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2021 International Conference on Internet of Things and Machine Learning, IoTML 2021

作者： Zhang, Yu Zhou, Yangbo Ma, Xiaowei Tianjin Key Laboratory of Network and Data Security Technology College of Cyber Science Nankai University Tianjin China

ISBN: (数字)9781510653269

ISBN: (纸本)9781510653252

network intrusion detection system (NIDS) is a tool that can detect various network attacks by analyzing network traffic. In recent years, traditional machine learning and deep learning methods have been widely used in NIDS. And these works achieved high detection rate and low false positive rate when identifying common attacks (such as DoS). However, the existing models performed poor in detecting rare or unseen network attacks. In this paper, we design a novel network intrusion detection model based on two-phase detection and manually labeling. This two-phase detection model (TPDM) is a multi-classification system, and it can classify network traffic into benign or specific attack type. TPDM gains the overall accuracy 98.35%, and average F1-score 99.23% on UNSW-NB15 dataset. The experiment shows that TPDM performs better than state of the art in detecting rare attacks with few training samples. Besides, it can detect unknown network attacks and then label these attacks. © SPIE.

关键词： Intrusion detection

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Fuzzing Drone Control System Configurations Based on Quality-Diversity Enhanced Genetic Algorithm 1st

Fuzzing Drone Control System Configurations Based on Qualit...

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1st International Conference on Artificial Intelligence security and Privacy, AIS and P 2023

作者： Chang, Zhiwei Zhang, Hanfeng Yang, Yue Jia, Yan Xu, Sihan Li, Tong Liu, Zheli College of Cyber Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China

ISBN: (纸本)9789819997848

As drones are becoming widely used in various fields, drone security is a growing challenge nowadays. Drone control systems use various configuration parameters to control their positions and attitudes. If these parameters are misconfigured, drones will fall into abnormal flight states, such as trajectory deviation and crash to the ground. Existing works mainly focus on system memory errors which lead to obvious system failure but don’t apply to drone flight state anomalies. This paper focuses on abnormal drone flight states caused by configuration parameter errors. We propose a novel state-guided fuzzing system called APFuzzer, which searches for incorrect configuration parameter values that would trigger abnormal flight states. To enhance the capability of searching for multiple optimal solutions, we design a quality-diversity enhanced genetic algorithm (QDGA) to mutate configurations to search for incorrect configuration parameter values and consider the effects of environmental factors and flight missions on the flight states. We evaluated APFuzzer on the drone control system ArduPilot and successfully searched 3389 incorrect configuration parameter values and triggered all predefined five abnormal flight states. In addition, APFuzzer automatically analyzed the fuzzing results and found five software bugs related to configurations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Genetic algorithms

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Science China(Information Sciences) 2019年第3期62卷 196-198页

作者： Yusong DU Baodian WEI Huang ZHANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Information Security Technology Chongqing Key Lab of Computer Network and Communication Technology

Dear editor,Discrete Gaussian sampling, that is, sampling from a discrete Gaussian distribution DΛ,σ,cwith parameter σ> 0 and center c∈R~n over an ndimensional lattice Λ, has been considered by the cryptography research community as one of the fundamental building blocks of lattice-based cryptography [1, 2]. The simplest lattice is the one-

关键词： A rejection sampling algorithm for off-centered discrete Gaussian distributions over the integers

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Homomorphic Modular Reduction and Improved Bootstrapping for BGV Scheme 17th

Homomorphic Modular Reduction and Improved Bootstrapping for...

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17th International Conference on Information security and Cryptology, Inscrypt 2021

作者： Li, Ruiqi Jia, Chunfu College of Cyber Science Nankai University Tianjin300350 China Tianjin Key Laboratory of Network and Data Security Technology Tianjin300350 China

ISBN: (纸本)9783030883225

Bootstrapping is a crucial subroutine of fully homomorphic encryption (FHE), where a homomorphic encryption scheme evaluates its own decryption circuits. Homomorphic modular reduction is a crucial part of bootstrapping a BGV ciphertext. In this paper, we investigate the homomorphic modular reduction technique. We propose a new homomorphic modular reduction algorithm based on the idea of "blind rotation". This new homomorphic modular reduction procedure requires no basic homomorphic operations, hence it has lower noise accumulation and more suitable for implementing. Furthermore, we also resort to the blind rotation to construct a new bootstrapping procedure for the BGV scheme. We analyze the noise performance and the computational complexity of our scheme. The results illustrate that our new bootstrapping scheme achieves low noise accumulation so that the lattice approximation factor for the underlying worst-case lattice assumption is smaller than that of Chen and Zhang’s work. Meanwhile, the complexity of our bootstrapping scheme is comparable with their scheme. © 2021, Springer Nature Switzerland AG.

关键词： Cryptography

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Research on Text Classification Based on BERT-BiGRU Model 3

Research on Text Classification Based on BERT-BiGRU Model

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2020 3rd International Conference on Modeling, Simulation and Optimization Technologies and Applications, MSOTA 2020

作者： Yu, Qing Wang, Ziyin Jiang, Kaiwen Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

Text classification is a typical application of natural language processing. At present, the most commonly used text classification method is deep learning. Meanwhile there are many difficulties in natural language processing, such as metaphor expression, semantic diversity and grammatical specificity. To solve these problems, this paper proposes the structure of BERT-BiGRU model. First, use the BERT model instead of the traditional word2vec model to represent the word vector, the word representation is calculated according to the context information, and it can be adjusted according to the meaning of word while the context information is fused. Secondly BiGRU model is attached to the BERT model, BiGRU model can extract the text information features from both directions at the same time. Multiple sets of experiments were set up and compared with the model proposed in this paper, according to the final experimental results, using the proposed BERT-BiGRU model for text classification, the final accuracy, recall and F1 score were all above 0.9. It shows that BERT-BiGRU model has good performance in the Chinese text classification task. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Sentiment Analysis of Web Text Based on Deep Learning with a Attention Mechanism 12

Sentiment Analysis of Web Text Based on Deep Learning with a...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Yu, Qing Zhao, Hui Jiang, Kaiwen Tianjin Key Laboratory of Intelligence Computing and Network Security Tianjin University of Technology Tianjin China

ISBN: (纸本)9781728148526

The sentiment analysis of online commentary is a hot research topic in the field of network information mining. Traditional text sentiment analysis methods are mainly based on emotional dictionaries or machine learning. However, it relies on the structure of the sentiment dictionary and the artificial design and extraction features, which limits the generalization ability. In contrast, the deep learning model has a stronger ability to express complex mapping functions from data to emotional semantics. This paper proposes a model combining Bidirectional Gated Recurrent Unit(BGRU) neural network and Convolutional Neural network(CNN), and introduces attention mechanism for text sentiment analysis. The experimental results show that compared with the traditional method, the method effectively improves the accuracy of text sentiment classification. © 2019 IEEE.

关键词： Sentiment analysis

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TMOVF: A Task-Agnostic Model Ownership Verification Framework

TMOVF: A Task-Agnostic Model Ownership Verification Framewor...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Sun, Zhe Huang, Zhongyu Zhao, Wangqi Zhang, Yu Zhang, Jianzhong College of Cyber Science College of Computer Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (纸本)9798350337020

The protection of model intellectual property is becoming an increasingly important issue. However, the existing methods for protecting model ownership, although effective, have limitations. Firstly, they primarily focus on classification models, and secondly, most of the proposed methods reduce the model's utility. To overcome these shortcomings, this paper proposes a task-agnostic model ownership verification framework based on feature fingerprint, called TMOVF, which separates ownership verification from model task. Our key idea is that model knowledge can be uniquely characterized by the extracted features, which may be high-dimensional, complicated, and difficult to compare for each input sample. Nevertheless, these features contain inherent information that cannot be ignored in cases of piracy. To measure the inheritance of our fingerprint, we introduce outlier detection into model ownership verification, which is a first in the field. By reconstructing the outlier detection algorithm, we extract the feature fingerprints of the victim model and the suspicious model, and compute the outliers of their feature fingerprints. By comparing the results, we can verify the ownership of the models. We conduct extensive experiments to evaluate our framework and demonstrate the inheritability of feature fingerprints in stolen models. Our experiments show that the framework is effective in verifying ownership, regardless of the model task. Additionally, our results demonstrate that our framework is more effective than existing methods. © 2023 IEEE.

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Achieving reliable and anti-collusive outsourcing computation and verification based on blockchain in 5G-enabled IoT

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Digital Communications and networks 2022年第5期8卷 644-653页

作者： Linjie Wang Youliang Tian Jinbo Xiong School of Data Science Tongren UniversityTongren554300China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou UniversityGuiyang550025China Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber SecurityFujian Normal UniversityFuzhou350117China

Widespread applications of 5G technology have prompted the outsourcing of computation dominated by the Internet of Things(IoT)cloud to improve transmission efficiency,which has created a novel paradigm for improving the speed of common connected objects in ***,although it makes it easier for ubiquitous resource-constrained equipment that outsources computing tasks to achieve high-speed transmission services,security concerns,such as a lack of reliability and collusion attacks,still exist in the outsourcing *** this paper,we propose a reliable,anti-collusion outsourcing computation and verification protocol,which uses distributed storage solutions in response to the issue of centralized storage,leverages homomorphic encryption to deal with outsourcing computation and ensures data ***,we embed outsourcing computation results and a novel polynomial factorization algorithm into the smart contract of Ethereum,which not only enables the verification of the outsourcing result without a trusted third party but also resists collusion *** results of the theoretical analysis and experimental performance evaluation demonstrate that the proposed protocol is secure,reliable,and more effective compared with state-of-the-art approaches.

关键词： Outsourcing computation Ethereum Smart contract Public verification Homomorphic encryption

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