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DBKEM-AACS: a distributed key escrow model in blockchain with anonymous authentication and committee selection

science China(Information sciences) 2023年第3期66卷 289-290页

作者： Axin XIANG Hongfeng GAO Youliang TIAN Liang WAN College of Computer Science and Technology Guizhou University Institute of Cryptography and Data Security Guizhou University Network and Information Management Center Guizhou University State Key Laboratory of Public Big Data Guizhou University Institute of Computer Software and Theory Guizhou University

Dear editor, Key security is of great practical significance and demand to guarantee the security of digital assets in the blockchain system. At present, users prefer to escrow their assets on centralized institutions, but this phenomenon has gone against the unique characteristics of decentralization and anonymity in the blockchain. Among them, the suspense incidents of assets lock or lost are enough to prove that the security of escrowed keys on the exchanges is questionable.

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Average Performance Analysis of Multi-Class Classification Based on Error-Correcting Output Codes

Average Performance Analysis of Multi-Class Classification B...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Kobayashi, Manabu Kumoi, Gendo Yagi, Hideki Hirasawa, Shigeichi Center for Data Science Waseda University Tokyo Japan Nagaoka University of Technology Department of Information and Management Systems Engineering Niigata Japan University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan

ISBN: (纸本)9781665410205

In machine learning, one of the methods to solve multiclass classification problems is a framework called Error-Correcting Output Codes (ECOC), which constructs a multiclass classifier by combining a lot of binary classifiers. ECOC assigns binary codewords to each category, and the multiclass classification performance varies depending on the code. In this study, we treat each element of the codeword as a random variable and evaluate the average performance of ECOC. As a result, for M class classification if the number of binary classifiers is O(M), then the average error probability of various codes approaches that of MAP estimation. We show that the important points are the ratio between the number of binary classifiers and M and the difference between the maximum posterior probability and the second highest posterior probability for the categories. © 2024 IEEE.

关键词： Coding errors

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ISAR Imaging and Cross-Range Scaling Based on Image Rotation Correlation

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Journal of Beijing Institute of Technology 2022年第2期31卷 196-207页

作者： Fangming Liu Cong Du Yujiao Ding Minghai Wang Wei Dong State Key Laboratory on Integrated Optoelectronics College of Electronic Science&EngineeringJilin UniversityChangchun 130012China. Big Data and Network Management Center Jilin UniversityChangchun 130012China.

With the rapid advancement of technology,not only do we need to acquire a clear in-verse synthetic aperture radar(ISAR)image,but also the real size of the target on the imaging plane,so it’s particularly important for the ISAR to rescale the *** is,the ISAR image which is in the range-Doppler domain is converted into the range-azimuth ***,the key point to solving the problem is to estimate the rotation *** this paper,a new scheme to rescale the images is *** the sake of solving the problem of two-dimensional image blur and target high-speed,the instantaneous range instantaneous Doppler(IRID)method is used to obtain ISAR images,and the rotation parameters are estimated by comparing the rotation correlation of the two *** this method,the error of the estimated rotation parameters is greatly reduced,so that the target can be rescaled *** simulation results verify the ef-fectiveness of the proposed algorithm.

关键词： cross-range scaling instantaneous range instantaneous Doppler(IRID) inverse synthet-ic aperture radar(ISAR) rotational correlation rotation parameters

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Effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images)

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Multimedia Tools and Applications 2024年 1-25页

作者： Ramadass, Sudhir Narayanan, Sreekumar Kumar, Rajiv K, Thilagavathi Data Analytics Sterck Systems Private Limited Chennai Nungambakkam India Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Electronics and Communication Engineering Kumaraguru College of Technology Coimbatore India

For clinical analysis or medical intrusion, imaging technology is a potent tool for producing visual images of a body's inside. However, several existing methods include restricted access to high-quality and diverse datasets, overfitting, a lack of interpretability, high computational costs, varying medical imaging data, and ethical issues concerning patient privacy, consent, and bias. To overcome these issues, the effectiveness of generative adversarial networks in denoising medical imaging (CT/MRI images) is proposed. The input CT images are first gathered and preprocessed to reduce undesired noise using Altered Phase Preserving Dynamic Range Compression (APPDRC). The General Adversarial network (GAN) is then given these preprocessed images to cope with the noisy areas of CT/MRI while also complying with maintaining image structures such as boundary details while preventing over-smoothing, and the weight parameters for the GAN are optimized using the Golden Eagle Optimization (GEO) technique. General Adversarial network (GAN) has piqued the interest of academics studying medical image-denoising mechanisms and developing sophisticated medical image-denoising algorithms based on deep learning. In light of reducing the difficulty in feature learning and improving final diagnosis accuracy, data preprocessing is necessary and crucial in gan-based medical image denoising methods. This work focuses on GAN-based effective denoising approaches in CT/MRI. The potential challenges and research objectives are in the context of data preprocessing in medical image denoising. Because of how CT/MRI images are acquired, it was observed that noise in CT/MRI data follows the distribution. Generative Adversarial networks (GANs) for denoising medical imaging, mainly CT and MRI images, have yielded promising results of 99% accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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Large-Scale network Adaptive Situation Awareness Method in Spatio-Temporal Dimension 1

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1st International Artificial Intelligence Conference, IAIC 2023

作者： Zhang, Hongbin Xu, Ying Liu, Bin Zhao, Dongmei Bai, Yikang School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science Shijiazhuang China

ISBN: (数字)9789819713325

ISBN: (纸本)9789819713318

In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： network security

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Real-Time Cyber Attack Detection Based on Automated Ai Security System 6

Real-Time Cyber Attack Detection Based on Automated Ai Secur...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Madala, Ravikiran Amardeep, Rashmi Sowmya, S.R. Supriya, R.K. Nikshepa, T. Surabhi, K.R. University of Cumberlands Network and Data Center Engineer Ph.D Information Technology DurhamNC United States Dayananda Sagar Academy of Technology and Management Department of Information Science and Engineering Karnataka Bengaluru India

ISBN: (纸本)9798350304480

Cyber security tactics must be more potent and cunning due to the complexity of cybercrimes, which is always expanding. This will enable guard components to make real-time decisions that successfully counter sophisticated threats. The two scientists and professionals should be familiar with current methods for ensuring cyber security in order to aid in this. Implementing automated and expert cycles for recognizing cyber threats is one of the key aspects of cyber security. In light of artificial brain association, we describe an artificial intelligence (AI) cyber risk detection method in this analysis. They gave arrangement utilizes progressed learning-based detection procedures to improve cyber danger distinguishing proof and completely changes countless gathered security occasions into individual occasion profiles. The innovation centers around recognizing valid and bogus positive alerts with the goal those security reviewers can rapidly answer cyber attacks. The exploratory consequences of this study affirm the reasonableness of our proposed approach as a learning-based model for network shortcoming detection, and in any event, when this show is applied to real-world applications;it isn't equivalent to regular AI. It shows that it is better than the technique. © 2023 IEEE.

关键词： AI Artificial Intelligence Automated Cyber Attack Detection Real-Time Security System

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A Study on a Reduced Indicator System for the network Security Situational Assessment Model

A Study on a Reduced Indicator System for the Network Securi...

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Zhang, Hongbin Bai, Yikang Zhao, Dongmei Liu, Bin Xu, Ying Hebei Normal University Hebei Key Laboratory of Network and Information Security Shijiazhuang China Hebei University of Science and Technology School of Information Science and Engineering Shijiazhuang China School of Economics and Management Research Center of Big Data and Social Computing Shijiazhuang China

ISBN: (纸本)9798350327380

It is a challenge to monitor the network security state in a large-scale network environment because the selection of situation assessment indicators is overrun by massive observational information. To address this issue, this paper proposes a network security situational assessment model based on a reduced indicator system. The model categorises the characteristics of network assets and threats into endogenous situational factors and exogenous situational factors. By applying the multi-label extremely random tree algorithm to select the importance of network features, a reduced indicator system with independence and strong comprehensiveness is established. Additionally, the weights of the reduced indicator system are calculated using an improved analytical hierarchy process, which yields the evaluation results for network security situations. The experimental results show that the model improves the accuracy and timeliness of the situation assessment model, and the method can reflect the network situation in real time. © 2023 IEEE.

关键词： network security

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Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators 29

Chosen-Instruction Attack Against Commercial Code Virtualiza...

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29th Annual network and Distributed System Security Symposium, NDSS 2022

作者： Li, Shijia Jia, Chunfu Qiu, Pengda Chen, Qiyuan Ming, Jiang Gao, Debin Tianjin Key Laboratory of Network and Data Security Technology College of Computer Science Nankai University China The University of Texas Arlington United States Singapore Management University Singapore

ISBN: (纸本)1891562746

Code virtualization is a well-known sophisticated obfuscation technique that uses custom virtual machines (VM) to emulate the semantics of original native instructions. Commercial VM-based obfuscators (e.g., Themida and VMProtect) are often abused by malware developers to conceal malicious behaviors. Since the internal mechanism of commercial obfuscators is a black box, it is a daunting challenge for the analyst to understand the behavior of virtualized programs. To figure out the code virtualization mechanism and design deobfuscation techniques, the analyst has to perform reverse-engineering on large-scale highly obfuscated programs. This knowledge learning process suffers from painful cost and imprecision. In this project, we study how to automatically extract knowledge from the commercial VM-based obfuscator via a novel chosen-instruction attack (CIA) technique. Our idea is inspired by chosen-plaintext attack, which is a cryptanalysis attack model to gain information that reduces the security of the encryption scheme. Given a commercial VM-based obfuscator, we carefully construct input programs, proactively interact with the obfuscator, and extract knowledge from virtualized output programs. We propose using the anchor instruction and the guided simplification technique to efficiently locate and extract knowledge-related instructions from output programs, respectively. Our experimental results demonstrate that the modern commercial VM-based obfuscators are under the threat of CIA. We have discovered 760 anchor instructions and extracted 1,915 verified instruction mapping rules from the four most widely used commercial obfuscators. The extracted knowledge enables security analysts to understand virtualized malware and improve deobfuscation techniques. Besides, we also contributed the first fine-grained benchmark suite for systematically evaluating the deobfuscation techniques. The evaluation result shows that three state-of-the-art deobfuscation techniques are ins

关键词： Virtual machine

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What makes a successful rebuttal in computer science conferences?: A perspective on social interaction

What makes a successful rebuttal in computer science confere...

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作者： Huang, Junjie Huang, Win-bin Bu, Yi Cao, Qi Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Information Management Peking University Beijing China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China

With an exponential increase in submissionsto top-tier Computer science (CS) conferences, more and more conferences have introduced a rebuttal stage to the conference peer review process. The rebuttal stage can be modeled as social interactions between authors and reviewers. A successful rebuttal often results in an increased review score after the rebuttal stage. In this paper, we conduct an empirical study to determine the factors contributing to a successful rebuttal using over 3000 papers and 13,000 reviews from ICLR2022, one of the most prestigious computer science conferences. First, we observe a significant difference in review scores before and after the rebuttal stage, which is crucial for paper acceptance. Furthermore, we investigate factors from the reviewer's perspective using signed social network analysis. A notable finding is the increase in balanced network structure after the rebuttal stage. Subsequently, we evaluate several quantifiable author rebuttal strategies and their effects on review scores. These strategies can help authors in improving their review scores. Finally, we used machine learning models to predict rebuttal success and validated the impact of potential factors analyzed in this paper. Our experiments demonstrate that the utilization of all features proposed in this study can aid in predicting the success of the rebuttal. In summary, this work presents a study on the impact factors of successful rebuttals from both reviewers’ and authors’ perspectives and lays the foundation for analyzing rebuttals with social network analysis. © 2023 Elsevier Ltd

关键词： Forecasting

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Stacking-Based Attention Temporal Convolutional network for Action Segmentation 48

Stacking-Based Attention Temporal Convolutional Network for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Liu Jiang, Yu Hong, Junkun Wu, Zhenjie Yang, Zhan Long, Jun Central South University School of Computer Science and Engineering China Central South University Big Data Institute Central South University China Central South University Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha China

ISBN: (纸本)9781728163277

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast. © 2023 IEEE.

关键词： Convolution

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