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Accumulative Time Based Ranking Method to Reputation Evaluation in information Networks

Journal of computer science & technology 2022年第4期37卷 960-974页

作者： Hao Liao Qi-Xin Liu Ze-Cheng Huang Ke-Zhong Lu Chi Ho Yeung Yi-Cheng Zhang National Engineering Laboratory on Big Data System Computing Technology College of Computer Science and SoftwareEngineeringShenzhen UniversityShenzhen 518060China Guangdong Province Key Laboratory of Popular High Performance Computers Shenzhen UniversityShenzhen 518060China Guangdong Province Engineering Center of China-Made High Performance Data Computing System College of ComputerScience and Software EngineeringShenzhen UniversityShenzhen 518060China Institute of Big Data Intelligent Management and Decision Shenzhen UniversityShenzhen 518060China Department of Science and Environmental Studies The Education University of Hong KongHong Kong 999077China Department of Physics University of FribourgFribourg 1700Switzerland

Due to over-abundant information on the Web, information filtering becomes a key task for online users to obtain relevant suggestions and how to extract the most related item is always a key topic for researchers in various fields. In this paper, we adopt tools used to analyze complex networks to evaluate user reputation and item quality. In our proposed Accumulative Time Based Ranking (ATR) algorithm, we take into account the growth record of the network to identify the evolution of the reputation of users and the quality of items, by incorporating two behavior weighting factors which can capture the hidden facts on reputation and quality dynamics for each user and item respectively. Our proposed ATR algorithm mainly combines the iterative approach to rank user reputation and item quality with temporal dependence compared with other reputation evaluation methods. We show that our algorithm outperforms other benchmark ranking algorithms in terms of precision and robustness on empirical datasets from various online retailers and the citation datasets among research publications. Therefore, our proposed method has the capability to effectively evaluate user reputation and item quality.

关键词： temporal network behavior dynamics reputation evaluation ranking algorithm

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Retrofitting XoM for Stripped Binaries without Embedded Data Relocation

arXiv

引用

arXiv 2024年

作者： Luo, Chenke Ming, Jiang Xie, Mengfei Peng, Guojun Fu, Jianming Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Department of Computer Science School of Science and Engineering Tulane University United States

system programs are frequently coded in memory-unsafe languages such as C/C++, rendering them susceptible to a variety of memory corruption attacks. Among these, just-in-time return-oriented programming (JIT-ROP) stands out as an advanced form of code-reuse attack designed to circumvent code randomization defenses. JIT-ROP leverages memory disclosure vulnerabilities to dynamically harvest reusable code gadgets and construct attack payloads in real-time. To counteract JIT-ROP threats, researchers have developed multiple execute-only memory (XoM) prototypes to prevent dynamic reading and disassembly of memory pages. XoM, akin to the widely deployed W⊕X protection, holds promise in enhancing security. However, existing XoM solutions may not be compatible with legacy and commercial off-the-shelf (COTS) programs, or they may require patching the protected binary to separate code and data areas, leading to poor reliability. In addition, some XoM methods have to modify the underlying architectural mechanism, compromising compatibility and performance. In this paper, we present PXoM, a practical technique to seamlessly retrofit XoM into stripped binaries on the x86-64 platform. As handling the mixture of code and data is a well-known challenge for XoM, most existing methods require the strict separation of code and data areas via either compile-time transformation or binary patching, so that the unreadable permission can be safely enforced at the granularity of memory pages. In contrast to previous approaches, we provide a fine-grained memory permission control mechanism to restrict the read permission of code while allowing legitimate data reads within code pages. This novelty enables PXoM to harden stripped binaries but without resorting to error-prone embedded data relocation. We leverage Intel’s hardware feature, Memory Protection keys, to offer an efficient fine-grained permission control. We measure PXoM’s performance with both micro- and macrobenchmarks, and it only

关键词： C++ (programming language)

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GNN-ASG: A Double Feature Selection-based Adversarial Sample Generation Method in Industrial Control system

GNN-ASG: A Double Feature Selection-based Adversarial Sample...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Lijuan Xu Zhiang Yao Dawei Zhao Xin Li Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Key Laboratory of Computing Power Network and Information Security Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Due to the unique constraints of industrial control data, industrial control adversarial sample attacks are particularly challenging. Existing methods strive to conduct adversarial sample attacks under the conditions of satisfying industrial control data constraints, however, the results are not ideal. Therefore, this study proposes a new adversarial sample generation method GNN-ASG based on double feature selection. GNN-ASG uses data constraints to ensure the rationality of generated data, and uses Graph Deviation Network (GDN) and Autoencoder to improve the quality and versatility of adversarial samples. A new adversarial sample evaluation metrics Adversarial Sample Attack Impact Rate (ASAIR) is proposed to address the problem that existing evaluation metrics are difficult to accurately judge the effectiveness of adversarial sample attacks. This method considers the principle and application environment of adversarial samples, and fully demonstrates the practical effect of adversarial samples. In a comprehensive experiment conducted on three public datasets, GNN-ASG achieves an impressive ASAIR of 21.83%, higher than existing methods of 13.94%. This paper demonstrates the versatility and effectiveness of GNN-ASG by comparing its performance with three state-of-the-art adversarial sample generation methods on four anomaly detection models. GNN-ASG can maximally reduce the F1-Score of the detection model by 0.7605.

关键词： Measurement Federated learning Industrial control Feature extraction Anomaly detection Glass box

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Bidding Management Platform for Cloud-Chain Convergence

Bidding Management Platform for Cloud-Chain Convergence

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International Conference on computer Supported Cooperative Work in Design

作者： Yanzhi Zhang Xiaoming Wu Xiangzhi Liu Huomin Dong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Strengthening the supervision of bidding business is an important measure to optimise the business environment of bidding and ensure fair competition among enterprises. The existing blockchain-based bidding platform has already achieved the uploading of simple business, but with the development of the bidding industry, single-modal data storage is not enough to support the credible traceability of business processes. Therefore, to address the problems of multimodal data uploading, storage space limitation and performance in the bidding field, and combined with the node complexity of the bidding scenario, we design and propose a bidding management platform for cloud-chain convergence(BMPC 3 ), in which ChainMaker serves as the implementation platform. In this alliance chain, we meet different business logic requirements by designing specific smart contracts and storage structures. According to different business data types, the ledger storage method is optimised to meet the on-chain storage requirements of multimodal data such as images, videos, PDFs, etc. Meanwhile, we explore the blockchain architecture and system configuration, and propose an optimisation scheme, which effectively improves the system transaction throughput. The final experiment shows that the transaction throughput of the system can reach 3300TPS, which can meet the application requirements.

关键词： Industries Federated learning Smart contracts Memory Throughput Logic Optimization

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A BiLSTM cardinality estimator in complex database systems based on attention mechanism

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CAAI Transactions on Intelligence technology 2022年第3期7卷 537-546页

作者： Qiang Zhou Guoping Yang Haiquan Song Jin Guo Yadong Zhang Shengjie Wei Lulu Qu Louis Alberto Gutierrez Shaojie Qiao School of Information Science and Technology Southwest Jiaotong UniversityChengduChina School of Software Engineering Chengdu University of Information TechnologyChengduChina School of Energy Power and Mechanical Engineering North China Electric Power UniversityBaodingChina Digital Media Art Key Laboratory of Sichuan ProvinceSichuan Conservatory of MusicChengduChina Department of Computer Science Rensselaer Polytechnic InstituteNew YorkNew YorkUSA

An excellent cardinality estimation can make the query optimiser produce a good execution *** there are some studies on cardinality estimation,the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as *** particular,they are difficult to accurately obtain the complex relationships between multiple tables in complex database *** dealing with complex queries,the existing cardinality estimators cannot achieve good *** this study,a novel cardinality estimator is *** uses the core techniques with the BiLSTM network structure and adds the attention ***,the columns involved in the query statements in the training set are sampled and compressed into ***,the Word2vec model is used to embed the word vectors about the query ***,the BiLSTM network and attention mechanism are employed to deal with word *** proposed model takes into consideration not only the correlation between tables but also the processing of complex *** experiments and the evaluation of BiLSTM-Attention Cardinality Estimator(BACE)on the IMDB datasets are *** results show that the deep learning model can significantly improve the quality of cardinality estimation,which is a vital role in query optimisation for complex databases.

关键词： attention BiLSTM cardinality estimation complex database systems query optimiser Word2vec

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Measurement-based Verification of Quantum Markov Chains

arXiv

引用

arXiv 2024年

作者： Guan, Ji Feng, Yuan Turrini, Andrea Ying, Mingsheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Beijing100190 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Institute of Intelligent Software Guangzhou511458 China The Centre for Quantum Software and Information University of Technology SydneyNSW2007 Australia

Model-checking techniques have been extended to analyze quantum programs and communication protocols represented as quantum Markov chains, an extension of classical Markov chains. To specify qualitative temporal properties, a subspace-based quantum temporal logic is used, which is built on Birkhoff–von Neumann atomic propositions. These propositions determine whether a quantum state is within a subspace of the entire state space. In this paper, we propose the measurement-based linear-time temporal logic MLTL to check quantitative properties. MLTL builds upon classical linear-time temporal logic (LTL) but introduces quantum atomic propositions that reason about the probability distribution after measuring a quantum state. To facilitate verification, we extend the symbolic dynamics-based techniques for stochastic matrices described by Agrawal et al. (JACM 2015) to handle more general quantum linear operators (super-operators) through eigenvalue analysis. This extension enables the development of an efficient algorithm for approximately model checking a quantum Markov chain against an MLTL formula. To demonstrate the utility of our model-checking algorithm, we use it to simultaneously verify linear-time properties of both quantum and classical random walks. Through this verification, we confirm the previously established advantages discovered by Ambainis et al. (STOC 2001) of quantum walks over classical random walks and discover new phenomena unique to quantum walks. © 2024, CC BY.

关键词： Eigenvalues and eigenfunctions

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GLAND SEGMENTATION VIA DUAL ENCODERS AND BOUNDARY-ENHANCED ATTENTION

arXiv

引用

arXiv 2024年

作者： Wang, Huadeng Yu, Jiejiang Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Pathology Ganzhou Municipal Hospital Ganzhou China

Accurate and automated gland segmentation on pathological images can assist pathologists in diagnosing the malignancy of colorectal adenocarcinoma. However, due to various gland shapes, severe deformation of malignant glands, and overlapping adhesions between glands. Gland segmentation has always been very challenging. To address these problems, we propose a DEA model. This model consists of two branches: the backbone encoding and decoding network and the local semantic extraction network. The backbone encoding and decoding network extracts advanced Semantic features, uses the proposed feature decoder to restore feature space information, and then enhances the boundary features of the gland through boundary enhancement attention. The local semantic extraction network uses the pre-trained DeepLabv3+ as a Local semantic-guided encoder to realize the extraction of edge features. Experimental results on two public datasets, GlaS and CRAG, confirm that the performance of our method is better than other gland segmentation methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Extraction

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YOLO-RDD: A road defect detection algorithm based on YOLO

YOLO-RDD: A road defect detection algorithm based on YOLO

引用

International Conference on computer Supported Cooperative Work in Design

作者： Jiabin Pei Xiaoming Wu Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Road maintenance is an indispensable part of the transportation system, ensuring road safety and maximizing road efficiency. The rapid and accurate acquisition of road surface information is crucial for effective road maintenance management. With the development of artificial intelligence, computer vision-based road defect detection methods are becoming popular. In this study, we propose a universal road defect detection model named YOLO-RDD, using You Only Look Once version 8 as the basic framework. Firstly, we introduce a new feature extraction and fusion approach to enhance the interaction between shallow and deep feature information, enabling multi-scale defect detection. Secondly, inspired by dynamic snake convolution, as cracks are the major road defects, we propose a new feature extraction module called DSC-C2f to adapt to the elongated and continuous morphology of cracks. Finally, we establish cross-layer connections between the backbone, neck, and prediction stages and integrate a coordinate attention module that considers inter-channel relationships and long-range positional dependencies. This allows the model to selectively focus on relevant parts of shallow and deep semantic information. We evaluate our model using the dataset provided by the crowdsensing-based road damage detection challenge (CRDDC2022) and further assess its performance on a publicly available crack and sealed crack dataset. The experiments demonstrated that our model achieved the best performance compared to the state-of-the-art (SOTA) models. Compared to the baseline model YOLOv8, our model achieved improvements of 1.2% in Precision, 1.9% in Recall, 1.9% in mAP@0.5, 0.8% in mAP@0.5:0.95, and 1.6% in F1-Score.

关键词： YOLO Cross layer design Roads Semantics Transportation Surface morphology Feature extraction

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CBAN: A DDoS detection method based on CNN, BiGRU, and attention mechanism

CBAN: A DDoS detection method based on CNN, BiGRU, and atten...

引用

2024 International Conference on computer Network security and Software Engineering, CNSSE 2024

作者： Wang, Bing Yu, Yankun Zhao, Chunlan Jiang, Jingjing School of Computer Science and Software Engineering Southwest Petroleum University Sichuan Chengdu610500 China Key Laboratory of Energy Security and Low-carbon development Southwest Petroleum University Sichuan Chengdu610500 China School of Sciences Southwest Petroleum University Sichuan Chengdu610500 China Information Technology Department Jinchuan County Middle School Aba Tibetan and Qiang Autonomous Prefecture Sichuan624100 China

ISBN: (纸本)9781510680234

In order to address the obstacles posed by the growing security issues of the Internet of Things to the development of big data, this paper conducts in-depth research on the defense of the most harmful DDoS attack. This paper designs a deep learning model for DDoS detection - CBAN. The CBAN model integrates technologies such as 1D-CNN, BiGRU, and attention mechanism for structural design. This model can effectively extract spatial and temporal features of network traffic data for efficient detection of potential DDoS attacks. The CBAN model has shown excellent performance on the CIC-DDoS-2019 dataset. © 2024 SPIE.

关键词： Big data

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TIBS: A Deep-Learning Model for Network Intrusion Detection for SDN Environments

TIBS: A Deep-Learning Model for Network Intrusion Detection ...

引用

International Conference on computers, Communications, and systems (ICCCS)

作者： Yilin Zhang Xiaoming Wu Huomin Dong Key Laboratory of Computing Power Network and Information Security Shandong Computer Science Center (National Supercomputer Center in Jinan) Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Software-Defined Networking (SDN) is a modern network strategy that replaces traditional network structures. Its management efficiency and convenience are significantly higher than traditional networks. However, it should be pointed out that the centralized control mechanism adopted in SDN may increase the risk of single points of failure, making it more susceptible to different types of network attacks, such as Distributed Denial of Service (DDoS), DoS, PortScan and Brute Force etc. The identification and defense of these attacks are complex and require effective preventive measures. Network intrusion detection technology increasingly plays an irreplaceable role in network security. Therefore, this paper proposes an intrusion detection method for SDN environments. The system covers multiple stages, each with unique functions and roles. First, it is proposes an imbalanced data oversampling method based on improved Auxiliary Classifier Generative Adversarial Networks (ACGAN) to solve the problem of data imbalance. At the same time, an intrusion detection model TIBS based on Transformer and Inception-BiGRU-SA network is proposed. First, the Encoder part of Transformer is used to capture the connection globally and perform preliminary feature extraction on the input data. Secondly, the improved Inception module is used to extract multi-scale features. Extract, use the self-attention (SA) to weight the extracted spatial features of different scales, use bidirectional gated recurrent unit(BiGRU) to improve the model's ability to extract temporal features, and use the Softmax activation function for final classification. The experiment uses the CIC-IDS-2017 dataset and CIC-DDoS-2019 dataset. The experimental results show that the proposed method has high accuracy and robustness in intrusion detection tasks, and can better identify various types of attack behaviors than traditional methods. And provides more accurate prediction results. Therefore, this method has potential

关键词： Accuracy Computational modeling Network intrusion detection Network security Feature extraction Transformers Data models

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