demonstrate the superiority of mmWave covert communication, in terms of covertness and rate, compared to the RF counterpart. 

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5. Joint Turbo Receiver for LDPC-Coded MIMO Systems Based on Semi-definite Relaxation

   Wang, K.; Ding, Z. (August 2018, IEEE Vehicular Technology Conference)

   Semi-definite relaxation (SDR) has demonstrated the capability of approaching maximum-likelihood (ML) performance. In this work, we first develop a new SDR-based detector that exploits forward error correction (FEC) code information in the detection stage. The joint SDR detector substantially improves overall receiver performance by generating highly reliable information to downstream decoder. For further performance improvement, we integrate the joint SDR detector with decoder using a feedback link to form an iterative turbo receiver. Meanwhile, we propose a simplified SDR receiver that solves only one SDR problem per codeword instead of solving multiple SDR problems in the iterative turbo processing. This simplification significantly reduces the complexity of SDR turbo receiver, while maintaining a similarly superior error performance. 

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