Real-time and accurate prediction of charging pile energy demands in electric vehicle (EV) charging networks contributes significantly to load shedding and energy conservation. However, existing methods usually suffer from either data privacy leakage problems or heavy communication overheads. In this paper, we propose a novel blockchain-based personalized federated deep learning scheme, coined $P^{3}$, for privacy-preserving energy demands prediction in EV charging networks. Specifically, we first design an accurate deep learning-based energy demands prediction model for charging piles, by making use of the CNN, BiLSTM, and attention mechanism. Second, we develop a blockchain-based hierarchical and personalized federated learning framework with a consensus committee, allowing charging piles to collectively establish a comprehensive energy demands prediction model in a low-latency and privacy-preserving way. Last, a CKKS cryptosystem based secure communication protocol is crafted to guarantee the confidentiality of model parameters while model training. Extensive experiments on two real charging pile datasets demonstrate the superiorities of the proposed $P^{3}$ scheme in accurately predicting real-time energy demands over state-of-the-art schemes. Further, the $P^{3}$ scheme can achieve reasonably low computational costs, compared with other homomorphic-based schemes, such as Paillier and BFV.

Vortex electromagnetic (EM) waves, with different orbital angular momentum (OAM) modes, have the ability to distinguish the azimuth of radar targets, and then the two-dimensional reconstruction of the targets can be achieved. However, the vortex EM wave imaging methods in published research have no ability to obtain the elevation of the targets, and thus, the three-dimensional spatial structure and richer feature information of the radar target cannot be obtained. Therefore, a three-dimensional imaging method of vortex EM waves with integer- and fractional-order OAM modes is proposed in this paper, which can realize a three-dimensional reconstruction of a radar target based on a uniform circular array (UCA) with two-step imaging. First, the vortex EM wave with integer- and fractional-order OAM modes is generated, and the echo model with different OAM mode types is established. Thereafter, the echo with integer order is processed to obtain the range-azimuth image by fast Fourier transform (FFT). Then, in order to realize the three-dimensional reconstruction, the echo with fractional order is processed by utilizing the butterfly operation and analyzing the characteristics of the fractional Bessel function. Moreover, the resolution and reconstruction precision of the azimuth and elevation are analyzed. Finally, the effectiveness of the proposed method is verified by simulation experiments.

Rising cyber threats have created an immediate demand for Deep Learning (DL) in cybersecurity. Nevertheless, the opaque nature of DL models poses challenges in deploying, collaborating, and assessing their effectiveness in less reliable cybersecurity environments. Despite eXplainable Artificial Intelligence (XAI) playing a role in enhancing cybersecurity analytics, the limited task scope, the propensity for data overfitting, and the stochastic explanations hinder its broader application. To fill the gap, this paper introduces a generic interpretable classification framework, named MetaCluster. MetaCluster generates semantic prototypes for features, patterns, and domains at varying granular levels by following three fundamental steps: embedding representations, acquiring prototypes, and aggregating semantics. These mechanisms guarantee that MetaCluster achieves critical information extraction and reliable classification at minimal cost. The experiments encompass cybersecurity classification tasks and assess the interpretability of the framework. These tasks encompass malware family classification, threat behavior analysis, and malicious traffic identification. In particular, when compared to other DL models, MetaCluster exhibits a significant reduction in parameter consumption by 79.52% to 91.78%, and boosts operational speed up to 71.37%, while its F1 scores remain stable or slightly increase. Additionally, MetaCluster possesses the ability to assess and visually represent the significance of image, text, and statistical features. This capability leads to a reduction of Mean Squared Error (MSE) between expected and actual predictions by 0.0101 to 0.1020.