Automated spectrum monitoring necessitates the accurate detection of low probability of intercept (LPI) radio frequency (RF) anomaly signals to identify unwanted interference in wireless networks. However, detecting these unforeseen low-power RF signals is fundamentally challenging due to the scarcity of labeled RF anomaly data. In this paper, we introduce WANDA (Wireless ANomaly Detection Algorithm), an automated framework designed to detect LPI RF anomaly signals in low signal-to-interference ratio (SIR) environments without relying on labeled data. WANDA operates through a two-step process: (i) Information extraction, where a convolutional neural network (CNN) utilizing soft Hirschfeld-Gebelein-Rényi correlation (HGR) as the loss function extracts informative features from RF spectrograms; and (ii) Anomaly detection, where the extracted features are applied to a one-class support vector machine (SVM) classifier to infer RF anomalies. To validate the effectiveness of WANDA, we present a case study focused on detecting unknown Bluetooth signals within the WiFi spectrum using a practical dataset. Experimental results demonstrate that WANDA outperforms other methods in detecting anomaly signals across a range of SIR values (-10 dB to 20 dB).

The detection of unauthorized frequency hopping (FH) signals has several applications in securing the radio frequency spectrum and achieving spectrum awareness in both tactical and cyber-physical systems. However, the blind detection of adversary FH signals is a challenging task, particularly in low signal-to-noise ratio (SNR) regimes, due to the adoption of dynamic hopping patterns. In this study, we propose a cyclo-stationary signal features-based blind FH signal detection scheme to address this challenge. Our proposed scheme consists of two steps: (i) feature extraction, where cyclic features are extracted from the spectral correlation function of the signals, and (ii) feature classification, where the extracted features are associated with ON/OFF detection states using a trained support vector machine (SVM) classifier. We leverage both binary and one-class SVM classifiers to enable adversary FH signal detection with and without pre-existing signal labels. Extensive simulations are conducted to verify the efficacy of the proposed FH signal detection scheme in low SNR regimes. Simulation results also provide insights into the interplay of various system parameters, such as the numbers of cyclic features and emission bandwidth, on the detection performance of the proposed SVM classifiers.

5G is the beginning of a new era in cellular communication, bringing up a highly connected network with the incorporation of the Internet of Things (IoT). To flexibly operate all the IoT devices over a cellular network, Device-to-Device (D2D) communication standard was developed. However, IoT devices such as drones utilizing 5G D2D services could be a perfect target for malicious attacks as they pose several safety threats if they are compromised. Furthermore, there will be heavy traffic with an increased number of IoT devices connected to the 5G core. Therefore, we propose a lightweight, fast, and reliable authentication mechanism compatible with the 5G D2D ProSe standard mechanisms. Specifically, we propose a distributed authentication with a delegation-based scheme instead of the repeated access to the 5G core network key management functions. Hence, a legitimate drone is authorized by the core network via offering a proxy signature to authenticate itself to other drones. We implemented the proposed protocol in ns-3 that supports 5G D2D-based communication. We also conducted computational calculations on the RaspberryPi3 IoT device to mimic the drone calculation process and delays. The results demonstrate that the proposed protocol is lightweight and reliable.

Unmanned Aerial Vehicles (UAVs), or drones, are increasingly being utilized for public safety circumstances including post-disaster recovery of destroyed communication infrastructure. For instance, drones are temporarily positioned within an affected area to create a wireless mesh network among public safety personnel. To serve the need for high-rate video-based damage assessment, drone-assisted communication can utilize high-bandwidth millimeter wave (mm Wave) technologies such as IEEE 802.11ad. However, short-range mm Wave communication makes it hard for optimally-positioned drones to be authenticated with a centralized network control center. Therefore and assuming that there are potential imposters, we propose two lightweight and fast authentication mechanisms that take into account the physical limitations of mm Wave communication. First, we propose a drone-to-drone authentication mechanism, which is based on proxy signatures from a control center. Accordingly, any newly joining drone can authenticate itself to an exist one rather than attempting to authenticate to the out-of-reach control center. Second, we propose a drone-to-ground authentication mechanism, to enable each drone to authenticate itself to its associated ground users. Such authentication approach is based on challenge-response broadcast type, and it is still utilizing fast proxy signature approach. The evaluation of the proposed authentication mechanisms, conducted using NS-3 implementation of IEEE 802.11ad protocol, show their efficiency and practicality.

Unmanned aerial vehicle (UAV) plays prominent role in enhancing backhaul connectivity and providing extended coverage areas due to its mobility and flexible deployment. To realize these objectives simultaneously, we present a new framework for positioning the UAV to maximize the small-cells backhaul network connectivity characterized by its Fiedler value, the second smallest eigenvalue of the Laplacian matrix representing the network graph, while maintaining particular signal-to-noise ratio constraint for each individual user equipment. Moreover, we show that the localization problem can be approximated by a low complexity convex semi-definite programming optimization problem. Finally, our extensive simulations verify the approximation validity and demonstrate the potential gain of UAV deployment.