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DDoS Attack Autonomous Detection Model Based on Multi-Strategy Integrate Zebra Optimization Algorithm

Computers, Materials & Continua 2025年第1期82卷 645-674页

作者： Chunhui Li Xiaoying Wang Qingjie Zhang Jiaye Liang Aijing Zhang School of Information Engineering Institute of Disaster PreventionLangfang065201China Langfang Key Laboratory of Network Emergency Protection and Network Security Langfang065201China

Previous studies have shown that deep learning is very effective in detecting known ***,when facing unknown attacks,models such as Deep Neural networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low ***,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 *** hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 *** evaluation accuracy of the new dataset GINKS2023 created in this paper is *** to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.

关键词： Distributed denial of service attack intrusion detection deep learning zebra optimization algorithm multi-strategy integrated zebra optimization algorithm

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SFSDA:Secure and Flexible Subset Data Aggregation with Fault Tolerance for Smart Grid

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Intelligent Automation & Soft Computing 2023年第8期37卷 2477-2497页

作者： Dong Chen Tanping Zhou Xu An Wang Zichao Song Yujie Ding Xiaoyuan Yang College of Cryptography Engineering Engineering University of PAPXi’an710086China Key Laboratory of Network and Information Security under the PAP Xi’an710086China Institute of Software Chinese Academy of Sciences Beijing100080China

Smart grid(SG)brings convenience to users while facing great chal-lenges in protecting personal private *** aggregation plays a key role in protecting personal privacy by aggregating all personal data into a single value,preventing the leakage of personal data while ensuring its ***,a flexible subset data aggregation(FSDA)scheme based on the Pail-lier homomorphic encryption was first proposed by Zhang et *** scheme can dynamically adjust the size of each subset and obtain the aggregated data in the corresponding *** this paper,firstly,an efficient attack with both theorems proving and experimentative verification is *** find that in a specific scenario where the encrypted data constructed by a smart meter(SM)exceeds the size of one Paillier ciphertext,the malicious fog node(FN)may use the received ciphertext to obtain the reading of the ***,to avoid the possibility of privacy disclosure under certain circumstances,additional hash functions are added to the individual encryption *** addition,fault tolerance is very important to aggregation schemes in practical *** most of the current schemes,once some SMs failed,then they will not *** far as we know,there is no multi-subset aggregation scheme both supports flexible subset data aggregation and fault ***,we construct the first secure flexible subset data aggregation(SFSDA)scheme with fault tolerance by combining the fault tolerance method with the flexible multi-subset aggregation,where FN enables the control server(CS)to finally decrypt the aggregated ciphertext by recovering equivalent ciphertexts when some SMs fail to submit their *** show that our SFSDA scheme keeps the efficiency in implementing a flexible multi-subset aggregation function,and only has a small delay in implementing fault-tolerant data aggregation.

关键词： Flexible subset aggregation fault tolerance privacy preservation smart grid

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Review of Coverless Image Steganography 4

Review of Coverless Image Steganography

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4th International Conference on Electronic information engineering and Computer Technology, EIECT 2024

作者： Tang, Yixin Di, Fuqiang Zhang, Zhen Zhang, Minqing College of Cryptography Engineering Engineering University of Pap Xi'an China Key Laboratory of Pap for Cryptology and Information Security Xi'an China Key Laboratory of Ctc & Information Engineering Ministry of Education Xi'an China

ISBN: (纸本)9798331528850

Traditional image steganography techniques have revealed many drawbacks due to advancements in steganalysis and steganalysis detection technologies. With the continuous development and improvement of various generator-free models, more and more researchers are shifting their focus to the combination of image generation models and steganography techniques. Compared to traditional methods, generative steganography directly drives the secret information to generate images containing the hidden data, exhibiting better resistance to detection against existing statistical feature-based steganalysis. This paper describes and analyzes GAN-based steganography algorithms as well as the latest steganography algorithms based on diffusion models and deep generative models. At the end of the paper, current issues in generative steganography are discussed, along with future research and development directions. © 2024 IEEE.

关键词： component deep generative models diffusion models GAN steganography

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GAN-INN-Steg: a simple and accurate method for image steganography 2

GAN-INN-Steg: a simple and accurate method for image stegano...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhang, Zhuo Liu, Jia College of Cipher Engineering Engineering University of PAP Shaanxi Xi'an710086 China Key Laboratory of PAP for Cryptology and Information Security Shaanxi Xi'an710086 China

ISBN: (纸本)9781510689053

We propose an efficient and accurate image steganography method that combines generative adversarial networks (GAN) with reversible neural network models. In this approach, the reversible neural network model is used to construct the generator of the GAN. Then, this combined GAN is trained using adversarial training on a fixed dataset, allowing for the rapid acquisition of a generator that can efficiently fit the image distribution of the dataset. Leveraging the bidirectional mapping capability of this generator, we can achieve bidirectional mapping between input noise and forged images. By designing a mapping method for secret messages and noise, we can synthesize several bits of secret messages into a stego image, which can then undergo direct and lossless message extraction using the reverse generator. Unlike other steganography methods, this model can quickly complete iterative training for image steganography, offering enhanced efficiency, security, substantial steganographic capacity, and complete reversibility. We have experimentally validated this method and conducted theoretical analysis and experimental testing on the quality, security, reversibility, and steganographic capacity of the stego images. © 2025 SPIE.

关键词： Steganography

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TMC-GCN: Encrypted Traffic Mapping Classification Method Based on Graph Convolutional networks

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Computers, Materials & Continua 2025年第2期82卷 3179-3201页

作者： Baoquan Liu Xi Chen Qingjun Yuan Degang Li Chunxiang Gu School of Cyberspace Security Zhengzhou UniversityZhengzhou450002China School of Cyberspace Security Information Engineering UniversityZhengzhou450001China Henan Key Laboratory of Network Cryptography Technology Zhengzhou450001China

With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not only reflects the time characteristics of the packet but also strengthens the relationship between the client or server packets. According to FMG, a Traffic Mapping Classification model (TMC-GCN) is designed, which can automatically capture and learn the characteristics and structure information of the top vertex in FMG. The TMC-GCN model is used to classify the encrypted traffic. The encryption stream classification problem is transformed into a graph classification problem, which can effectively deal with data from different data sources and application scenarios. By comparing the performance of TMC-GCN with other classical models in four public datasets, including CICIOT2023, ISCXVPN2016, CICAAGM2017, and GraphDapp, the effectiveness of the FMG algorithm is verified. The experimental results show that the accuracy rate of the TMC-GCN model is 96.13%, the recall rate is 95.04%, and the F1 rate is 94.54%.

关键词： Encrypted traffic classification deep learning graph neural networks multi-layer perceptron graph convolutional networks

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Reversible Data Hiding Algorithm in Encrypted Images Based on Adaptive Median Edge Detection and Ciphertext-Policy Attribute-Based Encryption

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Computers, Materials & Continua 2024年第10期81卷 1123-1155页

作者： Zongbao Jiang Minqing Zhang Weina Dong Chao Jiang Fuqiang Di Key Laboratory of Network and Information Security of People’s Armed Police Chinese People’s Armed Police Force Engineering UniversityXi’an710086China

With the rapid advancement of cloud computing technology,reversible data hiding algorithms in encrypted images(RDH-EI)have developed into an important field of study concentrated on safeguarding privacy in distributed cloud ***,existing algorithms often suffer from low embedding capacities and are inadequate for complex data access *** address these challenges,this paper proposes a novel reversible data hiding algorithm in encrypted images based on adaptive median edge detection(AMED)and ciphertext-policy attributebased encryption(CP-ABE).This proposed algorithm enhances the conventional median edge detection(MED)by incorporating dynamic variables to improve pixel prediction *** carrier image is subsequently reconstructed using the Huffman coding *** image generation is then achieved by encrypting the image based on system user attributes and data access rights,with the hierarchical embedding of the group’s secret data seamlessly integrated during the encryption process using the CP-ABE ***,the encrypted image is transmitted to the data hider,enabling independent embedding of the secret data and resulting in the creation of the marked encrypted *** approach allows only the receiver to extract the authorized group’s secret data,thereby enabling fine-grained,controlled *** results indicate that,in contrast to current algorithms,the method introduced here considerably improves the embedding rate while preserving lossless image ***,the average maximum embedding rates for the(3,4)-threshold and(6,6)-threshold schemes reach 5.7853 bits per pixel(bpp)and 7.7781 bpp,respectively,across the BOSSbase,BOW-2,and USD ***,the algorithm facilitates permission-granting and joint-decryption ***,this paper conducts a comprehensive examination of the algorithm’s robustness using metrics such as image correlation,information entropy,and number of pix

关键词： Ciphertext-policy attribute-based encryption complex data access structure reversible data hiding large embedding space

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Heterogeneous Dual-Dynamic Attention network for Modeling Mutual Interplay of Stocks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3595-3606页

作者： Liu, Haotian Zhou, Yadong Zhou, Yuxun Hu, Bowen Xi'an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security Faculty of Electronic and Information Engineering Xi'an710049 China

Modern quantitative finance and portfolio-based investment hinge on the dependence structure among financial instruments (like stocks) for return prediction, risk management, and hedging. Several attempts have been made to leverage recent advancements in graph neural networks (GNNs) to achieve effective modeling to capture stock interactions. However, existing work suffers from the homogeneity and stationary of the predefined graph structure, and their reliance on hard-coded correlation-based relationships neglects the mutual intervention of different instruments. In this study, we propose the heterogeneous dual-dynamic attention network (HD2AT) to address these challenges in modeling the mutual interplay among stocks, across four key dimensions: heterogeneity, causality, structural dynamics, and lead-lag dynamics. More specifically, our method uncovers the causal interdependencies among instruments, dynamically inferring the heterogeneous network structure from real-time trading activities. Subsequently, we identify the cross-temporal propagation of interventions by integrating interactions at the node, time, and graph levels. Extensive experiments on real-world trading data demonstrate the superiority of the proposed framework over various baselines in terms of both return prediction and back-testing profitability. © 2020 IEEE.

关键词： Commerce

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Verifiable and Forward-Secure Multi-keyword Query in Internet of Medical Things

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IEEE Internet of Things Journal 2025年第13期12卷 23809-23822页

作者： Li, Hongjun He, Debiao Feng, Qi Luo, Min Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan China Wuhan University School of Cyber Science and Engineering Wuhan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China National Key Laboratory of Security Communication Chengdu China

The Internet of Medical Things (IoMT) plays a pivotal role in modern healthcare systems, enhancing patients' medical experiences and improving the efficiency of public medical services. However, concerns regarding security and privacy may hinder the widespread implementation and development of IoMT in practical applications. Dynamic Searchable Symmetric Encryption (DSSE) can maintain search capabilities on encrypted data, even when files are dynamically added or deleted. Earlier DSSE schemes typically support only single keyword query and provide forward security under the assumption of semi-honest servers, which significantly limits their applicability in real-world scenarios. To resolve these limitations, we propose a forward-secure and verifiable DSSE scheme that supports multi-keyword conjunctive query. Our scheme ensures forward security by utilizing a chain structure and guarantees correctness and completeness through RSA accumulator and Homomorphic Message Authentication Code (HMAC). Furthermore, we integrate the forward index and inverted index to enable efficient retrieval. Through comprehensive security and performance analysis, we demonstrate that our scheme effectively protects users' privacy while maintaining low computation and communication overheads. Finally, some experimental evaluations are conducted to verify both the correctness and efficiency of the proposed scheme. © 2014 IEEE.

关键词： Structured Query Language

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Minimizing CNOT-count in quantum circuit of the extended Shor’s algorithm for ECDLP

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Cybersecurity 2025年第1期8卷 153-179页

作者： Xia Liu Huan Yang Li Yang State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of SciencesBeijingChina School of Cyber Security University of Chinese Academy of SciencesBeijingChina Beijing Youzhuju Network Technology Co. Ltd.BeijingChina

The elliptic curve discrete logarithm problem(ECDLP)is a popular choice for cryptosystems due to its high level of ***,with the advent of the extended Shor’s algorithm,there is concern that ECDLP may soon be *** the algorithm does ofer hope in solving ECDLP,it is still uncertain whether it can pose a real threat in *** the perspective of the quantum circuits of the algorithm,this paper analyzes the feasibility of cracking ECDLP using an ion trap quantum computer with improved quantum circuits for the extended Shor’s *** give precise quantum circuits for extended Shor’s algorithm to calculate discrete logarithms on elliptic curves over prime felds,including modular subtraction,three diferent modular multiplication,and modular ***,we incorporate and improve upon windowed arithmetic in the circuits to reduce the *** previous studies mostly focused on minimizing the number of qubits or the depth of the circuit,we focus on minimizing the number of CNOT gates in the circuit,which greatly afects the running time of the algorithm on an ion trap quantum ***,we begin by presenting implementations of basic arithmetic operations with the lowest known CNOT-counts,along with improved constructions for modular inverse,point addition,and windowed ***,we precisely estimate that,to execute the extended Shor’s algorithm with the improved circuits to factor an n-bit integer,the CNOT-count required is1237n^(3)/log n+2n^(2)+***,we analyze the running time and feasibility of the extended Shor’s algorithm on an ion trap quantum computer.

关键词： Elliptic curve discrete logarithm problem Extended Shor’s algorithm Quantum circuits Ion trap quantum computer

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Complexity-Adaptive Reversible Video information Hiding Based on Motion Vectors 4

Complexity-Adaptive Reversible Video Information Hiding Base...

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4th International Conference on Computer Science and Management Technology, ICCSMT 2023

作者： Meng, Yifei Niu, Ke Liang, Yucheng School of Cryptographic Engineering University of Pap Shanxi Xi'an China Key Laboratory of Information Security of Pap Shanxi Xi'an China

ISBN: (纸本)9798400709517

In order to address the issue of the existing motion vector-based video reversible information hiding algorithms, which are unable to adaptively adjust the embedding capacity based on the visual characteristics of video frames and have limited capacity, a multi-channel vector sorting reversible video information hiding algorithm is proposed. This algorithm achieves adaptive information embedding for subsequent frames based on the complexity of reference frame texture and motion. Simultaneously, an improvement is made to the multi-pass pixel value ordering (multi-pass PVO) technique and applied to video information embedding, effectively enhancing the embedding capacity of the reversible hiding algorithm. Experimental results demonstrate that compared to similar algorithms, the changes in PSNR and SSIM are reduced by 14.5% and 8.5% respectively, while the embedding capacity is increased by 7.4%. This algorithm exhibits significant improvements in terms of visual quality and embedding capacity. © 2023 ACM.

关键词： Textures

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