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Advanced persistent threat (APT) is a kind of stealthy, sophisticated, and long-term cyberattack that has brought severe financial losses and critical infrastructure damages. Existing works mainly focus on APT defense under stable network topologies, while the problem under time-varying dynamic networks (e.g., vehicular networks) remains unexplored, which motivates our work. Besides, the spatiotemporal dynamics in defense resources, complex attackers’ lateral movement behaviors, and lack of timely defense make APT defense a challenging issue under time-varying networks. In this paper, we propose a novel game-theoretical APT defense approach to promote real-time and optimal defense strategy-making under both periodic time-varying and general time-varying environments. Specifically, we first model the interactions between attackers and defenders in an APT process as a dynamic APT repair game, and then formulate the APT damage minimization problem as the precise prevention and control (PPAC) problem. To derive the optimal defense strategy under both latency and defense resource constraints, we further devise an online optimal control-based mechanism integrated with two backtracking-forward algorithms to fastly derive the near-optimal solution of the PPAC problem in real time. Extensive experiments are carried out, and the results demonstrate that our proposed scheme can efficiently obtain optimal defense strategy in 54481 ms under seven attack-defense interactions with 9.64% resource occupancy in stimulated periodic time-varying and general time-varying networks. Besides, even under static networks, our proposed scheme still outperforms existing representative APT defense approaches in terms of service stability and defense resource utilization. [ABSTRACT FROM AUTHOR]

Due to the limited battery and computing resource, offloading unmanned aerial vehicles (UAVs)’ computation tasks to ground infrastructure, e.g., vehicles, is a fundamental framework. Under such an open and untrusted environment, vehicles are reluctant to share their computing resource unless provisioning strong incentives, privacy protection, and fairness guarantee. Precisely, without strategy-proofness guarantee, the strategic vehicles can overclaim participation costs so as to conduct market manipulation. Without the fairness provision, vehicles can deliberately abort the assigned tasks without any punishments, and UAVs can refuse to pay by the end, causing an exchange dilemma. Lastly, the strategy-proofness and fairness provision typically require transparent payment/task results exchange under public audit, which may disclose sensitive information of vehicles and make the privacy preservation a foremost issue. To achieve the three design goals, we propose SEAL, an integrated framework to address Strategy-proof, fair, and privacy-prEserving UAV computation offLoading. SEAL deploys a strategy-proof reverse combinatorial auction mechanism to optimize UAVs’ task offloading under practical constraints while ensuring economic-robustness and polynomial-time efficiency. Based on smart contracts and hashchain micropayment, SEAL implements a fair on-chain exchange protocol to realize the atomic completion of batch payments and computing results in multi-round auctions. In addition, a privacy-preserving off-chain auction protocol is devised with the assistance of the trusted processor to efficiently protect vehicles’ bid privacy. Using rigorous theoretical analysis and extensive simulations, we validate that SEAL can effectively prevent vehicles from manipulating, ensure privacy protection and fairness, improve the offloading efficiency, and reduce UAV’s energy costs and expenses with low overheads. [ABSTRACT FROM AUTHOR]

Proof of work (PoW), as the representative consensus protocol for blockchain, consumes enormous amounts of computation and energy to determine bookkeeping rights among miners but does not achieve any practical purposes. To address the drawback of PoW, we propose a novel energy-recycling consensus mechanism named platform-free proof of federated learning (PF-PoFL), which leverages the computing power originally wasted in solving hard but meaningless PoW puzzles to conduct practical federated learning (FL) tasks. Nevertheless, potential security threats and efficiency concerns may occur due to the untrusted environment and miners’ self-interested features. In this paper, by devising a novel block structure, new transaction types, and credit-based incentives, PF-PoFL allows efficient artificial intelligence (AI) task outsourcing, federated mining, model evaluation, and reward distribution in a fully decentralized manner, while resisting spoofing and Sybil attacks. Besides, PF-PoFL equips with a user-level differential privacy mechanism for miners to prevent implicit privacy leakage in training FL models. Furthermore, by considering dynamic miner characteristics (e.g., training samples, non-IID degree, and network delay) under diverse FL tasks, a federation formation game-based mechanism is presented to distributively form the optimized disjoint miner partition structure with Nash-stable convergence. Extensive simulations validate the efficiency and effectiveness of PF-PoFL. [ABSTRACT FROM AUTHOR]

Vehicular networks which are paralyzed by natural disasters is faced with communication dilemma. Through building a decentralized communication network, unmanned aerial vehicles (UAVs) with high mobility and flexibility are expected to be the solution to the post-disaster vehicular networks. The blockchain technology has been widely used in UAV networks to provision the prompt security of distributed communications. However, existing works ignore the dynamics of the network in that due to high mobility, distributed UAVs cannot timely connect to the backbone to synchronize blockchain transactions. The delay of synchronization can result in severe security issues. On addressing the issues, this paper proposes UAVs-aided blockchain offline transactions to ensure the security and effectiveness of delay-tolerant blockchain transactions when UAVs are offline. In specific, we consider vehicle-to-vehicle (V2V) charging transactions in post-disaster vehicular networks. By establishing offline channel between charging and discharging electric vehicles (EVs) by hashed time locked contract (HTLC), we design a UAVs aided penalty algorithm with accountable assertions to prevent deposit forging attacks and double-spending attacks. In addition, considering the selfishness of EVs, a Stackelberg game based incentive scheme is developed to encourage EVs to participate the offline transactions and to improve their trust values. By suing the mechanisms above, our proposal addresses the security of offline EV charging, as well as the selfishness of participant. Using extensive simulations, we demonstrate that the proposed scheme can realize secure transactions among EVs and can effectively improve the utilities of EVs through the comparison with conventional schemes. [ABSTRACT FROM AUTHOR]

With the ever-increasing demand for the content delivery services in autonomous vehicular networks (AVNs), caching popular contents in the edge nodes in advance is expected to reduce the transmission delay. Current works on the contents cached in connected and autonomous vehicles (CAVs) or roadside units (RSUs) are facing the problems of limited caching size and high deployment cost. In this paper, by exploiting the advantages of high caching space and flexibility of truck platoons composed of connected and autonomous trucks (CATs), we propose a secure content delivery service for CATs based on coalition formation game. Firstly, in order to protect the security and privacy of content delivery services, a differential privacy model is proposed to protect the sensitive information of CATs. Meanwhile, the differential privacy model is combined with the incentive based trust evaluation model to monitor the behaviors of CATs. In the incentive based models, CATs are encouraged to improve their trust values to obtain higher utilities and find a balance between confidence levels and utilities. Moreover, a coalition formation game is established among CATs with the same driving route, in which all CATs can maximize their utilities with the formation of several minor coalitions. Finally, we conduct extensive simulations to demonstrate the effectiveness and superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) mobile communication network is expected to provide world-connected smart and autonomous services by leveraging artificial intelligence (AI) technologies. In particular, federated learning (FL) is advocated to promote the implementations of ubiquitous AI applications in 6G, where massive distributed data is utilized to cooperatively train AI models as well as protect privacy and reduce resource consumption. However, due to the large number of interactions among mobile devices or infrastructures in FL, the AI inevitably suffers from a series of potential privacy and security risks. In this article, we investigate the security and privacy of AI-enabled 6G. Specifically, we first present the AI-enabled 6G architecture, including the space-air-ground-ocean integrated network (SAGOIN) architecture and two different FL-based AI model training frameworks in 6G. Furthermore, we discuss in detail the privacy and security threats, including cheating attack, low-quality local model training attack, and privacy stealing attack. Afterwards, we present a case study to design the security and privacy preservation scheme by integrating the trust evaluation, Q-learning based incentive, and local differential privacy (LDP) mechanism. Finally, we give out several future research directions in promising AI-enabled 6G. [ABSTRACT FROM AUTHOR]

Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, as compared to other benchmark schemes. [ABSTRACT FROM AUTHOR]

As the natural disasters may destroy the ground communication infrastructures for the transportation systems, the communication relief in post-disaster networks is more crucial to reduce risk loss. The growing application of unmanned aerial vehicles (UAVs) holds great potential for disaster communication relief due to its flexibility and functionalities. In this paper, we investigate the channel allocation and data delivery problems for UAV-assisted cooperative transportation communications in post-disaster networks to provide communication and data delivery services for affected users. Specifically, we first introduce the UAV-assisted communication relief system, in which UAVs equipped with the communication and caching functionalities are deployed as the aerial base stations in post-disaster regions. Then, we propose the channel allocation scheme between UAVs and users by taking the interferences into consideration, and obtain the channel allocation strategy to improve the network throughput. Based on the optimal channel allocation strategy, users can deliver their data to UAVs for backup. Next, we propose the data delivery scheme to cope with the pricing problem for UAVs and the data delivery strategy for users to improve the efficiency of data delivery, with the objective of maximizing the utilities of both UAVs and users. The optimal strategy for both UAVs and users are derived according to the analysis of Stackelberg game. Finally, we conduct simulations to evaluate the performance of the proposed channel allocation and data delivery scheme, and the numerical results demonstrate that the proposed scheme can significantly improve the efficiency and effectiveness of channel allocation and data delivery in post-disaster networks, compared with benchmark schemes. [ABSTRACT FROM AUTHOR]

Feature extraction is a key step for fault diagnosis of rolling bearings based on vibration measurements. Lempel-Ziv complexity (LZC) has been widely applied to extracting fault features. However, LZC with single-scale analysis may result in the loss of fault information. Then, the multiscale LZC is proposed to uncover the multiscale features. Nevertheless, multiscale LZC would shorten the length of sequences, leading to inaccurate calculation results. Moreover, the mean operation during the coarse-grained process of the traditional multiscale analysis cannot reveal the dynamic changes of the original sequences, which may also result in the loss of potential information. Thus, this paper develops a feature extraction method called generalized variable-step multiscale LZC (GVSMLZC) for the vibration-based fault diagnosis of rolling bearings. GVSMLZC reveals more information and gains more robust complexity by improving the coarse-grained process. Moreover, GVSMLZC enhances the capability of mining dynamic characteristics by generalizing first-order moments to second-order moments. Based on the proposed GVSMLZC, fault type identification and fault detection of life-cycle data schemes are designed to realize the fault diagnosis of rolling bearings. Simulated and experimental results demonstrate that GVSMLZC outperforms multiscale LZC, variable-step multiscale LZC, and multiscale sample entropy in extracting bearing fault information. [ABSTRACT FROM AUTHOR]