Quantum computing is a game-changing technology that affects modern cryptography and security systems including distributed energy resources (DERs) systems. Since the new quantum era is coming soon in 5–10 years, it is crucial to prepare and develop quantum-safe DER systems. This paper provides a comprehensive review of vulnerabilities caused by quantum computing attacks, potential defense strategies, and remaining challenges for DER networks. First, new security vulnerabilities and attack models of the cyber-physical DER systems caused by quantum computing attacks are explored. Moreover, this paper introduces potential quantum attack defense strategies including Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC), which can be applied to DER networks and evaluates defense strategies. Finally, remaining research opportunities and challenges for next-generation quantum-safe DER are discussed.

For passive sonar arrays, when far-field sources lie in a masking region caused by near-field interferences, direction-of-arrival (DOA) estimations of the interfered far-field sources become difficult. To mitigate the interference, we propose a near-field interference mitigation (NFIM) beamformer that utilizes subarrays and a proposed azimuth-domain filter without any source constraint. For the proposed method, this paper mathematically analyzes the masking region and the subarray configuration to separate the beams of the far-field source from the near-field interference and designs the proposed azimuth-domain filter in the proposed zeta domain. The suppression filter is implemented for all subarrays to mitigate the interferences, and the filtered outputs of all subarrays are combined to suppress residual interferences. Computer simulations show that the proposed NFIM beamformer mitigates the interferences and increases the DOA estimation performance compared with those of conventional methods.

In this paper, we propose a high resolution grid-based deep learning source localization that precisely estimates the locations of near-field sources without a priori knowledge of the number of sources. The proposed method consists of a principal component analysis network (PCAnet) and a spatial spectrum network (Sp2net). The proposed PCAnet calculates the noise spaces of the received signals by convolutional layers without a priori knowledge or the estimation of the number of sources and has the lower complexity than eigenvalue decomposition (EVD). The proposed Sp2net calculates the spatial spectrum with a linear layer from the output of the PCAnet and classifies dense location grids with a convolutional neural network (CNN). From the spatial spectrum, this paper also proposes an activation function to enlarge the values at the grid points where the near-field sources exist, which are differentiable for all input values. Then, the direction of arrivals (DOAs) and the ranges of the near-field sources are estimated with high resolution. Computer simulations demonstrated that the proposed method had better DOA and range estimation performances than those of the conventional methods.

This paper proposes a covert underwater acoustic communication scheme to mimic dolphin whistles by continuously varying the carrier frequency and by using antipodal symbol modulation. The continuously varying carrier frequency precisely mimics dolphin whistles, while the conventional method utilizes discrete carrier-frequencies. The antipodal symbol modulation increases bit error rate (BER) performance by a larger Euclidean distance between symbols than conventional chirp spread spectrum (CSS). BER performance was tested by computer simulations and practical ocean experiments, and the degree of mimic (DoM) was evaluated by correlation coefficients and the mean opinion score (MOS) test. BER of the proposed method attains about a 3 dB SNR gain compared with that of conventional CSS, and the DoM of the proposed scheme demonstrates a larger correlation coefficient and a 39% greater MOS score than that of conventional CSS.

For underwater acoustic covert communications, biomimetic covert communications have been developed using dolphin whistles. The conventional biomimetic covert communication methods transmit slightly different signal patterns from real dolphin whistles, which results in a low degree of mimic (DoM). In this paper, we propose a novel biomimetic communication method that preserves the large DoM with a low bit error rate (BER). For the transmission, the proposed method utilizes the various contours of real dolphin whistles with the link information among consecutive whistles, and the proposed receiver uses machine learning based whistle detectors with the aid of the link information. Computer simulations and practical ocean experiments were executed to demonstrate the better BER performance of the proposed method. Ocean experiments demonstrate that the BER of the proposed method was 0.002, while the BER of the conventional Deep Neural Network (DNN) based detector showed 0.36.

Underwater acoustic (UWA) biomimicking communications have been developed for covert communications. For the UWA covert communications, it is difficult to achieve the bit error rate (BER) and the degree of mimic (DoM) performances at the same time. This paper proposes a biomimicking covert communication method to increase both BER and DoM (degree of mimic) performances based on the Time Frequency Shift Keying (TFSK). To increase DoM and BER performances, the orthogonality requirements of the time- and frequency-shifting units of the TFSK are theoretically derived, and the whistles are multiplied by the sequence with a large correlation. Two-step DoM assessments are also developed for the long-term whistle signals. Computer simulations and practical lake and ocean experiments demonstrate that the proposed method increases the DoM by 35% and attains a zero BER at −6 dB of Signal to Noise Ratio (SNR).