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Privacy-Preserving Covert Channels in VoLTE via Inter-Frame Delay Modulation 1

2nd International Conference on Artificial Intelligence Security and Privacy, AIS and P 2024

作者： Wu, Xiaokai sun, Xuan Huang, Jiaxin Shi, Ning Liang, Chen Computer School Beijing Information Science and Technology University Beijing100192 China China Southern Airlines Co. Ltd. Guangdong Guangzhou510000 China Hebei Key Laboratory of IoT Blockchain Integration Shijiazhuang China

ISBN: (数字)9789819611485

ISBN: (纸本)9789819611478

The rapid advancement of artificial intelligence and mobile communication technologies has brought unprecedented convenience, but also significant privacy and security concerns, particularly in real-time video communications, such as deepfake. Several researchers have investigated multiple security measures, with Covert Timing Channels (CTCs) emerging as a promising technique for secure information transmission, yet most of them suffer from low transmission efficiency, limiting their real-time responsiveness and network adaptability, making them inadequate for high-capacity covert information transmission. To address this limitation, this paper proposes a novel CTC scheme that modulates the Inter-Frame Delay (IFD) between audio and video frames during VoLTE(Voice over LTE) video calls to transmit covert information. Furthermore, to maintain the undetectability of the channel, a fitting algorithm is used to ensure that the cumulative distribution function (CDF) of the IFD of covert traffic aligns with that of legitimate traffic. The design also incorporates an embedding factor that allows communicating parties to dynamically adjust network parameters, balancing robustness, transmission efficiency, and undetectability. Experimental results demonstrate that, compared to existing methods, the proposed IFDCTC scheme achieves higher transmission efficiency, with a peak transmission rate of up to 30 bps, while maintaining acceptable bit error rates and strong undetectability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distribution functions

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Comprehensive Perturbation Consistency for Semi-Supervised Change Detection in Remote Sensing Images

Comprehensive Perturbation Consistency for Semi-Supervised C...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zan Mao Xin Li Ze Luo Yingjuan Tang Dongmei Jiang Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences PengCheng Laboratory National Key Laboratory of Advanced Vehicle Integration and Control Beijing Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, many change detection (CD) methods rely on supervised learning, which necessitates extensive manually annotated data, resulting in significant labor and time requirements. Recently, semi-supervised (SS) approaches have emerged in the CD community, which exploit large amounts of unlabeled data by utilizing consistency regularization. However, these methods do not consider the broader perturbation consistency to confer better generalization of the model. In this paper, we propose a novel SS CD framework with a comprehensive perturbation consistency called CPC, which extends perturbation consistency to the entire learning period. Specifically, our CPC combines the input, feature, and network perturbations for comprehensive perturb space. And, we design two distinct structures in practice, decoupled CPC and coupled CPC. Furthermore, we propose a change-aware input perturbation that introduces expensive annotation information to further expand the input perturb space. Extensive experiments conducted on the WHUCD, and GZ-CD datasets demonstrate that the proposal performs favorably against the state-of-the-art methods.

关键词： Codes Annotations Perturbation methods Supervised learning Signal processing Acoustics Proposals Labeling Speech processing Remote sensing

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Brillouin Frequency Shift Extraction for BOTDA Based on Gramian Angular Fields 4

Brillouin Frequency Shift Extraction for BOTDA Based on Gram...

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4th International Computational Imaging Conference, CITA 2024

作者： Zhu, Borong Zhang, Zhihao Qian, Yuhao Ma, Xiaole Wang, Liang Yu, Kuanglu Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Institute of Information Science School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China National Engineering Laboratory for Next Generation Internet Access System School of Optics and Electronic Information Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781510688834

To accurately extract Brillouin frequency shift of BOTDA with large sweeping step sizes, a novel structure of GAFCNN is proposed, combining time series coding with convolutional neural networks. The experimental data validation results demonstrate that the GAFCNN is capable of obtaining more accurate results under large sweep steps and low signal-to-noise ratios (SNRs) than other current methods. © 2025 SPIE.

关键词： Convolutional neural networks

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A circuit area optimization of MK-3 S-box

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Cybersecurity 2025年第1期8卷 61-71页

作者： Yanjun Li Weiguo Zhang Yiping Lin Jian Zou Jian Liu Information Industry Information Security Evaluation Center The 15th Research Institute of China Electronic Technology Group CorporationBeijing 100083China Henan Key Laboratory of Network Cryptography Technology Zhengzhou 450001China Beijing Electronic Science and Technology Institute No.7 Fufeng RoadFengtai DistricBeijing 100083China College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

In MILCOM 2015,Kelly et *** the authentication encryption algorithm MK-3,which applied the 16-bit *** paper aims to implement the 16-bit S-box with less circuit ***,we classifed the irreducible polynomials over F_(2n)into three *** we compared the logic gates required for multiplication over the fnite feld constructed by the three types of irreducible *** to the comparison result,we constructed the composite felds, F_((2^(4))^(2))and F_((2^(8))^(2)).Based on the isomorphism of fnite felds,the operations over F_(2)^(16)can be conducted over F_((2^(8))^(2)).Similarly,elements over F28 can be mapped to the corresponding elements over F_((2^(4)))^(2).Next,the SAT solver was used to optimize the operations over smaller feld F_(2)^(4).At last,the architecture of the optimized MK-3 S-box was worked *** with the implementation proposed by the original designer,the circuit area of the MK-3 S-box in this paper is reduced by at least 55.9%.

关键词： irreducible operations optimization

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A Reversible Data Hiding in Encryption Domain for JPEG Image Based on Controllable Ciphertext Range of Paillier Homomorphic Encryption Algorithm 21st

A Reversible Data Hiding in Encryption Domain for JPEG Ima...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Ma, Bin Zhao, Chunxin Ma, Ruihe Xian, Yongjin Wang, Chunpeng Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China School of Economics Jilin University Changchun130015 China

ISBN: (纸本)9789819601219

With the rapid advancement of cloud computing, significant security concerns regarding data storage have been continuously raised. When the encrypted images are stored in the cloud, reversible data hiding in encrypted domain is used to embed additional data to manage the storage resources in the cloud. Most of the current works are applied to uncompressed images rather than JPEG images. Therefore, this paper proposes a reversible data hiding algorithm for JPEG images based on an enhanced Paillier homomorphic encryption algorithm. In the proposed scheme, the AC coefficients are altered by using a ciphertext distribution constrained paillier homomorphic encryption algorithm, so as to reduce the size of the data-embedded encrypted image. Whereas, the DC coefficients are encrypted by using the one-dimensional tangent chaos encryption algorithm, thereby ensuring high security of the encrypted image. Moreover, according to the characteristics of the homomorphic encryption algorithm, the confidential data can be recovered from both the encryption domain and from the plain domain. Experimental results demonstrate that the ciphertext distribution-constrained Paillier homomorphic encryption algorithm obtains higher data embedding capacity and superior security in comparison with other advanced algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Density Peak Clustering Algorithm Based on Data Field Theory and Grid Similarity

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Journal of computer Science and Technology 2025年第2期40卷 301-321页

作者： Yu, Qing-Ying Shi, Ge-Ge Xu, Dong-Sheng Wang, Wen-Kai Chen, Chuan-Ming Luo, Yong-Long School of Computer and Information Anhui Normal University Wuhu241002 China Anhui Provincial Key Laboratory of Network and Information Security Wuhu241002 China

The density peak clustering algorithm can rapidly identify cluster centers by drawing decision graphs without any prior knowledge;however, when multiple density peaks are present in one cluster of the dataset, the cluster centers cannot be accurately obtained, leading to incorrect clustering. Moreover, the single-step allocation strategy is poorly fault-tolerant and can lead to successive allocation errors. To address these problems, this study proposes a density peak clustering method based on data field theory and grid similarity, which divides the original data into grid spaces to obtain subspace codes and grid data. Subsequently, the potential grid density is introduced to measure the density of each grid based on the data field, and the density peaks are identified to obtain the cluster centers from the high-density grids. Finally, self-organized clustering is realized based on neighborhood extension centers and grid similarity. It thus reduces the possibility of successive assignment errors and the negative impact of multiple density peaks on the clustering results. In addition, the algorithm automatically determines the cluster centers and can correctly assign the non-density-peak grid to the corresponding clusters. Experimental results with synthetic and real-world datasets demonstrate that the proposed algorithm outperforms similar algorithms in terms of accuracy and efficiency when dealing with complex datasets with large density differences and cross-tangling. © Institute of Computing Technology, Chinese Academy of Sciences 2025.

关键词： Clustering algorithms

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Do Not Trust What They Tell: Exposing Malicious Accomplices in Tor via Anomalous Circuit Detection 34

Do Not Trust What They Tell: Exposing Malicious Accomplices ...

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34th ACM Web Conference, WWW 2025

作者： Yao, Yixuan Yang, Ming Liu, Zixia Dong, Kai Gu, Xiaodan Wang, Chunmian Southeast University Jiangsu Nanjing China Anhui University of Technology Anhui Maanshan China Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

ISBN: (纸本)9798400712746

The Tor network, while offering anonymity through traffic routing across volunteer-operated nodes, remains vulnerable to attacks that aim to deanonymize users by correlating traffic patterns between colluded entry and exit nodes in circuits. This paper presents a novel approach for detecting anomalous circuits in the Tor network, and for the first time provides a more comprehensive identification of potential malicious accomplice nodes in Tor by taking roles of nodes in anomalous circuits into consideration. Our method strategically utilizes modified middle nodes to capture traffic data, followed by a novel circuit classification based on traffic patterns to pinpoint concerned circuits. Two kinds of anomalies are identified: routing anomalies and usage anomalies, that respectively represent the anomalies with explicit or implicit violation of Tor’s circuit construction guidelines. This leads to a successful revealing of totally 1,960 anomalous nodes in Tor. Furthermore, we apply clustering analysis with considering corresponding anomalous circuits and other key characteristics to the detected anomalous nodes, revealing potential hidden organizations behind these nodes that can threaten the network’s security. Our findings highlight the necessity for the Tor project to adopt targeted mitigation strategies to enhance overall network security and privacy. © 2025 Copyright held by the owner/author(s).

关键词： Anomalous circuit Anonymity Tor network Traffic analysis

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua Science and Technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite System(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite System(BDS) long short-term memory(LSTM) multipath classification

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8th International Congress on Edge Computing, EDGE 2024, Held as Part of the Services Conference Federation, SCF 2024

作者： Tian, Yun Wu, Bin Shi, Jiaoli Zhang, Caicai Xu, Du School of Computer and Big Data Science Jiujiang University Jiujiang332005 China Jiujiang Key Laboratory of Network and Information Security Jiujiang332005 China School of Modern Information Technology Zhejiang Polytechnic University of Mechanical and Electrical Engineering Hangzhou310053 China

ISBN: (纸本)9783031770685

The development of cloud computing and the widespread application of cloud services have made outsourcing services more convenient. The need for individuals and businesses to store and manipulate the graph data they generate is growing rapidly. The unreliability and insecurity of cloud servers make outsourcing graph data a great risk of information leakage. To effectively protect data security, encrypting outsourced data is a useful method. The adjacent vertex query is a very commonly used and fundamental operation, and similarity search is a widely used and powerful tool to improve the scope and functionality of queries. After outsourcing encrypted sparse graph data to cloud servers, it becomes very inconvenient to use and manipulate the data. In this work, we present a scheme to realize the adjacent vertex query supporting similarity search on sparse graph data in cloud environment (SSAQ), which also protects the security of the information. This work uses edit distance and the searchable encryption principle to construct query index, and next implement the similar adjacent vertex query on cloud server. This work provides a formal security analysis, and also gives the experimental comparison and analysis. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： information leakage

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Rehearse With User: Personalized Opinion Summarization via Role-Playing based on Large Language Models

arXiv

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arXiv 2025年

作者： Zhang, Yanyue He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King’s College London United Kingdom The Alan Turing Institute

Personalized opinion summarization is crucial as it considers individual user interests while generating product summaries. Recent studies show that although large language models demonstrate powerful text summarization and evaluation capabilities without the need for training data, they face difficulties in personalized tasks involving long texts. To address this, Rehearsal, a personalized opinion summarization framework via LLMs-based role-playing is proposed. Having the model act as the user, the model can better understand the user’s personalized needs. Additionally, a role-playing supervisor and practice process are introduced to improve the role-playing ability of the LLMs, leading to a better expression of user needs. Furthermore, through suggestions from virtual users, the summary generation is intervened, ensuring that the generated summary includes information of interest to the user, thus achieving personalized summary generation. Experiment results demonstrate that our method can effectively improve the level of personalization in large model-generated summaries. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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