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TCMS: A Multi-Sequence Log Parsing Method Based on Token Conversion

IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering New YorkNY10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer Computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public datasets Anomaly Detection Common Part Multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals Multi Objective Optimization Problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of datasets Template Generation Number Of Templates Similar Template Parse Tree Syntax Errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

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A Credible and Fair Federated Learning Framework Based on Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 301-316页

作者： Chen, Leiming Zhao, Dehai Tao, Liping Wang, Kai Qiao, Sibo Zeng, Xingjie Tan, Chee Wei China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China CSIRO's Data61 Sydney2015 Australia Hefei University of Technology Anhui230009 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Tiangong University School of Software Tianjin300387 China Southwest Petroleum University School of Computer Science Chengdu610500 China

Federated learning (FL) enables cooperative computation between multiple participants while protecting user privacy. Currently, FL algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise in real-world scenarios: 1) Malicious clients disrupt FL through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to solve this problem, most methods have limitations. 2) The current method cannot fairly evaluate client contribution in some scenarios. Some clients exhibit free-rider behavior, seeking to cheat the reward system and manipulate global models. Evaluating client contribution and distributing rewards also present challenges. To address these challenges, we design a trustworthy federated framework to ensure secure computing throughout the federated task process. First, we propose a method of detecting malicious models to guarantee secure model aggregation. Then, we propose a fair method of assessing contribution to identify client-side free-riding behavior. Finally, we implement a computation process based on blockchain and smart contracts to guarantee the trustworthiness and fairness of federated tasks. To validate the performance of our framework, we simulate different types of client attacks and contribution evaluation scenarios on several open-source datasets. The experiments show that our framework ensures the credibility of federated tasks and achieves a fair evaluation of client contributions. © 2020 IEEE.

关键词： Blockchain

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Unveiling the Superiority of Unsupervised Learning on GPU Cryptojacking Detection: Practice on Magnetic Side Channel-Based Mechanism

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4874-4889页

作者： Cao, Hangcheng Xu, Guowen He, Ziyang Shi, Shaoqing Xu, Shengmin Wu, Cong Ning, Jianting Hunan University College of Computer Science and Electronic Engineering Changsha410012 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu610054 China Zhengzhou University School of Cyber Science and Engineering Zhengzhou450001 China Zhengzhou University International College Zhengzhou450001 China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350007 China Nanyang Technological University College of Computing and Data Science Jurong West 639798 Singapore Wuhan University School of Cyber Science and Engineering Wuhan430072 China City University of Macau Faculty of Data Science Macau China

Ample profits of GPU cryptojacking attract hackers to recklessly invade victims’ devices, for completing specific cryptocurrency mining tasks. Such malicious invasion undoubtedly obstructs normal device usage and wastes computation resources. To resist the threat of GPU cryptojacking, existing works aim to timely detect and clear away it, by distinguishing the dissimilitude between it and legitimate applications. However, these detection mechanisms inappropriately rely on two conflict cornerstones, manifested in leveraging mutable samples of illegitimate cryptojacking to design supervision-based detection models requiring samples with stable patterns. This limitation compromises the practicability of existing detection mechanisms in the face of mutable cryptojacking samples. To fill the gap, we explore the superiority of unsupervised learning in handling this issue and further propose an unsupervised manner-enabled detection mechanism named MagInspector, only using legitimate applications’ magnetic signatures from GPU side channels for model construction. MagInspector innovates in training an unsupervised autoencoder network by an adversarial mode that well learns the stable signature patterns of legitimate applications, while incompatible with mutable cryptojacking ones. In the process of model training, we elaborately extract mutual energy cumulation distribution features to represent legitimate applications to overcome the impact of their inter-type differences. Meanwhile, a locality sensitive hashing-driven outlier removal algorithm is designed to enhance MagInspector’s robustness to the noise samples. Finally, extensive experiments are conducted on GPUs covering four generations of common NVIDIA architectures and two generations of AMD architectures;the results show that applying MagInspector to mutable cryptojacking signature detection achieves a significant average accuracy improvement of 25.5% and 17.8%, respectively. © 2005-2012 IEEE.

关键词： Unsupervised learning

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN computer Science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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MPNAS: Multimodal Sentiment Analysis Pruning via Neural Architecture Search

MPNAS: Multimodal Sentiment Analysis Pruning via Neural Arch...

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

作者： Zhang, Binyan Ren, Ao Zhang, Zihao Duan, Moming Liu, Duo Tan, Yujuan Zhong, Kan College of Computer Science Chongqing University Chongqing China School of Big Data Software & Engineering Chongqing University Chongqing China Institute of Data Science National University of Singapore Singapore

ISBN: (纸本)9798350368741

With the rapid development of social media, sentiment analysis from multimodal posts has garnered significant attention in recent years. However, the substantial size of these models impedes their deployment on resource-constrained embedded devices. Although pruning has been extensively studied to reduce the size of unimodal models, specific challenges remain for Multimodal Sentiment Analysis (MSA) models. First, existing techniques prune fixed original models into sparse models, while our findings indicate that different model architectures of identical size yield varying performance outcomes. Second, prior studies fail to explore the unique characteristics of MSA models, resulting in suboptimal pruning performance. To address these challenges, we propose MPNAS, a unified pruning framework via Neural Architecture Search (NAS) for MSA models. Specifically, we formulate pruning as a NAS problem and analyze MSA model characteristics to guide the subnet search. We conduct an initial coarse-grained NAS on the original model, expanding the search space slightly to identify suitable subnets that enhance pruning rates and accuracy. Subsequently, we refine coarse-grained subnets in a fine-grained NAS stage, where MSA model characteristics guide the search process. Extensive experiments on three representative datasets demonstrate the superiority of our approach over existing methods. © 2025 IEEE.

关键词： Multimodal Compression NAS

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A Comprehensive Survey of Floor Plan Image Analysis and Related Applications

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International Journal on Document Analysis and Recognition 2025年 1-19页

作者： Khade, Rasika Jariwala, Krupa Chattopadhyay, Chiranjoy Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Surat Surat India School of Computing and Data Sciences FLAME University Pune India

This paper presents an in-depth exploration of architectural floor plan analysis through computational statistics, addressing the layout optimization and performance enhancement of buildings. Floor plans, which include elements like windows, walls, doors, and appliances, are pivotal for collaboration among architects, engineers, and clients, allowing for tailored designs based on client preferences. The study acknowledges the complexity and diversity of floor plans, which pose significant challenges for both human and machine recognition. Our research provides a thorough review of the evolution and current state of floor plan analysis, as well as a projection into its future. We systematically classify existing models into nine categories-dimensional, room-based, structural, retrieval, object-detection, graph, map, residential, and area-based-and assess them against criteria such as application domain, dataset, publication year, and reported performance metrics. The paper also discusses the practical implications of computational floor plan analysis, such as space optimization, energy efficiency, accessibility, safety, and comfort in the built environment. By processing and recognizing floor plan images, we can gain valuable insights and propose solutions to these issues. Our comprehensive review not only charts the progress in the field but also highlights the unresolved challenges and potential directions for future research. This includes the need for advanced models that can effectively process the intricate details of floor plans to further the goals of sustainable and functional architectural design. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Document Image Floor plan Floor plan analysis Floor plan image retrieval Image Retrieval Neural networks

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Classical Solvability in a 2-D Cross-Diffusion System Arising from the Ion Transport Networks

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Results in Mathematics 2025年第4期80卷 1-15页

作者： Shen, Jieqiong School of Computer and Data Engineering NingboTech University Ningbo 315100 China

This paper is concerned with a three-component cross-diffusion system, which was proposed by Albi, Artina, Foransier and Markowich to model the ion transport networks. This system shares some basic features with the two-component repulsive chemotaxis system, although it also contains a possible singularity and a stronger nonlinearity, which may enhance explosion-supporting properties. The intention of this paper is to find a new type of small initial data which ensures the global existence of classical solution of the corresponding Dirichlet initial-boundary value problem in the two-dimensional setting. Moreover, the boundedness of solution is obtained as well. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： boundedness chemotaxis cross-diffusion system global existence Ion transport networks

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A sharding blockchain-based UAV system for search and rescue missions

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Frontiers of computer Science 2025年第3期19卷 103-118页

作者： Xihan ZHANG Jiashuo ZHANG Jianbo GAO Libin XIA Zhi GUAN Hao HU Zhong CHEN School of Computer Science Peking UniversityBeijing 100871China Peking University Chongqing Research Institute of Big Data Chongqing 401329China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China State Key Lab for Novel Software Technology Nanjing UniversityNanjing 210023China

Sharding is a promising technique to tackle the critical weakness of scalability in blockchain-based unmanned aerial vehicle(UAV)search and rescue(SAR)*** breaking up the blockchain network into smaller partitions called shards that run independently and in parallel,shardingbased UAV systems can support a large number of search and rescue UAVs with improved scalability,thereby enhancing the rescue ***,the lack of adaptability and interoperability still hinder the application of sharded blockchain in UAV SAR *** refers to making adjustments to the blockchain towards real-time surrounding situations,while interoperability refers to making cross-shard interactions at the mission *** address the above challenges,we propose a blockchain UAV system for SAR missions based on dynamic sharding *** from the benefits in scalability brought by sharding,our system improves adaptability by dynamically creating configurable and mission-exclusive shards,and improves interoperability by supporting calls between smart contracts that are deployed on different *** implement a prototype of our system based on Quorum,give an analysis of the improved adaptability and interoperability,and conduct experiments to evaluate the *** results show our system can achieve the above goals and overcome the weakness of blockchain-based UAV systems in SAR scenarios.

关键词： blockchain sharding unmanned aerial vehicle search and rescue blockchain interoperability

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Enhancing Weakly Supervised Medical Segmentation via Heterogeneous Co-training with Box-Wise Augmentation and Pseudo-Label Filtering 6th

Enhancing Weakly Supervised Medical Segmentation via Hetero...

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6th IFIP TC 12 International Conference on Intelligence Science, ICIS 2024

作者： Wang, You Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang State Key Laboratory of Novel Software Technology National Insititute of Health-care Data Science Nanjing University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Data and Computer Science Shandong Woman’s University Jinan China

ISBN: (纸本)9783031712524

In this paper, we introduce an innovative approach to weakly supervised medical image segmentation with box annotations. Different from the previous methods which simply utilize a single conventional network with the same augmentation techniques widely used in supervised segmentation, we aim to introduce diverse augmentations and heterogenous networks to leverage the box annotations for promising generalization ability. Specifically, to amplify the diversity between the contents within the box and its surroundings, we propose the interior and exterior box augmentation (IEBA) technique, in which distinct augmentation techniques are employed for regions inside and outside the bounding boxes. Also, for the purpose of selecting pseudo-labels of superior quality, we propose the pseudo-label filter module (PLFM) to eliminate unreliable pseudo-labels. Besides, as CNN demonstrates superior capabilities in acquiring local information, and ViT specializes in capturing global context, we facilitate a bidirectional learning process between CNN and ViT through quadruple cross consistency losses (QCCL). In inference, we only employ the superior model from the validation set to obtain parameter efficiency. Our approach is evaluated across four tasks on two public datasets, utilizing the 3D dice similarity coefficient as the evaluation metric. The experimental results show that the proposed method outperforms the state-of-the-art comparison methods. © IFIP International Federation for Information Processing 2025.

关键词： Image segmentation

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