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International Conference for Internet Technology and Secured Transactions (ICITST)

作者： Maher Salem Ulrich Buehler Group of Network and Data Security University of Applied Sciences Fulda Germany

Processing massive data flow in intrusion detection systems (IDS) become a serious challenge. It is considered as a major deficiency while handling heterogeneous and nonstationary data stream to uncover anomaly in the online operational mode. This paper proposes a novel online method that constructs connections from the massive data flow for evaluating IDS models. The proposed method overcomes this challenge by using a queuing concept of dynamic window size. It captures network traffic and hosts events constantly and handles them synchronously within time slot windows inside the queue in order to construct connection vectors based on certain features. We have evaluated the method in offline mode using DARPA dump data flow and in online mode using a simulated network at the university campus. In addition, we have evaluated our IDS model using the constructed connections to proof the feasibility and plausibility of the proposed method in IDS area. The performance evaluation confirms that, the proposed method is able to operate in offline as well online modes efficiently. Moreover, constructed connections are very adequate for training and evaluating IDS models.

关键词： Measurement Cryptography Receivers Correlation Silicon

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security control of unstructured data in the power IoT for isolation network between internal and external 8

Security control of unstructured data in the power IoT for i...

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2022 8th International Symposium on Sensors, Mechatronics and Automation System, ISSMAS 2022

作者： Yang, Ruxia Wang, Di Liang, Fei State Grid Key Laboratory of Information and Network Security Global Energy Interconnection Research Institute Co. Ltd. Nanjing China Big Data Center State Grid of China Beijing China

As a key national infrastructure, the safety of the power grid is an overall and strategic issue that affects the development of the national economy and society. With the popularization and development of the Internet of Things in the power industry, power Internet of Things terminal equipment has become a key target of network attacks. Using network attacks to damage power Internet of Things equipment has a strong detrimental power. As the main data storage method of the power Internet of Things, the security management and control of unstructured data has become the focus of the power Internet of Things security. This paper proposes a reformulation of access control policies based on the different attribute states and trust levels of power Internet of Things terminal equipment to reduce its access control authority, and on this basis, proposes a dynamic access control based on the isolation of internal and external networks. The detailed function design of each core security protection component is given, and the component module is explained. © Published under licence by IOP Publishing Ltd.

关键词： Internet of things

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Towards Resilience 5G-V2N: Efficient and Privacy-Preserving Authentication Protocol for Multi-Service Access and Handover

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5446-5463页

作者： Bi, Ye Jia, Chunfu Nankai University College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology DISSEC Tianjin300350 China

The booming 5 G cellular networks sparked tremendous interest in supporting more sophisticated critical use cases through vehicle-to-network (V2N) communications. However, the inherent technical vulnerabilities and densification of 5 G raise new security and efficiency challenges. The existing secondary authentication fails to support multi-service access. The random access process lacks authentication of the gNB, possibly leading to fake base station attacks (FBS). Moreover, related research extends key forward/backward secrecy (KF/BS) to require that it also applies to gNBs, thus invalidating most existing schemes. This paper introduces a comprehensive security framework for 5G-V2N that seamlessly integrates with existing standardized architecture to provide privacy-preserving mutual authentication and key agreement for the full service cycle. Specifically, we propose new secondary authentication involving gNBs and support single request access to multi-services. Second, incorporating the service migration idea, we design the g2g (gNB-to-gNB) channel establishment phase to promote secure context share. Finally, the proposed efficient handover phase achieves the security properties of enhanced KF/BS, known randomness secrecy and privacy-preserving, and avoids FBS. We verify the proposed protocol using three different formal techniques: provably secure, BAN-logic, and AVISPA tool. Extensive experimental results and comparison show that our scheme excels in computational and communication efficiencies, and detecting malicious events. © 2025 IEEE.

关键词： Authentication Protocol

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An Enhanced Clustering-Based (k, t)-Anonymity Algorithm for Graphs

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Chinese Journal of Electronics 2025年第1期34卷 365-372页

作者： Yuanyuan Wang Xing Zhang Zhiguang Chu Wei Shi Xiang Li School of Electronics and Information Engineering Liaoning University of Technology Key Laboratory of Security for Network and Data in Industrial Internet of Liaoning Province Faculty of Information Technology Beijing University of Technology

As people become increasingly reliant on the Internet, securely storing and publishing private data has become an important issue. In real life, the release of graph data can lead to privacy breaches, which is a highly challenging problem. Although current research has addressed the issue of identity disclosure, there are still two challenges: First, the privacy protection for large-scale datasets is not yet comprehensive; Second, it is difficult to simultaneously protect the privacy of nodes, edges, and attributes in social networks. To address these issues, this paper proposes a(k,t)-graph anonymity algorithm based on enhanced clustering. The algorithm uses k-means++ clustering for k-anonymity and t-closeness to improve k-anonymity. We evaluate the privacy and efficiency of this method on two datasets and achieved good results. This research is of great significance for addressing the problem of privacy breaches that may arise from the publication of graph data.

关键词： data privacy Uncertainty Social networking (online) Publishing Heuristic algorithms Scalability Clustering algorithms Privacy breach Internet Protection

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security analysis and secured access design for networks of image remote sensing

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Digital Communications and networks 2025年第1期11卷 136-144页

作者： Juan Zhao Haibo Dai Xiaolong Xu Hao Yan Zheng Zhang Chunguo Li School of Network Security Jinling Institute of TechnologyNanjing 211169China School of IoT Nanjing University of Posts and Telecommunications(NJUPT)Nanjing 210003China Jiangsu Key Lab.of Big Data Security and Intelligent Processing NJUPTNanJing 210003China School of Information Science and Engineering Southeast UniversityNanjing 210096China

The secured access is studied in this paper for the network of the image remote *** sensor in this network encounters the information security when uploading information of the images wirelessly from the sensor to the central collection *** order to enhance the sensing quality for the remote uploading,the passive reflection surface technique is *** one eavesdropper that exists nearby this sensor is keeping on accessing the same networks,he may receive the same image from this *** goal in this paper is to improve the SNR of legitimate collection unit while cut down the SNR of the eavesdropper as much as possible by adaptively adjust the uploading power from this sensor to enhance the security of the remote sensing *** order to achieve this goal,the secured energy efficiency performance is theoretically analyzed with respect to the number of the passive reflection elements by calculating the instantaneous performance over the channel fading *** on this theoretical result,the secured access is formulated as a mathematical optimization problem by adjusting the sensor uploading power as the unknown variables with the objective of the energy efficiency maximization while satisfying any required maximum data rate of the eavesdropper ***,the analytical expression is theoretically derived for the optimum uploading *** simulations verify the design approach.

关键词： Image remote sensing Secured access Energy efficiency Sensor transmit power Secured access design

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ADLT: a novel method for effective detection of adversarial attacks in image retrieval

ADLT: a novel method for effective detection of adversarial ...

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2024 International Conference on Computer Vision and Image Processing, CVIP 2024

作者： Xu, Shiyue Zhang, Yu College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin300350 China

ISBN: (纸本)9781510688384

The versatile application of deep learning has made it prone to vulnerabilities, particularly adversarial attacks, which have been increasingly prevalent as its scope expands. Attack methods based on adversarial queries pose significant challenges to image retrieval models, disrupting their functionality. While numerous defense methods have been proposed for adversarial query attacks in image classification, approaches to detecting or defending against such attacks in image retrieval remain limited due to inherent challenges in adapting these methods. Based on these observations, we introduce the Augmentation Detection based on Top-K-List (ADTL) method, the first effective approach for detecting adversarial attacks in image retrieval. ADTL applies image augmentation to a query and determines its adversarial nature by calculating the divergence in the Top-K list before and after augmentation. This paper presents a method that employs suitable image augmentation techniques and K-Reciprocal clustering for pre-classifying images in the gallery to ease divergence calculations, which has been tested on the Oxford5k and Paris6k datasets and has shown remarkable effectiveness with a high detection accuracy of 87.3%. © 2025 SPIE.

关键词： Adversarial perturbation Attack detection Image augmentation Image retrieval

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A Novel Approach to Cloud security: Publicly Verifiable Remote Signcryption Framework

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IEEE Internet of Things Journal 2025年第10期12卷 14828-14844页

作者： Ali, Mohammad Liu, Ximeng Amirkabir University of Technology Department of Mathematics and Computer Science Tehran Iran Fuzhou University Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou350108 China

In communication systems, ensuring both data integrity and confidentiality is crucial. Traditional methods, such as encryption and signature schemes, handle these aspects separately, leading to inefficiencies. Signcryption techniques, which address both requirements simultaneously, present a more efficient alternative. Nonetheless, they are unsuitable for cloud storage systems due to their reliance on original files for data auditing. Remote data integrity checking (RDIC) methods resolve this issue but compromise data confidentiality. To bridge the gap between signcryption and RDIC approaches, remote signcryption (RSC) has been proposed. However, existing RSC methods lack support for public auditing, rendering data auditing non-outsourcable. This paper introduces publicly verifiable remote signcryption (PVRSC) to address this shortcoming and presents the first concrete PVRSC scheme with public verifiability. We define the security model for PVRSC and prove its robustness based on the hardness of the Bilinear Diffie-Hellman (BDH) problem. Our implementation on real data shows that PVRSC significantly outperforms existing methods, achieving speeds up to 100 times faster and reducing communication costs by half compared to RDIC methods. For outsourced data up to 5MB, PVRSC maintains a communication cost below 8KB during data recovery, a substantial improvement over classical signcryption approaches, which exceed 5MB. © 2014 IEEE.

关键词： Cloud security

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A Weighted Discrete Wavelet Transform-Based Capsule network for Malware Classification 27th

A Weighted Discrete Wavelet Transform-Based Capsule Network ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Qiao, Tonghua Cao, Chunjie Zou, Binghui Tao, Fangjian Cheng, Yinan Zhang, Qi Sun, Jingzhang Lab of Heterogeneous Network Security and Governance Haikou China School of Cyberspace Security Hainan University Haikou China Faculty of Data Science City University of Macau China

ISBN: (纸本)9783031781278

The rise of sophisticated malware poses a grave threat to computer security, challenging traditional detection methods. Traditional malware detection techniques, which primarily rely on feature engineering and defining rules to identify malware, are gradually failing to comprehensively and accurately detect emerging malware variants. To address this problem, we proposed an efficient and accurate novel malware classification model based on capsule network with Weighted Discrete Wavelet Transform (WDWTCN). In the field of malware classification, datasets are predominantly composed of grayscale images, which present samples in a single-channel format. To more adeptly capture the key features within malware samples, WDWTCN incorporates the Weighted Discrete Wavelet Transform (WDWT) as a preprocessing step for input data. By converting the single-channel input of grayscale images into a four-channel input, our model becomes significantly more efficient in extracting feature information from malware samples. We used ResNet50 as the feature extraction layer of the capsule network. By integrating global and local features, the model can comprehensively understand and classify malware, thereby enhancing the detection performance of malware. Finally, to validate the performance of the WDWTCN, we conducted an extensive experimental evaluation on three large datasets, i.e., BIG2015, MalImg and BODMAS. The experimental results showed that WDWTCN achieves significant improvements in malware detection accuracy, reaching 98.93%, 99.21% and 96.14% accuracy, respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Discrete wavelet transforms

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Blockchain and signcryption enabled asynchronous federated learning framework in fog computing

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Digital Communications and networks 2025年第2期11卷 442-454页

作者： Zhou, Zhou Tian, Youliang Xiong, Jinbo Peng, Changgen Li, Jing Yang, Nan State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China

Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for cross-domain scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL. © 2024 Chongqing University of Posts and Telecommunications

关键词： Federated learning

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Crop Classification Methods Based on Siamese CBMM-CNN Architectures Using Hyperspectral Remote Sensing data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. The CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. The Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. These results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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