Large-scale multi-objective optimization problems (LSMOPs) face computational challenges due to high-dimensional search spaces and variable correlations. Existing algorithms often struggle to balance convergence, diversity, and efficiency in high-dimensional scenarios. We propose ATSPCA, a two-stage adaptive algorithm employing PCA-guided perturbation. The first stage uses PCA-based dynamic variable screening to identify key decision variable groups, applying adaptive Gaussian perturbation to guide optimization. The second stage enhances convergence and diversity through an improved competitive swarm optimizer (CSO) and reference pointbased environmental selection. An adaptive transition mechanism bridges the two stages. Tested on LSMOP and WFG benchmarks against state-of-the-art large-scale multi-objective evolutionary algorithms(LSMOEAs), ATSPCA demonstrates superior performance.

Industrial Internet of Things (IIoT) is an important part of industrial infrastructure but facing serious and evolving security threats in recent years. Deep learning has been widely considered as a promising solution for enhancing the security of IIoT. However, these existing deep learning models utilized in the intrusion detection of IIoT are manually developed that not only greatly rely on the experience of the designers but also is lack of utility due to the high model complexity. By taking into account the trade-off between the model performance and model complexity, this article makes the first attempt to propose a multi-objective joint optimization method of neural architecture search (NAS) and hyper-parameter optimization (HPO) based on multi-objective discrete extremal optimization (MODEO) to automatically design a lightweight convolutional neural network (CNN) for the intrusion detection task of IIoT, abbreviated as MODEO-CNN. A novel hybrid variable-length encoding strategy is developed by combing binary and integer encoding to characterize both the neural architectures including the number of blocks, the blocks-based network topology and the corresponding architecture parameters in CNN block, and some important hyper-parameters including batch size, learning rate, weight optimizer and regularization. The individual-based discrete multi-objective evolutionary process of MODEO is designed to obtain the Pareto-optimal CNN models. Three widely-used IIoT intrusion detection datasets, including the Gas Pipeline, BoT-IoT, and Power System Attack datasets, have been used to illustrate the superiority of the proposed MODEO-CNN over the state-of-the-art hand-craft models and two single-objective fixed-length blocks-based NAS models in terms of accuracy, precision, recall,$F_{1}$-Score, and model's million floating point operations.

Online ride-hailing (ORH) services have become indispensable for our travel needs, offering the convenience of easily locating the nearest driver for riders through ride matching algorithms. However, existing ORH systems, such as Lyft and Didi, require users (both riders and drivers) to disclose their real-time location information during the matching process, thus giving rise to serious privacy concerns. Despite the proposal of various privacy-preserving ride-matching schemes, they remain insufficient in addressing potential malicious behaviors from the ORH server, such as colluding with designated drivers and deviation from computation protocols to interfere with the matching process. These behaviors lead to non-optimal matching results for riders. To address these issues, we present EMPRide, an efficient and privacy-preserving ride-matching scheme resistant to malicious ORH server. In EMPRide, we design an efficient and accurate computation of distances between users protocol, which integrates road network embedding and secure two-party computation. Additionally, we design a verification protocol that allows riders to verify the correctness of computed distances and matching results. Crucially, the communication overhead for riders in EMPRide remains constant, irrelevant to the number of available drivers. Our evaluation using real-world datasets demonstrates that EMPRide significantly outperforms existing solutions. Specifically, under identical conditions, in EMPRide, the computation speed on the ORH server is$19.22\times $faster and the communication cost is$8.08\times $less than state-of-the-art approaches. Moreover, riders experience a speed improvement of 4.84 orders of magnitude with$1.30\times $less communication, while drivers benefit from a 4.79 orders of magnitude speed increase with$1.45\times $less communication.

Federated learning (FL) has attracted widespread attention because it supports the joint training of models by multiple participants without moving private dataset. However, there are still many security issues in FL that deserve discussion. In this paper, we consider three major issues: 1) how to ensure that the training process can be publicly audited by any third party; 2) how to avoid the influence of malicious participants on training; 3) how to ensure that private gradients and models are not leaked to third parties. Many solutions have been proposed to address these issues, while solving the above three problems simultaneously is seldom considered. In this paper, we propose a publicly auditable and privacy-preserving federated learning scheme that is resistant to malicious participants uploading gradients with wrong directions and enables anyone to audit and verify the correctness of the training process. In particular, we design a robust aggregation algorithm capable of detecting gradients with wrong directions from malicious participants. Then, we design a random vector generation algorithm and combine it with zero sharing and blockchain technologies to make the joint training process publicly auditable, meaning anyone can verify the correctness of the training. Finally, we conduct a series of experiments, and the experimental results show that the model generated by the protocol is comparable in accuracy to the original FL approach while keeping security advantages.

As the deployment of blockchains expands across various industries, the demand for exchanging digital assets among blockchain users has risen. Most of existing solutions either solely support asset exchanges among users on the same blockchain, or have limitations by only enabling cross-chain asset exchanges among a few specific blockchains or requiring an intermediary to involve in the cross-chain transaction. To address this problem, in this paper, we propose the concept of cross-chain digital asset marketplace which enables users across different blockchains to exchange their assets securely and efficiently. We then propose a privacy-preserving and adaptive cross-chain digital asset marketplace scheme, denoted as PACDAM. It adaptively matches purchasers’ requests and ensures atomic and privacy-preserving cross-chain transactions. Built on adaptor signatures and randomizable time-lock puzzles, the cross-chain transaction procedure only relies on the underlying blockchain for signature verification, making PACDAM compatible with various blockchains. Furthermore, this protocol eliminates the necessity for third-party involvement (e.g., brokers) in cross-chain transactions, leading to a substantial enhancement in system efficiency and scalability. We also give a comprehensive security analysis of PACDAM, demonstrating its robustness against common attacks and preserving the privacy of transaction participants. Finally, we conduct a series of experiments, and the results validate the effectiveness of our proposed scheme.

As a typical application of mobile crowdsourcing, streaming media has been attracting increasing attention since recent years. However, traditional streaming media platforms, such as Netflix, Disney+, and Hulu, may suffer some problems like inflexible billing modes, lacking sustainability in the incentive mechanisms, and management censorship. These problems may lead to a decrease in user participation rate, which will directly affect the interests of streaming media platforms. To address these issues, we propose a secure, efficient and flexible streaming media platform framework based on blockchain and well-designed smart contracts. In particular, we design a new billing model based on pay-as-you-go strategy and a new incentive mechanism with probabilistic payment technique. To improve the fairness of our incentive model, we introduce a secondary fee refund protocol where a user’s second consecutive payment could be refunded, which in turn can attract more users to participate in the platform. Since blockchain has the natural properties of decentralization and transparency, the proposed framework is resistant to censorship and enables the transactions to be publicly auditable. Based on the proposed framework, we have implemented two streaming media platform schemes. Scheme I relies primarily on smart contracts to implement the framework’s functionality, while Scheme II moves the main flow of framework to off-chain channels. As the execution of smart contracts requires transaction fees, Scheme I is more expensive but can provide much more security and accountability as well. Scheme II can execute the transaction process much faster and with only a small transaction fee. Finally, we deployed these two schemes on Ropsten and conduct a series of experiments. The results show the effectiveness and efficiency of the proposed schemes.

The optimal generation of initial connection weight parameters and dynamic updating strategies of connection weights are critical for adjusting the performance of back-propagation (BP) neural networks. This paper presents an adaptive fractional-order BP neural network abbreviated as PEO-FOBP for handwritten digit recognition problems by combining a competitive evolutionary algorithm called population extremal optimization and a fractional-order gradient descent learning mechanism. Population extremal optimization is introduced to optimize a large number of initial connection weight parameters and fractional-order gradient descent learning mechanism is designed to update these connection weight parameters adaptively during the evolutionary process of fractional-order BP neural network. The extensive experimental results for a well-known MNIST handwritten digits dataset have demonstrated that the proposed PEO-FOBP outperforms the original fractional-order BP neural network and the traditional integer-order BP neural network in terms of training and testing accuracies.

As a typical kind of the Internet of Things, smart grid has attracted a lot of attentions. The power energy management of smart grid is of great importance for energy distribution, system security, and market economics. One of the most important issues is the accurate and stable prediction of wind speed for the optimal operation and management of wind power generations connected to smart grid. In this paper, a novel two-layer nonlinear combination method termed as EEL-ELM is developed for short-term wind speed prediction problems, such as 10-min ahead and 1-h ahead. The first layer is based on extreme learning machine (ELM), Elman neural network (ENN), and long short term memory neural network (LSTM) to separately forecast wind speed by making use of their merits of calculation speed or strong ability in forecasting, and obtain three forecasting results. Then, we propose the second layer by making use of ELM-based nonlinear aggregated mechanism to alleviate the inherent weakness of single method and linear combination. Two real-world case studies, gathered from Inner Mongolia's wind farm in China, are implemented to demonstrate the effectiveness of the proposed EEL-ELM method. By comparing with other eight wind speed prediction methods, the simulation results reveal that EEL-ELM can achieve better forecasting performance according to three evaluation metrics and three statistical tests.

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. The standard ABC is weak at the local-search capability and precision. Extremal Optimization (EO) is a general-purpose heuristic method which has strong local-search capability and has been successfully applied to a wide variety of hard optimization problems. In order to strengthen the local-search capability of ABC, this work proposes a novel hybrid optimization method, called ABC-EO algorithm, through introducing EO to ABC. The simulation results show that the performance of the proposed method is as good as or superior to those of the state-of-the-art algorithms in complex numerical optimization problems.

Certificateless signature is an interesting cryptographic primitive which does not suffer from the inherent key escrow problem of identity‐based cryptography and the costly certificate management problem of traditional public key cryptography. Since security proofs in the random oracle model can only be viewed as heuristic arguments and cannot ensure the security in the real implementation, certificateless signature schemes with security proofs in the standard model (i.e. without random oracles) is more desirable. Some attempts have been devoted to propose certificateless signature schemes in the standard model, whereas all of these schemes are later shown to be either insecure or flawed in the security proofs. Recently, a new certificateless signature scheme in the standard model has been proposed. However, in this study the authors show that this scheme cannot resist the key replacement attack, and hence it is not existentially unforgeable.