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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Peng, Deming Cai, Yiqiao Fu, Shunkai Luo, Wei College of Computer Science and Technology Huaqiao University Quanzhou China Fujian Key Laboratory of Big Data Intelligence and Security Huaqiao University Xiamen361000 China

ISBN: (纸本)9789811534249

Recently, evolutionary multitasking optimization (EMTO) is proposed as a new emerging optimization paradigm to simultaneously solve multiple optimization tasks in a cooperative manner. In EMTO, the knowledge transfer between tasks is mainly carried out through the assortative mating and selective imitation operators. However, in the literature of EMTO, little study on the selective imitation operator has yet been done to provide a deeper insight in the knowledge transfer across different tasks. Based on this consideration, we firstly study the influence of the inheritance probability (IP) of the selective imitation on an EMTO algorithm, multifactorial differential evolution (MFDE), through the experimental analysis. Then, an adaptive inheritance mechanism (AIM) is introduced into the selective imitation operator of MFDE to automatically adjust the IP value for different tasks at different evolutionary stages. The experimental results on a suite of single-objective multitasking benchmark problems have demonstrated the effectiveness of AIM in enhancing the performance of MFDE. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multitasking

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A Lightweight Authentication Protocol Based on Pid-Ga and Ecc for Mobile Edge Computing

SSRN

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SSRN 2022年

作者： Zhang, Linghui Zhou, Changli Quan, Hanyu Wen, Jingliang College of Computer Science and Technology HuaQiao University Xiamen361021 China Xiamen Key Laboratory of Data Security and Blockchain Technology HuaQiao University Xiamen361021 China

The existing cloud computing network model is no longer applicable to the fast-growing edge terminal environment, and decentralized network model has gradually become a hot topic of concern. However, in the edge computing environment, the existing authentication protocols, on the hand, they lack high security, and on the other hand always use expensive bilinear pairing operations, which do not well meet the network requirements of low latency and high security in edge computing. To this end, we propose a lightweight authentication protocol based on ECC and improve a security algorithm called PID-GA. This algorithm guarantees more user anonymity and privacy security than the traditional method of generating pseudo-identities. In addition, we perform formal and informal security analysis to prove the security of the protocol;and we demonstrate that our scheme outperforms other schemes in terms of both computational and communication overheads through performance comparisons. © 2022, The Authors. All rights reserved.

关键词： Authentication

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Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection 38

Enhancing Semi-Supervised Learning via Representative and Di...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shao, Qian Kang, Jiangrui Chen, Qiyuan Li, Zepeng Xu, Hongxia Cao, Yiwen Liang, Jiajuan Wu, Jian College of Computer Science & Technology Liangzhu Laboratory Zhejiang University China BNU-HKBU United International College China WeDoctor Cloud China The State Key Laboratory of Blockchain and Data Security Zhejiang University China

Semi-Supervised Learning (SSL) has become a preferred paradigm in many deep learning tasks, which reduces the need for human labor. Previous studies primarily focus on effectively utilising the labelled and unlabeled data to improve performance. However, we observe that how to select samples for labelling also significantly impacts performance, particularly under extremely low-budget settings. The sample selection task in SSL has been under-explored for a long time. To fill in this gap, we propose a Representative and Diverse Sample Selection approach (RDSS). By adopting a modified Frank-Wolfe algorithm to minimise a novel criterion α-Maximum Mean Discrepancy (α-MMD), RDSS samples a representative and diverse subset for annotation from the unlabeled data. We demonstrate that minimizing α-MMD enhances the generalization ability of low-budget learning. Experimental results show that RDSS consistently improves the performance of several popular SSL frameworks and outperforms the state-of-the-art sample selection approaches used in Active Learning (AL) and Semi-Supervised Active Learning (SSAL), even with constrained annotation budgets. Our code is available at RDSS. © 2024 Neural information processing systems foundation. All rights reserved.

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Review of data security within energy blockchain:A comprehensive analysis of storage,management,and utilization

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High-Confidence Computing 2024年第3期4卷 82-102页

作者： Yunhua He Zhihao Zhou Yan Pan Fahui Chong Bin Wu Ke Xiao Hong Li School of Information Science and Technology North China University of TechnologyBeijing 100144China China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China Key Laboratory in Blockchain and Data Security by Ministry of Industry and Information Technology Beijing 100040China Institute of Information Engineering Chinese Academy of Sciences Beijing 100195China

Energy systems are currently undergoing a transformation towards new paradigms characterized by decarbonization,decentralization,democratization,and *** this evolving context,energy blockchain,aiming to enhance efficiency,transparency,and security,emerges as an integrated technological solution designed to address the diverse challenges in this *** security is essential for the reliable and efficient functioning of energy *** pressing need to address challenges related to secure data storage,effective data management,and efficient data utilization is increasingly *** paper offers a comprehensive survey of academic discourse on energy blockchain data security over the past five years,adopting an all-encompassing perspective that spans data storage,management,and *** work systematically evaluates and contrasts the strengths and weaknesses of various research ***,this paper proposes an integrated hierarchical on-chain and off-chain security energy blockchain architecture,specifically designed to meet the complex security requirements of multi-blockchain business ***,this paper identifies key directions for future research,particularly in advancing the integration of storage,management,and utilization of energy blockchain data security.

关键词： Energy blockchain data security Storage Management Utilization

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Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

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Computers, Materials & Continua 2023年第8期76卷 1947-1976页

作者： Asif Rahim Yanru Zhong Tariq Ahmad Sadique Ahmad Mohammed A.ElAffendi School of Computer and Information Security Guilin University of Electronic TechnologyGuilin541004China Guangxi Key Laboratory of Intelligent Processing of Computer Images and Graphics Guilin University of Electronic TechnologyGuilin541004China School of Information and Communication Guilin University of Electronic TechnologyGuilin541004China EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of *** Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of *** this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship *** conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 *** compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN *** results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like *** findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this ***,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic *** findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

关键词： Convolutional Neural Network(CNN) hyper-tuning authorship verification digital forensics

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PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature 24

PS3: Precise Patch Presence Test Based on Semantic Symbolic ...

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Zhan, Qi Hu, Xing Li, Zhiyang Xia, Xin Lo, David Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei China Singapore Management University Singapore

ISBN: (纸本)9798400702174

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. PS3 outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options. © 2024 ACM.

关键词： Software design

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Block-gram: Mining Knowledgeable Features for Smart Contract Vulnerability Detection 7th

Block-gram: Mining Knowledgeable Features for Smart Contrac...

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Li, Tao Wang, Haolong Fang, Yaozheng Jian, Zhaolong Wang, Zichun Xie, Xueshuo Tianjin Key Laboratory of Network and Data Security Technology Tianjin China College of Computer Science Nankai University Tianjin China Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province Hangzhou China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031281235

Effective vulnerability detection of large-scale smart contracts is critical because smart contract attacks frequently bring about tremendous economic loss. However, code analysis requiring traversal paths and learning methods requiring many features training is too time-consuming to detect large-scale on-chain contracts. This paper focuses on improving detection efficiency by reducing the dimension of the features, combined with expert knowledge. We propose a feature extraction method Block-gram to form low-dimensional knowledgeable features from the bytecode. We first separate the metadata and convert the runtime code to opcode sequence, dividing the opcode sequence into segments according to some instructions (jump, etc.). Then, we mine extensible Block-gram features for learning-based model training, consisting of 4-dimensional block features and 8-dimensional attribute features. We evaluate these knowledge-based features using seven state-of-the-art learning algorithms to show that the average detection latency speeds up 25 to 650 times, compared with the features extracted by N-gram. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Smart contract

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Transform Sequential data to Image for Detecting Covert Timing Channel

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International Journal of Network security 2024年第2期26卷 224-234页

作者： Huang, Xuwen Chen, Yonghong Zhuang, Xiaolong Lin, Yuwei School of Computer Science and Technology Huaqiao University China Xiamen Key Laboratory of Data Security and Blockchain Technology Huaqiao University Xiamen Fujian 361000 China

The network covert timing channel utilizes inter-packet delay to encode binary data to achieve information leakage and other purposes. In recent years, Covert Timing Channels (CTCs) have been demonstrated to be applicable across various protocols and networks, posing a significant threat to network security. Simultaneously, new CTCs have challenged the efficacy of CTC detection schemes. The recent ϵ-κlibur and ϵ-κlibur-O channels exhibit structural and characteristic similarities to legitimate channels, rendering ML-based detection methods like GAS and Snap ineffective. In this paper, we investigate an approach based on Gramian Angular Field (GAF), Markov Transition Field (MTF), and Recurrence Plot (RP) image processing. We further employ the knowledge-distilled and compressed image classification model MobileVit for detection. Our approach achieves a detection rate 98.24% for seven different channels within a sampling window of 64 IPDs. Experimental results demonstrate our proposed scheme’s generality, sensitivity, and effectiveness. © 2024, Femto Technique Co.. All rights reserved.

关键词： Distillation

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Preventing the Popular Item Embedding Based Attack in Federated Recommendations 40

Preventing the Popular Item Embedding Based Attack in Federa...

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40th IEEE International Conference on data Engineering, ICDE 2024

作者： Zhang, Jun Li, Huan Rong, Dazhong Zhao, Yan Chen, Ke Shou, Lidan Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Aalborg University Department of Computer Science Aalborg Denmark

ISBN: (纸本)9798350317152

Privacy concerns have led to the rise of federated recommender systems (FRS), which can create personalized models across distributed clients. However, FRS is vulnerable to poisoning attacks, where malicious users manipulate gradients to promote their target items intentionally. Existing attacks against FRS have limitations, as they depend on specific models and prior knowledge, restricting their real-world applicability. In our exploration of practical FRS vulnerabilities, we devise a model-agnostic and prior-knowledge-free attack, named PIECK (Popular Item Embedding based Attack). The core module of PIECK is popular item mining, which leverages embedding changes during FRS training to effectively identify the popular items. Built upon the core module, PIECK branches into two diverse solutions: The PIECKIPE solution employs an item popularity enhancement module, which aligns the embeddings of targeted items with the mined popular items to increase item exposure. The PIECKUEA further enhances the robustness of the attack by using a user embedding approximation module, which approximates private user embeddings using mined popular items. Upon identifying PIECK, we evaluate existing federated defense methods and find them ineffective against PIECK, as poisonous gradients inevitably overwhelm the cold target items. We then propose a novel defense method by introducing two regularization terms during user training, which constrain item popularity enhancement and user embedding approximation while preserving FRS performance. We evaluate PIECK and its defense across two base models, three real datasets, four top-tier attacks, and six general defense methods, affirming the efficacy of both PIECK and its defense. © 2024 IEEE.

关键词： Embeddings

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FESR - Framework to evaluate Software Reliability based on software metrics 24

FESR - Framework to evaluate Software Reliability based on s...

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8th International Conference on High Performance Compilation, Computing and Communications, HP3C 2024

作者： Fahad, Hurriya Ahmed, Irfan Mahmood, Jabar Saman Batool, Khadija Ali, Qasim Department of Computer Science University of Sialkot Sialkot Pakistan State Key Laboratory of Blockchain and Data Security School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Zhejiang Hangzhou310007 China

ISBN: (纸本)9798400716904

Evaluating reliability of software is hot concern for decision makers and software engineers seeing as if we assess, it cannot be mastered. It is common that reliability of system stratum could be employed for evaluating growth of testing of system with help of comparison of failure of currently implemented software with required system. It is also generic that reliability of system echelon assures improved utilization by the user consequently enhanced contentment of customers. In this paper, our goal is to put forward a meticulously analysis of reliability measurement process. As the existing approaches still lack software reliability, quality and its growth, successful generation of test cases, scalability with large software systems, cost and customer satisfaction. we have projected a new framework of software reliability measurement process based on software metrics. Our model is evaluated by comparing it with an existing model and a published case study. The control experiment is used to evaluate FESR Framework. © 2024 ACM.

关键词： Software quality

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