Biblio

Vehicular Fog Computing (VFC) has been proposed to address the security and response time issues of Vehicular Ad Hoc Networks (VANETs) in latency-sensitive vehicular network environments, due to the frequent interactions that VANETs need to have with cloud servers. However, the anonymity protection mechanism in VFC may cause the attacker to launch Sybil attacks by fabricating or creating multiple pseudonyms to spread false information in the network, which poses a severe security threat to the vehicle driving. Therefore, in this paper, we summarize different types of Sybil attack detection mechanisms in VFC for the first time, and provide a comprehensive comparison of these schemes. In addition, we also summarize the possible impacts of different types of Sybil attacks on VFC. Finally, we summarize challenges and prospects of future research on Sybil attack detection mechanisms in VFC.

Data are the basis in Digital Twin (DT) to set up bidirectional mapping between physical and virtual spaces, and realize critical environmental sensing, decision making and execution. Thus, trustworthiness is a necessity in data content as well as data operations. A dual blockchain framework is proposed to realize comprehensive data security in various DT scenarios. It is highly adaptable, scalable, evolvable, and easy to be integrated into Digital Twin Network (DTN) as enhancement.

In photovoltaic (PV) systems, machine learning-based methods have been used for fault detection and diagnosis in the past years, which require large amounts of data. However, fault types in a single PV station are usually insufficient in practice. Due to insufficient and non-identically distributed data, packet loss and privacy concerns, it is difficult to train a model for diagnosing all fault types. To address these issues, in this paper, we propose a decentralized federated learning (FL)-based fault diagnosis method for insulated gate bipolar transistor (IGBT) open-circuits in PV inverters. All PV stations use the convolutional neural network (CNN) to train local diagnosis models. By aggregating neighboring model parameters, each PV station benefits from the fault diagnosis knowledge learned from neighbors and achieves diagnosing all fault types without sharing original data. Extensive experiments are conducted in terms of non-identical data distributions, various transmission channel conditions and whether to use the FL framework. The results are as follows: 1) Using data with non-identical distributions, the collaboratively trained model diagnoses faults accurately and robustly; 2) The continuous transmission and aggregation of model parameters in multiple rounds make it possible to obtain ideal training results even in the presence of packet loss; 3) The proposed method allows each PV station to diagnose all fault types without original data sharing, which protects data privacy.

Subscription permanent identifier has been concealed in the 5G systems by using the asymmetric encryption scheme as specified in standard 3GPP TS 33.501 to protect the subscriber privacy. The standardized scheme is however subject to the SUPI guess attack as the public key of the home network is publicly available. Moreover, it lacks the inherent mechanism to prevent SUCI replay attacks. In this paper, we propose three methods to enhance the security of the 3GPP scheme to thwart the SUPI guess attack and replay attack. One of these methods is suggested to be used to strengthen the security of the current subscriber protection scheme.

In a wireless system, a signal map shows the signal strength at different locations termed reference points (RPs). As access points (APs) and their transmission power may change over time, keeping an updated signal map is important for applications such as Wi-Fi optimization and indoor localization. Traditionally, the signal map is obtained by a full site survey, which is time-consuming and costly. We address in this paper how to efficiently update a signal map given sparse samples randomly crowdsourced in the space (e.g., by signal monitors, explicit human input, or implicit user participation). We propose Compressive Signal Reconstruction (CSR), a novel learning system employing Bayesian compressive sensing (BCS) for online signal map update. CSR does not rely on any path loss model or line of sight, and is generic enough to serve as a plug-in of any wireless system. Besides signal map update, CSR also computes the estimation error of signals in terms of confidence interval. CSR models the signal correlation with a kernel function. Using it, CSR constructs a sensing matrix based on the newly sampled signals. The sensing matrix is then used to compute the signal change at all the RPs with any BCS algorithm. We have conducted extensive experiments on CSR in our university campus. Our results show that CSR outperforms other state-of-the-art algorithms by a wide margin (reducing signal error by about 30% and sampling points by 20%).