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3rd International Conference on Algorithms, Data Mining, and information technology, ADMIT 2024

作者： Huang, Haojie Chen, Jinyin Zheng, Haibin Chen, Yixin Huang, Haoben College of Information Engineering Zhejiang University of Technology China Institute of Cyberspace Security Zhejiang University of Technology China

ISBN: (纸本)9798400718120

The rapid development of artificial intelligence has led to the increasing application of image recognition in autonomous driving and surveillance. However, these systems are often vulnerable to adversarial attacks. In this paper, a multi-target optimized projection attack method based on image recognition is designed and implemented to evaluate and improve the security of image recognition system. By controlling the projection parameters, you can control the color, clarity, shape, and size of the projection interference, projecting it onto the object to test the recognition effect. We built a physical platform, implemented the existing attack, and then designed a multi-target optimized projection attack method to optimize the projection properties through particle swarm optimization. Tests on ResNet18 and custom fruit models have shown a 95% success rate for indoor and 90% outdoor dark attacks. This highlights the vulnerability of image recognition to physical adversarial attacks and highlights the need to enhance the system’s anti-interference capabilities, thus providing insights for protecting intelligent systems. © 2024 Copyright held by the owner/author(s).

关键词： Adversarial machine learning

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In‑depth Correlation Power Analysis Attacks on a Hardware Implementation of CRYSTALS‑Dilithium

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Cybersecurity 2025年第2期8卷 229-241页

作者： Huaxin Wang Yiwen Gao Yuejun Liu Qian Zhang Yongbin Zhou School of Cyber Science and Engineering Nanjing University of Science and TechnologyNanjingChina Institute of Information Engineering Chinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina

During the standardisation process of post-quantum cryptography,NIST encourages research on side-channel analysis for candidate *** the recommended lattice signature scheme,CRYSTALS-Dilithium,when implemented on hardware,has seen limited research on side-channel analysis,and current attacks are incomplete or requires a substantial quantity of ***,we conducted a more complete analysis to investigate the leakage of an FPGA implementation of CRYSTALS-Dilithium using the Correlation Power Analysis(CPA)method,where with a minimum of 70,000 traces partial private key coefficients can be ***,we optimise the attack by extracting Point-of-Interests using known information due to parallelism(named CPA-PoI)and by iteratively utilising parallel leakages(named CPA-ITR).Our experimental results show that CPA-PoI reduces the number of traces by up to 16.67%,CPA-ITR by up to 25%,and both increase the number of recovered key coefficients by up to 55.17% and 93.10% using the same number of *** outperfom the CPA *** a result,it suggests that the FPGA implementation of CRYSTALS-Dilithium is more vulnerable than thought before to side-channel analysis.

关键词： CRYSTALS-Dilithium Post-Quantum Cryptography Correlation Power Analysis FPGA Side-Channel Attack

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Boosting for Distributed Online Convex Optimization

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Tsinghua Science and technology 2023年第4期28卷 811-821页

作者： Yuhan Hu Yawei Zhao Lailong Luo Deke Guo Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410073China Medical Engineering Laboratory of Chinese PLA General Hospital and also with the School of Cyberspace SecurityDongguan University of TechnologyDongguan 523000China

Decentralized Online Learning(DOL)extends online learning to the domain of distributed ***,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized *** the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network,applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models.A new boosting method,namely Boosting for Distributed Online Convex Optimization(BD-OCO),is designed to realize the application of boosting in distributed ***-OCO achieves the regret upper bound O(M+N/MNT)where M measures the size of the distributed network and N is the number of Weak Learners(WLs)in each *** core idea of BD-OCO is to apply the local model to train a strong global ***-OCO is evaluated on the basis of eight different real-world *** results show that BD-OCO achieves excellent performance in accuracy and convergence,and is robust to the size of the distributed network.

关键词： distributed Online Convex Optimization(OCO) online boosting Online Gradient Boosting(OGB)

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © 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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Incremental Learning of Radio Modulation Classification Based on Sample Recall

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China Communications 2023年第7期20卷 258-272页

作者： Yan Zhao Shichuan Chen Tao Chen Weiguo Shen Shilian Zheng Zhijin Zhao Xiaoniu Yang School of Communication Engineering Hangzhou Dianzi UniversityHangzhou 310018China Science and Technology on Communication Information Security Control Laboratory Jiaxing 314033China Institute of Cyberspace Security Zhejiang University of TechnologyHangzhou 310023China

Radio modulation classification has always been an important technology in the field of *** difficulty of incremental learning in radio modulation classification is that learning new tasks will lead to catastrophic forgetting of old *** this paper,we propose a sample memory and recall framework for incremental learning of radio modulation *** data with different signal-to-noise ratios,we use a partial memory strategy by selecting appropriate samples for *** compare the performance of our proposed method with three baselines through a large number of simulation *** show that our method achieves far higher classification accuracy than finetuning method and feature extraction ***,it performs closely to joint training method which uses all old data in terms of classification accuracy which validates the effectiveness of our method against catastrophic forgetting.

关键词： radio modulation classification incremen-tal learning deep learning convolutional neural net-work.

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Differential Fault Analysis on 3DES Middle Rounds Based on Error Propagation

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Chinese Journal of Electronics 2022年第1期31卷 68-78页

作者： MA Xiangliang ZHANG Lizhen WU Liji LI Xia ZHANG Xiangmin LI Bing LIU Yuling School of Integrated Circuits Tsinghua University Beijing National Research Center for Information Science and Technology National Research Center for Information Technology Security Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences

Since differential fault analysis(DFA)was first implemented on data encryption standard(DES), many scholars have improved this attack and extended the limit of the original last two rounds to the earlier rounds. However, the performance of the novel attacks which target middle rounds is not effective, i.e. the number of correct/incorrect ciphertexts required is very large and the recovered result maybe not correct. We address this problem by presenting new DFA methods that can break 3 DES when injecting faults at round 12 or *** simulating the process of single-bit error propagation,we have built two kinds of error propagation models as well as an intermediate error propagation state *** we simplify the intermediate states into state templates that will be further used to locate the injected fault position, which is the main difficulty of implementing fault injection in the middle rounds. Finally, in terms of the idea of error propagation and probability theory, we can recover the last round key only using 2 sets of correct/incorrect ciphertexts when inducting fault in the13 th round and 4 sets of correct/incorrect ciphertexts when inducting fault in the 12 th round.

关键词： intermediate error propagation state table probability theory data encryption standard single-bit error propagation fault injection 3DES middle rounds injected fault position cryptography error propagation models DFA methods differential fault analysis state templates inducting fault fault diagnosis error statistics

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Code-Based Conjunction Obfuscation

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Chinese Journal of Electronics 2023年第2期32卷 237-247页

作者： ZHANG Zheng ZHANG Zhuoran ZHANG Fangguo School of Computer Science and Engineering Sun Yat-sen University Guangdong Province Key Laboratory of Information Security Technology

The pattern-matching problem with wildcards can be formulated as a conjunction where an accepting string is same as the pattern for all non-wildcards. A scheme of conjunction obfuscation is a algorithm that “encrypt” the pattern to prevent some adversary from forging any accepting string. Since 2013, there are abundant works about conjunction obfuscation which discussed with weak/strong functionality preservation and distributed black-box security. These works are based on generic group model, learning with error assumption,learning with noise assumption, etc. Our work proposes the first conjunction obfuscation with strong functionality preservation and distributed black-box security from a standard assumption. Our scheme with some parameter constraints can also resist some related attacks such as the information set decoding attack and the structured error arrack.

关键词： Computers Quantum computing Computational modeling Closed box Resists Linear codes Mathematical models

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Energy Efficiency Maximization for Cooperative NOMA with Hardware Impairments

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China Communications 2024年第12期21卷 80-91页

作者： Wang Zhengqiang Chang Ruifei Wan Xiaoyu Fan Zifu Duo Bin School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Computer Science and Cyber Security Chengdu University of TechnologyChengdu 610059China

The massive connectivity and limited energy pose significant challenges to deploy the enormous devices in energy-efficient and environmentally friendly in the Internet of Things(IoT).Motivated by these challenges,this paper investigates the energy efficiency(EE)maximization problem for downlink cooperative non-orthogonal multiple access(C-NOMA)systems with hardware impairments(HIs).The base station(BS)communicates with several users via a half-duplex(HD)amplified-and-forward(AF)***,we formulate the EE maximization problem of the system under HIs by jointly optimizing transmit power and power allocated coefficient(PAC)at BS,and transmit power at the *** original EE maximization problem is a non-convex problem,which is challenging to give the optimal solution ***,we use fractional programming to convert the EE maximization problem as a series of subtraction form ***,variable substitution and block coordinate descent(BCD)method are used to handle the ***,a resource allocation algorithm is proposed to maximize the EE of the ***,simulation results show that the proposed algorithm outperforms the downlink cooperative orthogonal multiple access(C-OMA)scheme.

关键词： block coordinate descent cooperative non-orthogonal multiple access energy efficiency hardware impairments resource allocation

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Super-resolution reconstruction of single image for latent features

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Computational Visual Media 2024年第6期10卷 1219-1239页

作者： Xin Wang Jing-Ke Yan Jing-Ye Cai Jian-Hua Deng Qin Qin Yao Cheng School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610000 China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu 610000China School of Marine Engineering Guilin University of Electronic TechnologyGuilin 541000China School of Computer and Information Security Guilin University of Electronic TechnologyGuilin 541000China

Single-image super-resolution(SISR)typically focuses on restoring various degraded low-resolution(LR)images to a single high-resolution(HR)***,during SISR tasks,it is often challenging for models to simultaneously maintain high quality and rapid sampling while preserving diversity in details and texture *** challenge can lead to issues such as model collapse,lack of rich details and texture features in the reconstructed HR images,and excessive time consumption for model *** address these problems,this paper proposes a Latent Feature-oriented Diffusion Probability Model(LDDPM).First,we designed a conditional encoder capable of effectively encoding LR images,reducing the solution space for model image reconstruction and thereby improving the quality of the reconstructed *** then employed a normalized flow and multimodal adversarial training,learning from complex multimodal distributions,to model the denoising *** so boosts the generative modeling capabilities within a minimal number of sampling *** comparisons of our proposed model with existing SISR methods on mainstream datasets demonstrate that our model reconstructs more realistic HR images and achieves better performance on multiple evaluation metrics,providing a fresh perspective for tackling SISR tasks.

关键词： image superresolution reconstruction denoising diffusion probabilistic model normalized flow adversarial neural network variational auto-encoder

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kProtoClust:Towards Adaptive k-Prototype Clustering without Known k

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Computers, Materials & Continua 2025年第3期82卷 4949-4976页

作者： Yuan Ping Huina Li Chun Guo Bin Hao School of Information Engineering Xuchang UniversityXuchang461000China Henan Province Engineering Technology Research Center of Big Data Security andApplications Xuchang461000China College of Computer Science and Technology Guizhou UniversityGuiyang550025China Here Data Technology Shenzhen518000China

Towards optimal k-prototype discovery,k-means-like algorithms give us inspirations of central samples collection,yet the unstable seed samples selection,the hypothesis of a circle-like pattern,and the unknown K are still challenges,particularly for non-predetermined data *** propose an adaptive k-prototype clustering method(kProtoClust)which launches cluster exploration with a sketchy division of K clusters and finds evidence for splitting and *** behalf of a group of data samples,support vectors and outliers from the perspective of support vector data description are not the appropriate candidates for prototypes,while inner samples become the first candidates for instability reduction of *** from the representation of samples in traditional,we extend sample selection by encouraging fictitious samples to emphasize the representativeness of *** get out of the circle-like pattern limitation,we introduce a convex decomposition-based strategy of one-cluster-multiple-prototypes in which convex hulls of varying sizes are prototypes,and accurate connection analysis makes the support of arbitrary cluster shapes *** by geometry,the three presented strategies make kProtoClust bypassing the K dependence well with the global and local position relationship analysis for data *** results on twelve datasets of irregular cluster shape or high dimension suggest that kProtoClust handles arbitrary cluster shapes with prominent accuracy even without the prior knowledge K.

关键词： Prototype finding convex hull support vector data description geometrical information

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