With the continuous advancement of the Internet of Things (IoT) and mobile terminal technologies, the massive data generated by end devices poses new challenges for real-time processing in cloud computing. As a beneficial complement to cloud computing, edge computing has garnered significant attention due to its ability to provide low-latency services near the data source. However, user privacy data involved in the task offloading process faces leakage risks in open edge architectures, and existing solutions still lack synergy between privacy protection and computational efficiency. To address this, this paper proposes a privacy-preserving task offloading scheme for end-edge-cloud architectures based on blockchain federated learning. The main contributions are as follows: (1) constructing a collaborative computing architecture that integrates end devices, edge servers, and cloud centers; (2) proposing a privacy-preserving task offloading mechanism that integrates federated learning and blockchain technology, enabling distributed model training and secure auditing without sharing raw data; (3) validating the superiority of the proposed scheme in terms of energy consumption, latency, and privacy protection level through simulation experiments. The experimental results demonstrate that the proposed scheme significantly enhances user privacy protection while maintaining efficient task processing, providing a reliable technical approach for privacy-sensitive applications in edge computing environments.

This paper proposes an Android-oriented zero-day malware detection method named ”Android Zero-Day Guard.” By integrating deep neural networks with zero-shot learning, this approach is capable of identifying emerging threats without prior exposure to malicious samples. The method converts APK files into images and extracts deep features, enabling effective capture of behavioral malware patterns. Experimental results demonstrate that the proposed method achieves a precision of 94.93%, a recall of 93.75%, and an F1-score of 94.28% across multiple malware families. Without relying on dynamic analysis, it exhibits strong detection capability and generalization performance, making it well-suited for the early identification of emerging threats. While the model performs strongly on benchmark datasets, continuous validation on the latest families is essential for deployment in a rapidly evolving threat landscape.

The consensus mechanism is crucial to blockchain performance, making it essential to design a mechanism that aligns with the characteristics of the Internet of Things (IoT). This paper focuses on the application of PBFT consensus mechanism in the Internet of things. However, it is found that the current PBFT consensus mechanisms need to address some problems, such as communication overhead, bandwidth occupation and privacy protection. In this paper, we propose a voting sorting clustering mechanism based on group signatures to enhance the PBFT consensus mechanism (IPBFT), ensuring privacy protection between Internet of Things nodes. Finally, the communication efficiency is improved. The voting sorting clustering method reduces the communication probability with the nodes, and decreases the communication overhead and bandwidth occupation. Experimental results show that compared with other mechanisms, the proposed mechanism increases throughput, reduces communication overhead and bandwidth occupation, and alleviates privacy protection problems.

Sequential recommendations aim to capture user preferences through user historical behavior interaction data in order to make accurate recommendations. Recently, graph convo-lutional networks have achieved remarkable results in the field of sequential recommendations. However, most of them only utilize the original interactive items, ignoring the influence of time and noise information in their interaction sequences. In particular, some of them may pay attention to the time domain information, they also neglect the frequency domain information which can also be utilized to analyze user interests. To address these limitations, considering both time domain and frequency domain perspectives, we propose a novel model, named DFT-SR in short. First, our approach incorporates a timestamp embedding based on a window function to capture the temporal representations of user interaction sequences. Afterward, we replace the self-attention layer in the encoder with a learnable filter module, which comprises two components such as high-frequency and low-frequency functions, utilizing different neural network layers in the frequency domain to hierarchically cover specific frequency ranges. Experimental results demonstrate the superiority of DFT-SR model over other sequence models. The incorporation of frequency-aware filtering and timestamp embedding enhances the performance of sequential recommendations, increasing the HR@20 from 23.71% to 35.70%, and increasing the NDCG@20 from 26.54% to 51.28%.

Federated learning (FL), as a distributed machine learning paradigm, enables multiple users to train machine learning models locally using individual data and then update global model in a privacy-preserving aggregated manner. However, in FL, the users model parameters are at risk of a privacy breach. Furthermore, the aggregation server may forge aggregated results. To address these problems, in this paper, we propose SVCA, a secure and verifiable chained aggregation for privacy-preserving federated learning (PPFL) scheme. Specifically, we first group users and construct a chained aggregation structure, then employ secret sharing to prevent the entire group of users dropout, and finally propose a scheme for secure verification of the aggregation result to ensure the result correctness and the security of the verification process. The security analysis shows that SVCA not only protects the privacy of users but also ensures the training integrity. Extensive experimental results demonstrate the practical performance of SVCA without compromising classification accuracy.

With the continuous upgrading and development of malware attack methods, traditional detection methods have shown a series of serious problems such as low classification accuracy, easy overfitting, and high false alarm rate when facing new malware attacks. To address these challenges, this study introduces an innovative deep convolutional neural network (D-CNN) method that cleverly integrates permission features and API call graphs. Learn high-level abstract representation through DNN and combine it with CNN to build multiscale feature representation, aiming to improve the performance of the detection model and enhance the resistance to new malicious attacks. In order to ensure the standardization and representativeness of the data, the Min–Max method is first used to normalize the permission characteristics to ensure the standardization of the data. Second, the ant colony optimization method is used to achieve dimensionality reduction and prevent over-fitting. This article conducts experiments on Drebin and Google Play Store datasets. The results prove that the hybrid structure of D-CNN exhibits a deeper understanding of the data structure and achieves an accuracy of 96.80%, enabling more comprehensive and accurate malware detection and classification. It outperforms single deep learning methods in detection performance.

In recent years, the Internet of Things (IoT) has penetrated all aspects of our lives through smart cities, health, industries and others that are related to people's livelihood. With the increasing number of IoT devices, more and more personal information is exposed in the network space, which inevitably brings some network security problems. Due to the diversity and heterogeneity of IoT devices, identification of such devices in the complex IoT environments remains a major challenge. Existing deep learning-based device identification methods achieve identification of IoT devices by automatically extracting device traffic features, but usually only single modal features of device traffic are considered, which cannot achieve all-around characterization features of communication traffic and affect the identification results. Therefore, we propose an identification method, termed DMRMTT, that employs a Deep convolutional maxout network and MTT model (Multiple Time-series Transformers) to automatically extract the spatial and temporal features of IoT communication session fingerprints and perform further fusion using the structure of the residual, which makes up for the limitations of the existing methods for studying device traffic. This method can improve the characterization of device traffic behaviour and achieve a more accurate identification of IoT devices. Its efficacy is experimentally validated by using two publicly availbale datasets and compared with existing methods. Results show that our method outperforms other methods in widely used performance metrics and achieves 99.82% identification accuracy, demonstrating its superiority and usefulness in IoT device identification.

Aspect-Based Sentiment Analysis (ABSA) aims to determine the sentiment polarity of certain aspect words in a sentence.Recently, it is a popular approach to fuse the sentences' syntactic information via the dependency tree into the graph neural network.However, how to efficiently utilize the obtained syntactic information is still a challenging problem of this kind of approach.Therefore, this paper proposes a novel Aspect-level Sentiment Analysis model based on Convolutional network with Dependency Tree, named ASAC-DT in short.First, the attention mechanism is utilized to obtain the attention score of the sentence and the aspect word respectively, to improve the connection of the words related to the aspect word in the sentence.Afterwards, by relying on the syntactic information obtained from the dependency tree, the connections of words that are not related to the aspect words are reduced.Finally, the feature information most relevant to the aspect words in the proposed model is extracted through the graph convolutional neural network and the interactive network.Through extensive experimental baselines the proposed ASAC-DT model shows effectiveness in aspect-level sentiment classification and outperforms baselines in accuracy.

Abstract Requirements management (RM) plays an important role in requirements engineering. The development of machine learning (ML) is in full swing, and many ML software management techniques had been used to improve the performance of RM methods. However, as no research study is known that exists systematically to summarise the ML methods used in RM. To fill this gap, this paper adopts the systematic mapping study to survey the state‐of‐the‐art ML methods for RM primary studies and were finally selected in this mapping, which was published on 36 conferences and journals. The 24 factors affecting the ML method of RM are determined, of which 9, 11 and 4 are the three parts of RM, namely requirements baseline maintenance, requirements traceability and requirements change management separately. The 18 objectives of the ML method for RM are summarised, of which 6, 7 and 5 are the three parts of RM. The eight ML methods used in RM and their time sequence are summarised. The 18 evaluation indexes for RM in the ML method are determined, and the performance of these methods on these parameters is analysed. The research direction of this paper is of great significance to the research of researchers in demand management.

Mobile edge computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above-mentioned problems, in this article, the task offloading problem is formulated as an optimization problem, and particle swarm optimization (PSO) and quantum PSO based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared with recent advanced strategies, namely ant colony optimization, multiagent deep deterministic policy gradients, deep meta reinforcement learning-based offloading, iterative proximal algorithm, and parallel random forest.

As Vehicular Ad-hoc Networks (VANETs) bring fantastic revolution to intelligent transportation systems, their own security has become an important research topic. However, authentication security, as the key issue of VANETs’ security, is still facing great challenges. Therefore, this survey first starts with the background of VANETs and then introduces the main security concerns. To distinguish from existing surveys, this paper proposes the security challenges and security properties of VANETs from the perspective of builders and attackers, respectively. Then, we present the necessary and important characteristics of a VANET’s security system including the authenticity of nodes and information, the availability of network systems, the integrity and confidentiality of information, and the non-repudiation of information after transmission. Specifically, attack methods and detection schemes for these characteristics are highlighted in detail and analyzed in terms of their advantages and limitations, which fill the gaps in the existing survey. More importantly, we focus on the authentication schemes proposed in recent years, reporting the latest advances in VANETs. These schemes are analyzed and compared in depth in terms of the security characteristics and attack resistance of authentication, as well as in terms of overhead and efficiency. Finally, this paper summarizes some lessons and discusses several future research directions.

As a bridge for information interaction between people and things, and things and things, IoT devices bring security issues and data privacy protection issues that have always been the main challenges in the IoT environment. In terms of abnormal traffic detection of IoT devices, data sharing between device data is usually not possible. This makes the deep learning method for model training based on a large amount of data unable to fully exert its strength due to the lack of IoT device attack instances, resulting in the problem of low detection accuracy. To this end, we propose an abnormal traffic detection model for IoT devices, FL-DSCNN (Federated Learning and Depthwise separable convolutional neural networks). First, the mayfly optimization algorithm is used to select the traffic features, and the model training time is reduced by reducing the feature dimension. Then, by introducing the FL framework, the depthwise separable convolutional neural network is used as a local model for collaborative training without sharing private data, avoiding the problem of lack of labeled data due to the “data silos” phenomenon while protecting data privacy. In addition, we experimentally verify the proposed method on the existing public dataset Aposemat IoT-23 dataset and compare and evaluate it with existing methods. The experimental results show that the method can achieve two-class and multi-class detection respectively. The detection accuracy rates of 98.52% and 97.73% prove the progress and superiority of the proposed FL-DSCNN model in the detection of abnormal traffic of IoT devices.

With the continuous development of information technology, the network as the infrastructure of the information age has become an indispensable and vital aspect of our daily lives. With the popularization of 5G technology, the number of handheld devices has increased significantly. Although it has brought great convenience to our production and life, it has also introduced new security risks, making the network more likely to be infiltrated and attacked. Currently, abnormal network traffic detection technology has become a vital part of network security, effectively protecting the network and computer systems from intrusion and maintaining normal operation. In the network abnormal traffic detection experiment based on simulation, most researchers use public and well-known datasets, and different datasets contain different attack samples. When testing on different datasets, the model needs to be retrained, significantly increasing the consumption of computer resources. The paper proposes a wireless network abnormal traffic detection method based on the deep transfer adversarial environment dueling double deep Q-Network (DTAE-Dueling DDQN). First, use the old NSL-KDD dataset to train AE-Dueling DDQN and save the training model weights. Then, use the idea of fine-tuning, transfer the weight of the AE-Dueling DDQN training is completed to the target model, and fine-tune the target model using the newer AWID dataset in the WiFi environment. The experiment compares the current representative deep learning (DL) and deep reinforcement learning (DRL) methods. Experimental results show that our proposed method saves computer resources significantly and achieves good results in all evaluation indicators.

BACKGROUND: Technological and research advances have produced large volumes of biomedical data. When represented as a network (graph), these data become useful for modeling entities and interactions in biological and similar complex systems. In the field of network biology and network medicine, there is a particular interest in predicting results from drug-drug, drug-disease, and protein-protein interactions to advance the speed of drug discovery. Existing data and modern computational methods allow to identify potentially beneficial and harmful interactions, and therefore, narrow drug trials ahead of actual clinical trials. Such automated data-driven investigation relies on machine learning techniques. However, traditional machine learning approaches require extensive preprocessing of the data that makes them impractical for large datasets. This study presents wide range of machine learning methods for predicting outcomes from biomedical interactions and evaluates the performance of the traditional methods with more recent network-based approaches. RESULTS: We applied a wide range of 32 different network-based machine learning models to five commonly available biomedical datasets, and evaluated their performance based on three important evaluations metrics namely AUROC, AUPR, and F1-score. We achieved this by converting link prediction problem as binary classification problem. In order to achieve this we have considered the existing links as positive example and randomly sampled negative examples from non-existant set. After experimental evaluation we found that Prone, ACT and [Formula: see text] are the top 3 best performers on all five datasets. CONCLUSIONS: This work presents a comparative evaluation of network-based machine learning algorithms for predicting network links, with applications in the prediction of drug-target and drug-drug interactions, and applied well known network-based machine learning methods. Our work is helpful in guiding researchers in the appropriate selection of machine learning methods for pharmaceutical tasks.

The rapid development of Internet technology has brought great convenience to our production life, and the ensuing security problems have become increasingly prominent. These problems threaten users’ privacy and pose significant security risks to the normal conduct of many aspects of society, such as politics, economy, culture, and people’s livelihood. The growth of the information transmission rate expands the scope of attacks and provides a more attack environment for intruders. Abnormal detection is an effective security protection technology that can monitor network transmission in real-time, effectively sense external attacks, and provide response decisions for relevant managers. The development of machine learning has also led to the development of abnormal traffic detection technology. The goal has been to use powerful and fast learning algorithms to deal with changing threats and respond in real-time. Most of the current abnormal detection research is based on simulation, using public and well-known datasets. On the one hand, the dataset contains high-dimensional massive data, which traditional machine learning methods cannot be processed. On the other hand, the labeled data scale is far behind the application requirements, and the dataset’s labels are all manually labeled, so the labeling cost is exceptionally high. This paper proposes a semi-supervised Double Deep Q-Network (SSDDQN)-based optimization method for network abnormal traffic detection, mainly based on Double Deep Q-Network (DDQN), a representative of Deep Reinforcement Learning algorithm. In SSDDQN, the current network first adopts the autoencoder to reconstruct the traffic features and then uses a deep neural network as a classifier. The target network first uses the unsupervised learning algorithm K-Means clustering and then uses deep neural network prediction. The experiment uses NSL-KDD and AWID datasets for training and testing and performs a comprehensive comparison with existing machine learning models. The experimental results show that SSDDQN has certain advantages in time complexity and achieved good results in various evaluation metrics.