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

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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Enhancing Parameter-Efficient Transformers with Contrastive Syntax and Regularized Dropout for Neural Machine Translation 21st

Enhancing Parameter-Efficient Transformers with Contrastive...

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

作者： Fan, Yunlong Yang, Zhiheng Li, Baixuan Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9789819601219

Transformers have significantly improved Neural Machine Translation (NMT) models, accompanied by the inherent space complexity of O(n2). While recent approaches aim to be parameter-efficient, they often exhibit limited generalization capabilities with fewer parameters and decreased performances in longer sentences. To this end, we propose two methods, Syntax-enhanced Contrastive Learning (Syn-CL) and JS divergence-based Regularized Dropout (JSR-Drop) for the cross-layer parameter-sharing Transformer (baseline) in NMT. In Syn-CL, we add the corresponding target-to-source instances for the same batch in an extra bidirectional training phase, and minimize the distances of syntax-enhanced representation between the bilingual sentence-pair while maximizing the ones among others. In JSR-Drop, we refine the regularized dropout strategy with JS divergence, and improve generalization capabilities by reducing Transformers’ inconsistencies between training and inference introduced by dropout. Extensive experiments on six NMT tasks, IWSLT2014 (German↔English) and IWSLT2017 (English↔French and English↔Romanian), show an average BLEU score of 37.55, surpassing the vanilla Transformer and the baseline up to 1.75 BLEU, and outperforming models with 1.5×, 2.0× parameters in translating long sentences. Our code is available at https://***/Anon1214/EnPeNMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Random Staircase Generator Matrix Codes: Coding Theorem, Performance Analysis, and Code Design

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IEEE Transactions on information Theory 2025年第5期71卷 3497-3509页

作者： Wang, Qianfan Wang, Yiwen Wang, Yixin Liang, Jifan Ma, Xiao Sun Yat-sen University School of Computer Science Engineering and Guangdong Key Laboratory of Information Security Technology Guangzhou510006 China Sun Yat-sen University School of Systems Science and Engineering Guangzhou510006 China

In this paper, we present a class of codes, referred to as random staircase generator matrix codes (SGMCs), which have staircase-like generator matrices. In the infinite-length region, we prove that the random SGMC is capacity-achieving over binary-input output-symmetric (BIOS) channels. In the finite-length region, we propose the generalized representative ordered statistics decoding with local constraints (LC-ROSD) algorithm for the SGMCs. The most distinguished feature of the SGMCs with LC-ROSD is that the staircase-like matrices enable parallel implementation of the Gaussian elimination (GE), avoiding the serial GE of conventional OSD and supporting a potential low decoding latency, as implied from simulations. To analyze the performance of random SGMCs in the finite-length region, we derive the ensemble weight spectrum and invoke the conventional union bound. We also derive a partially random coding union (RCU) bound, which is tighter than the conventional one and is used as a criterion to design the SGMCs. Staircase-like generator matrices allow us to derive a series of (tighter and tighter) lower bounds based on the second-order Bonferroni inequality with the incremental number of codewords. The numerical results show that the decoding performance can match well with the proposed partially RCU bound for different code rates and different profiles. The numerical results also show that the tailored SGMCs with the LC-ROSD algorithm can approach the finite-length performance bound, outperforming the 5G low-density parity-check (LDPC) codes, 5G polar codes, and Reed-Muller (RM) codes. © 1963-2012 IEEE.

关键词： Decoding

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DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed Problems in Malicious Traffic Detection

DSDIR: A Two-Stage Method for Addressing Noisy Long-Tailed P...

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

作者： Li, Guoliang Zhang, Ruiqi Sun, Zhe Xing, Lingkai Zhang, Yu College of Cyber Science Nankai University Tianjin China College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China Key Laboratory of Data and Intelligent System Security Ministry of Education Tianjin China

ISBN: (纸本)9798350368741

In recent years, deep learning based malicious traffic detection (MTD) systems have demonstrated remarkable success. However, their effectiveness tend to decrease because most malicious traffic datasets are suffered from noisy-labeled and long-tailed problems. While numerous approaches have been developed to address these two problems individually, they become inefficient when confronting the combined challenge of noisy long-tailed data, as they typically tackle only a single adverse factor at a time. This paper proposes a two-stage method called Distribution-aware sample Selection and Dynamic Instance-based Relabeling (DSDIR), which simultaneously addresses the impacts of noisy-labeled and long-tailed problems. In the first stage, a noise-independent clean sample selection method is designed to obtain a clean dataset, which converts negative effects of the long-tailed problem into positive ones. In the second stage, dynamic instance-based relabeling is designed to train a model and improve the dataset's quality simultaneously. Eventually, DSDIR not only produces a balanced and noise-tolerant model but also obtains a clean dataset. Experimental results demonstrate that in the high noise condition of 60% and 80%, the accuracy rate of DSDIR is 5% higher than the state-of-the-art methods. Our code is available at https://***/nku-ligl/DSDIR. © 2025 IEEE.

关键词： deep learning distribution estimation dynamic relabeling malicious traffic detection noisy long-tailed problem

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FirmPorter: Porting RTOSes at the Binary Level for Firmware Re-hosting 26th

FirmPorter: Porting RTOSes at the Binary Level for Firmwa...

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26th International Conference on information and Communications security, ICICS 2024

作者： Xin, Mingfeng Wen, Hui Deng, Liting Li, Hong Li, Qiang Sun, Limin Beijing Key Laboratory of IOT Information Security Technology Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9789819788002

The rapid growth of the Industrial Internet of Things (IIoT) has brought real-time operating system (RTOS) into focus as major targets for both security analysts and malicious adversaries. Due to the non-standard hardware and diverse software, embedded RTOS devices present unique challenges to security analysts for the accurate analysis of firmware binaries. The diversity in hardware components and tight coupling between firmware and hardware makes it hard to perform dynamic analysis, which must have the ability to execute firmware code in virtualized environments. However, emulating the large expanse of hardware peripherals makes analysts have to frequently modify the emulator for executing various firmware code in different virtualized environments, which greatly limits the ability of analysis. In this work, we explore the problem of firmware re-hosting related to the RTOS. A device driver is developed by developers so that RTOS can be run on their platform. By providing alternative implementations for device drivers, we can make minimal modifications to the firmware that is to be migrated from its original hardware environment into a virtualized one. We show that an approach is capable of offering the ability to emulate various RTOS firmware in an automated manner without modifying existing emulators. Our approach, called static binary-level driver porting, first locates device driver initialization function and identify driver types in the target firmware, then adapts pre-built drivers to the existing emulator hardware. Finally, it replaces the drivers in the firmware with ours by utilizing binary rewriting technique. We demonstrate the practicality of the proposed method on multiple hardware platforms and firmware samples for security analysis. The results show that the approach is flexible enough to emulate firmware for vulnerability assessment and exploit development. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Firmware

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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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EPI-Mamba: State Space Model for Semantic Segmentation from Light Fields

EPI-Mamba: State Space Model for Semantic Segmentation from ...

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

作者： Li, Yan Chen, Jianan Wang, Qiong Xu, Jinshan College of Computer Science & Technology Zhejiang University of Technology Hangzhou China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Global contextual dependency is of significance for semantic segmentation from light fields. However, previous works mostly exploit attention mechanisms to model spatial context dependency and angular context dependency separately, since a light field capture is very data-intensive. Considering that self-attention is resource-consuming, existing methods only use it to capture spatial contextual dependency for the central view of light fields, and employ resource-efficient attention mechanisms to model angular view dependencies instead. Inspired by the recent success of State Space Sequence model (SSM), we present a novel SSM-based encoder for light field semantic segmentation, which models spatio-angular contextual dependency together. The proposed method is evaluated on the existing light field semantic segmentation datasets, and experimental results show that the proposed method significantly outperforms previous state-of-the-art methods. The code and models used in this work are available in https://***/YanWQ/EPI-Mamba. © 2025 IEEE.

关键词： Light field semantic segmentation Mamba State space model

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A Code-Based ISE to Protect Boolean Masking in Software

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2025年第2期2025卷 293-332页

作者： Tian, Qi Cheng, Hao Guo, Chun Page, Daniel Wang, Meiqin Wang, Weijia School of Cyber Science and Technology Shandong University Qingdao China Quan Cheng Laboratory Jinan China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Qingdao China School of Computer Science University of Bristol Bristol United Kingdom

Side-Channel Attacks (SCAs) pose a significant threat to data security in embedded environments. To counteract the power-based SCAs, masking is a widely used defense technique, that introduces randomness to obscure the side-channel information generated during the processing of secret data. However, in practice, some challenges exist when implementing masking schemes. For example, in the implementation of Boolean masking, they may refer to low noise level and implementation flaws. To address the said implementation challenges, we present an effective and efficient solution that incorporates the code-based masking technique: We mask the shares of Boolean masking with code-based masking and then use a self-designed Instruction Set Extension (ISE) to perform efficient private computations within this masked domain. Based on a 32-bit RISC-V Ibex core, we develop a prototype implementation of our ISE, whereby it mainly wraps the ALU with three code-based encoders/decoders and integrates a leakage-resilient pseudo-random generator (PRG). Compared to the base core (vanilla Ibex), the hardware overhead of the ISE implementation is only 8%. The security evaluation based on formal verification and practical evaluation demonstrates that our ISE can provide a more robust practical security guarantee. Furthermore, our approach significantly reduces the signal-to-noise ratio (SNR) of each share, decreasing it to just 2% of the original SNR on the base core. © 2025, Ruhr-University of Bochum. All rights reserved.

关键词： Side channel attack

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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FedFLD: Heterogeneous Federated Learning via Forget-Less Distillation

FedFLD: Heterogeneous Federated Learning via Forget-Less Dis...

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

作者： Yi, Xiaoyang Zhang, Jian Chen, Jing Bao, Yuru Xing, Lingkai College of Cyber Science Nankai University China College of Computer Science Nankai University China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9798350368741

Federated learning, as a distributed machine learning paradigm, enhances privacy protection but faces the challenge of heterogeneity. Data-free knowledge distillation (DFKD) methods attempt to overcome this challenge by using a generator to synthesize samples for fine-tuning a global model. However, these methods often suffer from significant shifts in output distribution, leading to catastrophic forgetting. To tackle these issues, we propose FedFLD, a novel federated forget-less distillation framework that mitigates catastrophic forgetting in DFKD while addressing the heterogeneity challenge. Specifically, FedFLD guides the generator's training from three key aspects and constrains the output distribution with an elastic weight consolidation penalty term. By synthesizing diverse samples from different perspectives through additional generator updates, FedFLD facilitates effective knowledge distillation from local models to the global model. Additionally, the global model is further optimized via the heterogeneity fine-tuning process, mitigating the bias from heterogeneity and resulting in a more expressive and robust model. © 2025 IEEE.

关键词： Catastrophic Forgetting Data-free Knowledge Distillation Federated learning Heterogeneity

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