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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Wu, Zhendong Li, Hu Zhang, Ming Lu, Shuaibing National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9789819624676

Deep learning models are widely used in security-sensitive tasks such as facial recognition and autonomous driving. security issues in deep models could have serious implications for people’s lives, such as life-threatening situations resulting from autonomous driving failures. To enhance the security of deep models, security testing is necessary before their formal deployment. Since different scenarios may involve different deep models, a security testing method that targets multiple scenarios would be more universally applicable. In this paper, we propose a security testing method for deep models that can be applied across multiple scenarios, primarily utilizing adversarial attacks to test the models. For the deep model under test, we first construct threat scenarios and then associate the model with datasets and adversarial attack methods. Subsequently, we generate adversarial examples to input into the deep model to check its accuracy in classification. Finally, we evaluate the success rate of adversarial attacks on the deep model. A high attack success rate indicates low model security, necessitating developers to employ adversarial training or other methods to enhance model security. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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TransFlowGAN: Generation and Balancing of Network Traffic Data 24

TransFlowGAN: Generation and Balancing of Network Traffic Da...

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2024 Asia Pacific Conference on Computing Technologies, Communications and Networking, CTCNet 2024

作者： Zhang, Hongzheng National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9798400709609

In recent years, with the rapid development of deep learning technology, researchers in the field of network security have begun to explore the use of deep learning to solve the problem of encrypted traffic classification. However, existing publicly available encrypted traffic datasets suffer from severe class imbalance issues, which significantly impact the performance of deep learning classification methods. Constructing a complete encrypted traffic dataset from scratch is both time-consuming and costly. To address this issue, this paper proposes an innovative model named TransFlowRGAN, which is an encrypted traffic sequence feature generation model that combines Transformer and Relative Generative Adversarial Network (RGAN). Our model leverages the advantages of RGAN, effectively mitigating the instability and mode collapse issues that traditional GAN models often encounter during training. Additionally, it incorporates the Transformer architecture. The self-attention mechanism of the Transformer can better capture the complex features and long-term dependencies in traffic data, resulting in the generation of higher-quality data. We evaluated the performance of our model on the ISCXVPN2016 public dataset. The results demonstrate that TransFlowRGAN excels in handling imbalanced datasets, significantly outperforming other data augmentation models. © 2024 Copyright held by the owner/author(s).

关键词： Generative adversarial networks

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Feature Distillation GAN: An Adversarial Example Defense Method Based on Feature Reconstruction 2nd

Feature Distillation GAN: An Adversarial Example Defense Met...

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2nd International Conference on Network Simulation and Evaluation, NSE 2023

作者： Liu, Ruiqi Li, Hu Wang, Dongxia Zhao, Chongyang Li, Boyu National Key Laboratory of Science and Technology On Information System Security Beijing China

ISBN: (纸本)9789819745180

Purifying and reconstructing adversarial examples into benign ones by generative models is a class of defense methods to eliminate adversarial perturbations. These methods do not make assumptions on adversarial attacks or model architectures, and thus can defend classifiers against unseen threats. In this work, we propose a new GAN-based adversarial purification method Feature Distillation GAN (FDGAN). We consider how to eliminate the feature differences between adversarial examples and benign examples which are difficult for humans to perceive. We extract and analyze the differences between the adversarial examples and the benign examples from multiple dimensionals. On the basis of comparing the differences among various features, we propose a new feature-level perspective on adversarial purification, design a new adversarial example purification method for image inputs, and implement our method based on GAN. Experimental results show that FDGAN is effective against unknown adversarial attacks and outperforms all the purification methods we used for comparison. Evaluating FDGAN against strong adaptive attacks, the robust accuracy of FDGAN under AutoAttack (L∞ = 8/255) exceeds the SOTA adversarial training methods on the Robustbench. Furthermore, FDGAN has lower training cost and can be combined with a variety of model-based defense strategies to further improve the defense effectiveness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distillation

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A new quantum key distribution resource allocation and routing optimization scheme

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Chinese Physics B 2024年第3期33卷 244-259页

作者：毕琳袁晓同吴炜杰林升熙 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130012China Key Laboratory of Network and Information Security in Jilin Province Changchun 130012China

Quantum key distribution(QKD)is a technology that can resist the threat of quantum computers to existing conventional cryptographic ***,due to the stringent requirements of the quantum key generation environment,the generated quantum keys are considered valuable,and the slow key generation rate conflicts with the high-speed data transmission in traditional optical *** this paper,for the QKD network with a trusted relay,which is mainly based on point-to-point quantum keys and has complex changes in network resources,we aim to allocate resources reasonably for data packet ***,we formulate a linear programming constraint model for the key resource allocation(KRA)problem based on the time-slot ***,we propose a new scheduling scheme based on the graded key security requirements(GKSR)and a new micro-log key storage algorithm for effective storage and management of key ***,we propose a key resource consumption(KRC)routing optimization algorithm to properly allocate time slots,routes,and key *** results show that the proposed scheme significantly improves the key distribution success rate and key resource utilization rate,among others.

关键词： quantum key distribution(QKD) resource allocation key storage routing algorithm

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Efficient and secure confidential transaction scheme based on commitment and aggregated zero-knowledge proofs

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Journal of Cyber security technology 2024年第4期8卷 312-332页

作者： Yong, Wang Lijie, Chen Yifan, Wu Qiancheng, Wan School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Cryptography and Information Security Guilin China

As a distributed ledger technology, blockchain has broad applications in many areas such as finance, agriculture, and contract signing due to its advantages of being tamperproof and difficult to forge. However, the open nature of blockchain also introduces severe privacy issues, currently cryptocurrency privacy protection solutions under account-based models cannot balance the internal verification time and confidentiality of transactions. In order to improve the confidentiality and efficiency of transactions under the account-based model, this paper proposes an access control anonymous payment scheme based on homomorphic commitment and aggregated zero-knowledge proofs, focusing on the realization of a one-to-many anonymous transfer function, one-to-one transfer function, deposit function and withdraw function, to ensure the privacy of a transaction data while reducing verification time and Gas costs. We evaluated our approach on a proof-of-concept implementation by generating Solidity contracts and implemented some interesting contracts. The experimental results show that this scheme not only consumes lower gas, but also reduces the internal time during transaction generation. In addition, our solution has established a one to many transfer transaction scheme, and the proof verification time after aggregation is constant. Thus the efficiency of this scheme is particularly well suited for the digital finance scenarios. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Access control

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Experimental demonstration of 220-GHz terahertz signals wireless transmission over 4.6 km

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science China(information sciences) 2025年第1期68卷 386-388页

作者： Yi WEI Jianjun YU Mingxu WANG Xiongwei YANG Qiutong ZHANG Jingwen TAN Bing ZHANG Ying WU Yang HAN Peng TIAN Wen ZHOU Kaihui WANG Feng ZHAO State Key Laboratory of ASIC and System Key Laboratory for Information Science of Electromagnetic Waves (MoE)School of Information Science and Technology Fudan University School of Electronic Engineering Xi'an University of Posts and Telecommunications

The advancement of terahertz (THz) communication technology drives the evolution of wireless communication systems,offering novel pathways and technical means for the development of future 6G communication *** wireless communication systems are often constrained by bandwidth limitations of electronic devices in high frequency ***,THz communication technology leverages the characteristics of electromagnetic waves to transcend these limitations,enabling communication athigher frequencies and wider bandwidths.

关键词： wireless terahertz over demonstration experimental signals transmission

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A backdoor attack against quantum neural networks with limited information

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Chinese Physics B 2023年第10期32卷 219-228页

作者：黄晨猗张仕斌 College of Cyberspace Security Chengdu University of Information TechnologyChengdu 610225China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu 610225China

Backdoor attacks are emerging security threats to deep neural *** these attacks,adversaries manipulate the network by constructing training samples embedded with backdoor *** backdoored model performs as expected on clean test samples but consistently misclassifies samples containing the backdoor trigger as a specific target *** quantum neural networks(QNNs)have shown promise in surpassing their classical counterparts in certain machine learning tasks,they are also susceptible to backdoor ***,current attacks on QNNs are constrained by the adversary's understanding of the model structure and specific encoding *** the diversity of encoding methods and model structures in QNNs,the effectiveness of such backdoor attacks remains *** this paper,we propose an algorithm that leverages dataset-based optimization to initiate backdoor attacks.A malicious adversary can embed backdoor triggers into a QNN model by poisoning only a small portion of the *** victim QNN maintains high accuracy on clean test samples without the trigger but outputs the target label set by the adversary when predicting samples with the ***,our proposed attack cannot be easily resisted by existing backdoor detection methods.

关键词： backdoor attack quantum artificial intelligence security quantum neural network variational quantum circuit

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Robust tube-based MPC with smooth computation for dexterous robot manipulation

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science China(information sciences) 2024年第11期67卷 252-268页

作者： Yu LUO Tianying JI Fuchun SUN Qie SIMA Huaping LIU Mingxuan JING Jianwei ZHANG Department of Computer Science and Technology Tsinghua University Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Department of Informatics University of Hamburg

Dexterous robot manipulation has shone in complex industrial scenarios, where multiple manipulators, or fingers, cooperate to grasp and manipulate objects. When encountering multi-objective optimization with system constraints in such scenarios, model predictive control(MPC) has demonstrated exceptional performance in complex multi-robot manipulation tasks involving multi-objective optimization with system constraints. However, in such scenarios, the substantial computational load required to solve the optimal control problem(OCP) at each triggering instant can lead to significant delays between state sampling and control application, hindering real-time performance. To address these challenges, this paper introduces a novel robust tube-based smooth MPC approach for two fundamental manipulation tasks: reaching a given target and tracking a reference trajectory. By predicting the successor state as the initial condition for imminent OCP solving, we can solve the forthcoming OCP ahead of time, alleviating delay effects. Additionally,we establish an upper bound for linearizing the original nonlinear system, reducing OCP complexity and enhancing response speed. Grounded in tube-based MPC theory, the recursive feasibility and closed-loop stability amidst constraints and disturbances are ensured. Empirical validation is provided through two numerical simulations and two real-world dexterous robot manipulation tasks, which shows that the seamless control input by our methods can effectively enhance the solving efficiency and control performance when compared to conventional time-triggered MPC strategies.

关键词： dexterous robot manipulation model predictive control smooth computation

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Online Fault-Tolerant Tracking Control With Adaptive Critic for Nonaffine Nonlinear systems

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 215-227页

作者： Ding Wang Lingzhi Hu Xiaoli Li Junfei Qiao IEEE the School of Information Science and Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protection and Beijing Institute of Artificial Intelligence Beijing University of Technology

In this paper, a fault-tolerant-based online critic learning algorithm is developed to solve the optimal tracking control issue for nonaffine nonlinear systems with actuator ***, a novel augmented plant is constructed by fusing the system state and the reference trajectory, which aims to transform the optimal fault-tolerant tracking control design with actuator faults into the optimal regulation problem of the conventional nonlinear error system. Subsequently, in order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain an initial admissible tracking policy. Then, the constructed model neural network(NN) is pretrained to recognize the system dynamics and calculate trajectory control. The critic and action NNs are constructed to output the approximate cost function and approximate tracking control,respectively. The Hamilton-Jacobi-Bellman equation of the error system is solved online through the action-critic framework. In theoretical analysis, it is proved that all concerned signals are uniformly ultimately bounded according to the Lyapunov *** tracking control law can approach the optimal tracking control within a finite approximation error. Finally, two experimental examples are conducted to indicate the effectiveness and superiority of the developed fault-tolerant tracking control scheme.

关键词： Adaptive critic control neural network (NN) nonaffine nonlinear systems online fault-tolerant tracking design uniform ultimate boundedness

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Deep radio signal clustering with interpretability analysis based on saliency map

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Digital Communications and Networks 2024年第5期10卷 1448-1458页

作者： Huaji Zhou Jing Bai Yiran Wang Junjie Ren Xiaoniu Yang Licheng Jiao School of Artificial Intelligence Xidian UniversityXi’an710071China Science and Technology on Communication Information Security Control Laboratory Jiaxing314033China

With the development of information technology,radio communication technology has made rapid *** radio signals that have appeared in space are difficult to classify without manually *** radio signal clustering methods have recently become an urgent need for this ***,the high complexity of deep learning makes it difficult to understand the decision results of the clustering models,making it essential to conduct interpretable *** paper proposed a combined loss function for unsupervised clustering based on *** combined loss function includes reconstruction loss and deep clustering *** clustering loss is added based on reconstruction loss,which makes similar deep features converge more in feature *** addition,a features visualization method for signal clustering was proposed to analyze the interpretability of autoencoder utilizing Saliency *** experiments have been conducted on a modulated signal dataset,and the results indicate the superior performance of our proposed method over other clustering *** particular,for the simulated dataset containing six modulation modes,when the SNR is 20dB,the clustering accuracy of the proposed method is greater than 78%.The interpretability analysis of the clustering model was performed to visualize the significant features of different modulated signals and verified the high separability of the features extracted by clustering model.

关键词： Unsupervised radio signal clustering Autoencoder Clustering features visualization Deep learning interpretability

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