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New Coefficient Grouping for Complex Affine Layers

Chinese Journal of Electronics 2025年第2期34卷 520-532页

作者： Qiao Wenxiao Sun Siwei Chen Ying Hu Lei Key Laboratory of Cyberspace Security Defense Institute of Information Engineering Chinese Academy of Sciences Beijing 100085 China School of Cyber Security University of Chinese Academy of Sciences Beijing 100049 China School of Cryptology University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Cryptology Beijing 100878 China Department of Cryptology Science & Technology Beijing Electronic Science & Technology Institute Beijing 100070 China

Recently, designing symmetric primitives for applications in cryptographic protocols including multi-party computation, fully homomorphic encryption, and zero-knowledge proofs has become an important research topic. Among many such new symmetric schemes, a power function over a large finite field \begin{document}$ \mathbb{F}_q $\end{document} is commonly used. In this paper, we revisit the algebraic degree’s growth for a substitution-permutation network (SPN) cipher over \begin{document}$ \mathbb{F}_{2^n} $\end{document}\begin{document}$ (n\ge3) $\end{document}, whose S-box is defined as a composition of a power function \begin{document}$ P(x)=x^{2^d+1} $\end{document} where \begin{document}$ d\ge1 $\end{document} with a polynomial \begin{document}$ A(x)=a_0+ $\end{document}\begin{document}$ \sum\limits_{w=1}^Wa_wx^{2^{\beta_w}} $\end{document} where \begin{document}$ a_i\in\mathbb{F}_{2^n} $\end{document} for \begin{document}$ 0\le i\le W $\end{document} and \begin{document}$ a_w\neq0 $\end{document} for \begin{document}$ 1\le w\le W $\end{document}. We propose a new coefficient grouping technique, which is based on our new description of the monomials that will probably appear in the state. Specifically, we propose a new measure to find proper \begin{document}$ (\beta_1,\beta_2,\dots,\beta_W) $\end{document} for the algebraic degree’s fastest growth and a new method to compute the algebraic degree’s upper bound for arbitrary \begin{document}$ A(x) $\end{document}. Especially for Chaghri, which was presented at ACM CCS 2022, we obtained a tighter upper bound on the algebraic degree.

关键词： Binary extension field Algebraic degree Coefficient grouping technique

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Recommending Orchestration Plan for Space-Ground Integration Information Network: A Subgraph Matching Approach 9

Recommending Orchestration Plan for Space-Ground Integration...

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9th IEEE Smart World Congress, SWC 2023

作者： Xu, Yaobing Guo, Yunchuan Yu, Mingjie Li, Fenghua Guo, Shoukun Fang, Liang Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China University of Science and Technology of China School of Cyber Security Hefei China

ISBN: (纸本)9798350319804

The Space-Ground Integration Information Networks (SGIIN) are facing increasing attacks and risks, highlighting the critical need to recommend network defense orchestration plans for the SGIIN in an efficient manner. However, due to the incompleteness of plans, dynamic topology of the SGIIN, and the non-isomorphism between the plan and topology, existing plan recommendation schemes are unable to effectively work in the SGIIN. To tackle these challenges, we propose a scheme that utilizes a comprehensive measurement to identify the optimal orchestration plan, considering the alignment between plans and the security requirement, the historical performance of plans, and their applicability to dynamic network topology. Moreover, we present a methodology to enhance the accuracy of alignment measurement, as well as an algorithm intended for the identification of plan-graph subgraphs in topology-graph matching. Experimental results demonstrate that the proposed scheme is effective, efficient, and scalable, even in the presence of dynamic changes in network topology. © 2023 IEEE.

关键词： orchestration plan recommendation SGIIN subgraph matching

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Forensic Analysis of I2P Communication Network in Android and macOS Environments 18

Forensic Analysis of I2P Communication Network in Android an...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Wangchuk, Tashi Mandela, Ngaira Mbinda, Tumaini Damedjate, Kamboisssoi Etyang, Felix Makopa, Joel Royal University of Bhutan Department of Information Technology Dewathang Bhutan National Forensic Sciences University School of Cyber Security and Digital Forensics Gandhinagar India Cochin University of Science and Technology School of Computer Science and Engineering Kochi India

ISBN: (纸本)9789380544519

The Invisible Internet Project (I2P) has emerged as a crucial privacy-centric communication network, offering users a secure and anonymous online environment. As the utilization of I2P continues to expand, the demand for specialized forensic techniques tailored to this network becomes imperative. This paper comprehensively investigates I2P forensics within Android and macOS environments, addressing the unique challenges inherent in extracting and scrutinizing digital evidence from I2P applications. The research explores various artifacts and techniques to aid in reconstructing user activities within the I2P network, including analyzing storage files, configuration data, logs, and cache files. Additionally, the study investigates memory forensics as a complementary approach to I2P forensics. The outcomes of this research contribute to advancing forensic analysis techniques in privacy-centric networks, facilitating the efficient investigation of I2P-related crimes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Android forensics dark web digital evidence digital forensics eepsite, Anti-forensics I2P forensics macOS forensics memory forensics privacy-centric networks

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LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection

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Computers, Materials & Continua 2025年第2期82卷 1901-1924页

作者： Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling School of Information and Cybersecurity People’s Public Security University of ChinaBeijing100038China Cyber Investigation Technology Research and Development Center The Third Research Institute of the Ministry of Public SecurityShanghai201204China Department of Cybersecurity Defense Beijing Police CollegeBeijing102202China School of Computer Science Henan Institute of EngineeringZhengzhou451191China

With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal *** deep learning-based text classifiers offer promise,they often exhibit suboptimal performance in fine-grained detection tasks,primarily due to imbalanced datasets and insufficient model representation *** address this challenge,this paper proposes an LLMs-enhanced graph fusion dual-stream Transformer model for fine-grained Chinese malicious SMS *** the data processing stage,Large Language Models(LLMs)are employed for data augmentation,mitigating dataset *** the data input stage,both word-level and character-level features are utilized as model inputs,enhancing the richness of features and preventing information loss.A dual-stream Transformer serves as the backbone network in the learning representation stage,complemented by a graph-based feature fusion *** the output stage,both supervised classification cross-entropy loss and supervised contrastive learning loss are used as multi-task optimization objectives,further enhancing the model’s feature *** results demonstrate that the proposed method significantly outperforms baselines on a publicly available Chinese malicious SMS dataset.

关键词： Transformers malicious SMS multi-task learning large language models

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A Hybrid Solution for Summarizing Diverse Medical Texts in the Health Domain

A Hybrid Solution for Summarizing Diverse Medical Texts in t...

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2024 International Conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Samuel, Prithi Berna, Eugene Kumar, Arun Reshmy, A.K. Sriraj, Swaroop Kadaba Saketh, Yanda Srm Institute Of Science And Technology Department Of Computational Intelligence Kattankulathur India Bannari Amman Institute Of Technology Department Of Artificial Intelligence And Machine Learning Erode India University Of South Wales Department Of Digital Forensics And Cyber Security Treforest United Kingdom

ISBN: (纸本)9798350366846

Since medical text summarization has become essential in facilitating rapid access to vital information for healthcare professionals, this research paper introduces a novel hybrid medical text summarizer that combines both extractive and abstractive summarization techniques while incorporating domain knowledge for the extractive process. Our approach first employs a domain-specific knowledge graph to guide the identification of salient and clinically relevant content from medical literature. This is followed by the application of advanced natural language processing techniques for abstractive summarization, ensuring the generated summaries maintain coherence and readability. We detail the use of the domain-specific knowledge base containing embedder, which captures medical concepts and relationships, enabling the system to discern important information from the input text. The cosine similarity-based ranking algorithm is adapted to prioritize sentences based on their relevance to the domain and their connectivity. The hybrid medical text summarizer is evaluated on a diverse set of medical articles at the same time to value summarizing multiple documents at the same time and compare its performance with existing approaches. Results demonstrate significant improvements in summary quality, as measured by ROUGE scores and human evaluations. Furthermore, we observe that incorporating domain knowledge in the extractive process enhances the overall effectiveness of the summarization system. We achieve a range-topping ROUGE score of a bit more than 0.6 for most of the texts summarized. © 2024 IEEE.

关键词： Domain Knowledge

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Stable NICE Model-Based Picture Generation for Generative Steganography 6th

Stable NICE Model-Based Picture Generation for Generative S...

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6th International Conference on security and Privacy in New Computing Environments, SPNCE 2023

作者： Cui, Xutong Zhou, Zhili Yang, Jianhua Yuan, Chengsheng Tang, Weixuan Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Institute of Artificial Intelligence Guangzhou University Guangdong510006 China School of Cyber Security Guangdong Polytechnic Normal University Guangdong510006 China

ISBN: (纸本)9783031736988

Steganography is one of most important techniques for covert communication. In recent years, generative steganography, which transforms a secret information into a generated picture, is a prospective steganography-resistant technique. Nevertheless, it is difficult to achieve a good trade-off between information hiding ability and extraction accuracy because of the low efficiency and irreversibility of the secret-to-picture conversion. In order to solve this problem, this paper proposes a secret message-driven picture generation solution for generative steganography. The presented SM-IG scheme is founded on the design of a stable version of the Nearly Independent Component Estimation (Stable NICE) model, allowing for a stable bijection mapping between a potential space with simple distributions and an picture space with complex distributions. During the secret to picture conversion, a latent vector is constructed, driven by a given secret message, which is then mapped to the generated picture via the Stable NICE modelAs a result, the secret information is eventually converted into the generated picture. Due to the good efficiency and reversibility of the SM-IG scheme, this steganography method has high hiding capability and accurate message extraction accuracy. The experiments prove that the proposed SM-IG can simultaneously realise good-level hiding capacity (as much as 4 bpp) and precise extraction accuracy (close to 100% accuracy) without compromising the required resistance to detection and imperceptibility. © ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Digital forensics Generative steganography Information hiding Steganography

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Track the Answer: Extending TextVQA from Image to Video with Spatio-Temporal Clues 39

Track the Answer: Extending TextVQA from Image to Video with...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yan Zeng, Gangyan Shen, Huawen Wu, Daiqing Zhou, Yu Ma, Can Institute of Information Engineering Chinese Academy of Sciences China VCIP & TMCC & DISSec College of Computer Science Nankai University China School of Cyber Security University of Chinese Academy of Sciences China School of Cyber Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)157735897X

Video text-based visual question answering (Video TextVQA) is a practical task that aims to answer questions by jointly reasoning textual and visual information in a given video. Inspired by the development of TextVQA in image domain, existing Video TextVQA approaches leverage a language model (e.g. T5) to process text-rich multiple frames and generate answers auto-regressively. Nevertheless, the spatio-temporal relationships among visual entities (including scene text and objects) will be disrupted and models are susceptible to interference from unrelated information, resulting in irrational reasoning and inaccurate answering. To tackle these challenges, we propose the TEA (stands for "Track thE Answer") method that better extends the generative TextVQA framework from image to video. TEA recovers the spatio-temporal relationships in a complementary way and incorporates OCR-aware clues to enhance the quality of reasoning questions. Extensive experiments on several public Video TextVQA datasets validate the effectiveness and generalization of our framework. TEA outperforms existing TextVQA methods, video-language pretraining methods and video large language models by great margins. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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A Privacy-Preserving Data Aggregation Protocol for Internet of Vehicles with Federated Learning

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Xu, Zisang Zhang, Ruirui Liang, Wei Li, Kuan-Ching Gu, Ke Li, Xiong Huang, Jialun Computer and Communication Engineer Institute Changsha University of Science and Technology Changsha China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Department of Computer Science and Information Engineering Providence University Taichung Taiwan Institute for Cyber Security School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

Federated learning (FL) is widely used in various fields because it can guarantee the privacy of the original data source. However, in data-sensitive fields such as Internet of Vehicles (IoV), insecure communication channels, semi-trusted RoadSide Unit (RSU), and collusion between vehicles and the RSU may lead to leakage of model parameters. Moreover, when aggregating data, since different vehicles usually have different computing resources, vehicles with relatively insufficient computing resources will affect the data aggregation efficiency. Therefore, in order to solve the privacy leakage problem and improve the data aggregation efficiency, this paper proposes a privacy-preserving data aggregation protocol for IoV with FL. Firstly, the protocol is designed based on methods such as shamir secret sharing scheme, pallier homomorphic encryption scheme and blinding factor protection, which can guarantee the privacy of model parameters. Secondly, the protocol improves the data aggregation efficiency by setting dynamic training time windows. Thirdly, the protocol reduces the frequent participations of Trusted Authority (TA) by optimizing the fault-tolerance mechanism. Finally, the security analysis proves that the proposed protocol is secure, and the performance analysis results also show that the proposed protocol has high computation and communication efficiency. IEEE

关键词： Vehicles

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Polycystic ovary syndrome detection using optimized SVM and DenseNet

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International Journal of Information technology (Singapore) 2024年第2期17卷 1039-1047页

作者： Silambarasan, E. Nirmala, G. Mishra, Ishani Department of CSE - Cyber Security Indian Institute of Information Technology Kottayam India Department of Electronics and Communication Engineering Mahendra Institute of Technology Namakkal (Dt) India Department of Electronics and Communication New Horizon College of Engineering Bangalore India

Polycystic ovary syndrome (PCOS) is a complicated endocrine disease that significantly impacts the health of women, affecting fertility and leading to various critical conditions. Unfortunately, around 70% of PCOS cases remain undiagnosed, emphasizing the importance of early detection. Ultrasound imaging has emerged as a valuable tool for detecting polycystic ovaries, providing crucial details such as follicle count, size, and position. However, manual diagnosis through ultrasound imaging is laborious and prone to errors, highlighting the need for more objective diagnostic methods. In this study, we propose two distinct predictive models for PCOS detection, utilizing both text and image based datasets. Firstly, an Optimized Support Vector Machine based PCOS detection model is developed using text-based datasets. Secondly, we introduce an image dataset based PCOS detection model using DenseNet. Experimental results demonstrated the suggested models’ effectiveness in accuracy, recall, F-score, and precision for both developed methods. The results showed that the present approaches offer superior performance compared to other methods. © Bharati Vidyapeeth's institute of Computer Applications and Management 2024.

关键词： Classification DenseNet Optimized Support Vector Machine PCOS detection Polycystic ovary syndrome

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ECSNN: Spiking Neural Networks for Efficient Exposure Correction in Endoscopy Imaging

ECSNN: Spiking Neural Networks for Efficient Exposure Correc...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Jun Zheng, Zhuoran Zhang, Jingang Ren, Wenqi Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China School of Cyber Science and Technology Sun Yat-sen University Shenzhen Campus China School of Future Technology University of Chinese Academy of Sciences China

ISBN: (纸本)9798350368741

The quality of endoscopic images is critical to the success of polyp segmentation, highlighting the need for accurate exposure correction in endoscopy. While traditional deep learning methods are effective, they demand substantial computational resources during inference. To address this, we propose the Endoscopic Exposure Correction Spiking Neural Network (ECSNN), an efficient framework designed for resource-limited devices. Our approach features a Positive Incentive Learning Module that reduces noise in input images. These enhanced features are then processed by U-Shape Networks (USNet), which leverages spiking neural networks to learn deep representations for exposure correction. Additionally, we introduce a Brightness Prompt Module consisting of two components: the Brightness Spike Encoding Module (BSEM), which encodes brightness information into spike signals, and the Brightness-Aware Prompt Block (BAPB), which adjusts exposure by guiding the network through brightness-aware attention. We evaluate ECSNN on the Endo4IE and ECSEG datasets, where it outperforms six state-of-the-art methods and demonstrates its practical utility in clinical diagnosis. © 2025 IEEE.

关键词： Endoscopic image exposure correction Medical image enhancement Positive incentive Spiking neural networks

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