Table of Content

03 June 2025, Volume 11 Issue 5

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A Blackbox Antiforensics Method of GANgenerated Faces Based on #br# Invertible Neural Network#br#

2025, 11(5):  394. 

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Generative adversarial network GANgenerated faces forensics models are used to distinguish real faces and GANgenerated faces. But due to the fact that forensics models are susceptible to adversarial attacks, the antiforensics techniques for GANgenerated faces have emerged. However, existing antiforensic methods rely on whitebox surrogate models, which have limited transferability. Therefore, a blackbox method based on invertible neural network (INN) is proposed for GANgenerated faces antiforensics in this paper. This method embeds the features of real faces into GANgenerated faces through the INN, which enables the generated antiforensics faces to disturb forensics models. Meanwhile, the proposed method introduces a feature loss during training to maximize the cosine similarity between the features of the antiforensics faces and the real faces, further improving the attack performance of antiforensics faces. Experimental results demonstrate that, under the scenarios where no whitebox models are involved, the proposed method has good attack performance against eight GANgenerated faces forensics models with better performance than seven comparative methods, and can generate highquality antiforensics faces.

Research and Implementation of a Blockchainbased Biometric #br# Information Sharing Scheme#br#

2025, 11(5):  402. 

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Traditional informationsharing solutions typically rely on data center servers for data storage and verification. However, this centralized model is vulnerable to issues such as data tampering, privacy breaches, and operational irregularities when under attack, making it difficult to meet the requirements for data trustworthiness. To address these challenges, this paper proposes a solution that combines blockchain technology with biometric information authentication. By using biometric features such as fingerprints and facial recognition to generate a unique authentication key, which is securely stored on the blockchain, the solution leverages the decentralized, tamperproof, and traceable characteristics of blockchain to ensure secure data storage and trusted sharing, thereby effectively safeguarding privacy and security during the information verification process. Taking the education sector as an example, this solution can effectively address issues like exam cheating and resource infringement, providing a new approach to data security and sharing that also ensures privacy protection.

Research on Malware Detection Based on Word Embedding and Feature Fusion#br#

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2025, 11(5):  412. 

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To address the limitations of traditional methods in feature extraction and representation, which are unable to simultaneously capture the spatial and temporal features of API sequences and fail to capture key features that determine the target task, a malware detection method based on word embedding and feature fusion has been proposed. First, the word embedding technology from the field of natural language processing is utilized to encode API sequences, obtaining their semantic feature representations. Then, multiple convolutional networks and BiLSTM networks are employed to extract ngram local spatial features and temporal features of the API sequences, respectively. Finally, a selfattention mechanism is used to deeply fuse the captured features of critical positions, thereby achieving the classification task by characterizing deep malicious behavior features. Experimental results show that in binary classification tasks, the accuracy of this method reaches 94.79%, which is an improvement of 12.37% on average compared to traditional machine learning algorithms, and 5.78% higher on average compared to deep learning algorithms. In multiclass classification tasks, the accuracy of this model also reaches 91.95%, effectively enhancing the detection accuracy of malware.

Hardware Trojan Detection Method Integrating Multiple Sidechannel #br# Analysis and Pearson Correlation Coefficient#br#

2025, 11(5):  420. 

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For the chip power consumption data acquisition when the influence of the noise problem, this paper proposes a multiple sidechannel analysis method based on correlation analysis, using the intrinsic relationship between dynamic current and electromagnetic radiation to identify the existence of hardware trojans. A dual channel detection platform is built to simultaneously collect and store the dynamic power consumption and electromagnetic radiation of the chip. Pearson correlation coefficient curves of power consumption and electromagnetism are obtained to distinguish hardware Trojan horse chip from hardware Trojan horse chip. The experimental results show that the hardware Trojan detection method based on multipleparameter sidechannel analysis can screen out the chip containing hardware Trojan whose area is only 0.28% of the chip to be tested, and can distinguish the two hardware Trojan horses whose area difference is only 0.08% of the chip to be tested.

Industrial Internet Data Sharing Scheme with Attributebased #br# Proxy Reencryption in Cloudchain Collaboration#br#

2025, 11(5):  427. 

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The industrial Internet is an application ecology of new generation information technology and industrial system in an allround and deep integration. Through data sharing, it can realize the overall management and allocation of various resources in the industrial field. Aiming at the problem of privacy leakage in the process of industrial Internet data sharing, this paper proposes a cloudchain collaborative attributebased proxy reencryption industrial Internet data sharing scheme, which stores massive industrial data ciphertext in the cloud, and solves the problem of computing and metadata ciphertext storage in the process of data security sharing on the blockchain. Construct a key generation algorithm that can avoid private key escrow by combining certificateless public key cryptography with ciphertext policy attributebased encryption. Design a trust evaluation consensus algorithm to select nodes with high credibility as proxies in blockchain networks, solving the semitrust problem of traditional proxy reencryption. The security analysis shows that the proposed scheme satisfies keyword security and resistance to collusion attacks. The performance and simulation results show that the scheme in this paper has better functions and higher efficiency, and applicability for industrial Internet data sharing.

Active Tor Website Fingerprint Recognition

2025, 11(5):  439. 

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The anonymous communication system Tor is often exploited by criminals, disrupting the network environment and social stability. Website fingerprinting can effectively monitor Tor activities. However, user behavior and website content on Tor change over time, leading to the problem of concept drift, which degrades model performance. Additionally, existing models suffer from large parameter sizes and low efficiency. To address these issues, a Tor website fingerprinting model based on active learning, named TorAL, is proposed. This method utilizes the image classification model ShuffleNetV2 for feature extraction and classification, and improves its downsampling module with Haar wavelet transform to losslessly reduce image resolution. The model’s recognition accuracy surpasses that of existing models. Moreover, by combining active learning, the model is trained with a small amount of highly contributive data, effectively addressing the concept drift problem.

Research on Tor Traffic Classification Based on Improved Bidirectional  Memory Residual Network

2025, 11(5):  447. 

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In order to solve the problem of difficulty in correctly classifying Tor traffic and regulating it due to the encryption characteristics of Tor links, a Tor traffic classification method based on an improved bidirectional memory residual neural network (CBAMBiMRNet) is proposed. Firstly, the SMOTETomek (SMOTE and Tomek links) comprehensive sampling algorithm is adopted to balance the dataset, so that the model could learn from the traffic data of all categories. Secondly, CBAM is used to assign greater weights to important features, combining 1D convolution with bidirectional long shortterm memory modules to extract temporal and local spatial features of Tor traffic data. Finally, by adding identity maps, the phenomenon of gradient vanishing and exploding caused by the increase in model layers was avoided, and the problem of network degradation was solved. The experimental results show that on the ISCXTor2016 dataset, the accuracy of our model for Tor traffic recognition reached 99.22%, and the accuracy for Tor traffic application service type classification reached 93.10%, proving that the model can effectively recognize and classify Tor traffic.

Hybrid Neural Network Encrypted Malicious Traffic Detection  in the Industrial Internet with Domain Adaptation

2025, 11(5):  457. 

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With the rapid development of information technology in the field of industrial control, the industrial Internet has become a major target for cyberattacks, making malicious traffic detection increasingly important. However, the widespread use of encryption allows attackers to easily hide malicious communication content, rendering traditional contentbased detection methods ineffective. This paper proposes an encrypted malicious traffic detection scheme based on a hybrid neural network and domain adaptation. The scheme integrates ResNet, ResNext, DenseNet, and similarity detection algorithms to construct a hybrid neural network. On this basis, a domain adaptation module is added to reduce data bias. By preprocessing streams from a public industrial Internet dataset, deep features are extracted from encrypted traffic without decryption. The hybrid neural network outputs higherdimensional feature vectors that leverage the strengths of each model. A domain classifier within the domain adaptation module enhances the model’s stability and generalization across different network environments and time periods, enabling accurate classification of malicious traffic. Experimental results show that the proposed scheme improves accuracy and efficiency in detecting encrypted malicious traffic.

Design and Verification of V2V Authentication and Key Exchange Protocol  for Internet of Vehicles

2025, 11(5):  465. 

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In the Internet of vehicles system, vehicles need to achieve communications of vehicle to vehicle(V2V), which needs strong security, low latency, user anonymity and other security characteristics. Authentication and key exchange protocol(AKE) is based on cryptographic algorithms, aiming to complete session key negotiation for subsequent information exchange between communication parties. It is an important means to ensure the security of vehicle networking. However, the existing protocol registration phase requires offline secure channels, which is inconsistent with reality. Also the authentication phase is mostly based on third parties and requires multiple rounds of information exchange, increasing the complexity of the protocol interactions. In this paper, a lightweight V2V protocol is designed for public channels, which does not rely on the third party and only requires two rounds of information exchange during login and authentication phases. At the same time, a fast login phase is added to solve the delay of information exchange caused by sudden network interruptions. Theoretical analysis and formal verification results show that the designed protocol satisfied security properties such as authentication and confidentiality.

Research on Auxiliary Classification Model Based on Extracting  Keypoints of Graph Structure

2025, 11(5):  473. 

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Auxiliary secret classification is a special text classification task that divides undecided encrypted text into different levels of confidentiality.. In order to solve the problems of the traditional method, such as weak feature representation and extraction ability and low interpretability of the classification process, keypoints feature representation method based on graph structure was proposed. On that basis, an auxiliary secret classification model based on keypoints extraction was further proposed, so as to enhance the ability of secret point features in describing the confidential matters, thus the performance of the auxiliary classification model is enhanced. Specifically, this paper deeply analyze the characteristics of classification rules, constructs the keypoints template with reference to text representation method of the graphic structure, extracts the keypoints and calculates the confidence level of the keypoints of the text to be classified, and obtains the secret level prediction results and the classification basis items through the filtered effective keypoints. The experimental result on the ACD indicates that the accuracy and recall rate of this model are 10% and 7% higher than those of BERT and TextCNN, which verifies the effectiveness of keypoints feature representation method based on the graph structure.

Task Independent Privacy Protection in Personalized Federated Learning  for Battery Monitoring

2025, 11(5):  481. 

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For the health management of batteries in new energy vehicles, it is essential to collaboratively share distributed battery data and establish a federated learning model to extract valuable information. To counteract the privacy leakage risks associated with battery data sharing, this paper designs a taskindependent privacy protection and communicationefficient federated learningempowered edge intelligence model. This model learns personalized subnetworks that generalize well to local data and uses network pruning to find the optimal subnetwork, ensuring inference accuracy. Meanwhile, to resist feature reconstruction attacks and privacy leakage risks, it constructs taskindependent privacyprotective anonymous intermediate representations. By employing adversarial training, it maximizes the reconstruction error of the adversarial reconstructor and the classification error of the adversarial classifier, while minimizing the classification error of the target classifier. Experimental simulations show that this method improves inference accuracy by 8.85%  and reduces communication overhead by 1.95 times. The balance analysis of utility and privacy demonstrates that it ensures the accuracy of target feature extraction while protecting privacy.