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Constructing the Impossible Differential of Type-II GFN with Boolean Function and Its Application to WARP

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Chinese Journal of Electronics 2024年第1期33卷 80-89页

作者： Jiali SHI Guoqiang LIU Chao LI College of Liberal Arts and Sciences National University of Defense Technology Hunan Engineering research Center of Commercial cryptography Theory and Technology Innovation State Key laboratory of Information Security Institute of Information Engineering

Type-Ⅱ generalized Feist el network(GFN) has attracted a lot of attention for its simplicity and high *** differential attack is one of the powerful cryptanalytic approaches for word-oriented block ciphers such as Feistel-like *** deduce the impossible differential of Type-Ⅱ GFN by analyzing the Boolean function in the middle *** main idea is to investigate the expression with the variable representing the plaintext(ciphertext) difference words for the internal state *** adopting the miss-in-the-middle approach,we can construct the impossible differential of Type-Ⅱ *** an illustration,we apply this approach to WARP,a lightweight 128-bit block cipher with a 128-bit key which was presented by Banik et *** SAC *** structure of WARP is a 32-branch Type-Ⅱ ***,we find two 21-round truncated impossible differentials and implement a 32-round key recovery attack on *** the 32-round key recovery attack on WARP,some observations are used to mount an effective *** the advantage of the early abort technique,the data,time,and memory complexities are 2125.69chosen plaintexts,2126.6832-round encryptions,and 2100-bit,*** the best of our knowledge,this is the best attack on WARP in the single-key scenario.

关键词： Ciphers Boolean functions Parallel processing Complexity theory

Improving Transferable Targeted Adversarial Attack for Object Detection Using RCEN Framework and Logit Loss Optimization

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Computers, Materials & Continua 2024年第9期80卷 4387-4412页

作者： Zhiyi Ding Lei Sun Xiuqing Mao Leyu Dai Ruiyang Ding School of cryptography Engineering Information Engineering UniversityZhengzhou450000China

Object detection finds wide application in various sectors,including autonomous driving,industry,and *** studies have highlighted the vulnerability of object detection models built using deep neural networks when confronted with carefully crafted adversarial *** not only reveals their shortcomings in defending against malicious attacks but also raises widespread concerns about the security of existing *** existing adversarial attack strategies focus primarily on image classification problems,failing to fully exploit the unique characteristics of object detectionmodels,thus resulting in widespread deficiencies in their ***,previous research has predominantly concentrated on the transferability issues of non-targeted attacks,whereas enhancing the transferability of targeted adversarial examples presents even greater *** attack techniques typically employ cross-entropy as a loss measure,iteratively adjusting adversarial examples to match target ***,their inherent limitations restrict their broad applicability and transferability across different *** address the aforementioned challenges,this study proposes a novel targeted adversarial attack method aimed at enhancing the transferability of adversarial samples across object detection *** the framework of iterative attacks,we devise a new objective function designed to mitigate consistency issues arising from cumulative noise and to enhance the separation between target and non-target categories(logit margin).Secondly,a data augmentation framework incorporating random erasing and color transformations is introduced into targeted adversarial *** enhances the diversity of gradients,preventing overfitting to white-box ***,perturbations are applied only within the specified object’s bounding box to reduce the perturbation range,enhancing attack *** were conducted on the Microsoft

关键词： Object detection model security targeted attack gradient diversity

The Exchange Attack and the Mixture Differential Attack Revisited: From the Perspective of Automatic Evaluation

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Chinese Journal of Electronics 2024年第1期33卷 19-29页

作者： Kexin QIAO Zhiyu ZHANG Zhongfeng NIU Liehuang ZHU School of Cyberspace Science and Technology Beijing Institute of Technology State Key laboratory of Cryptology Institute of Information Engineering Chinese Academy of Sciences School of cryptography University of Chinese Academy of Sciences

Recent results show that the differential properties within quadruples boom as a new inspiration in cryptanalysis of Advanced Encryption Standard(AES)-like constructions. These methods include the exchange attack proposed in Asiacrypt’19, the mixture differential attack proposed in To SC’18, etc., where the essential properties are obtained by manually scrutinizing the structures of the AES-like constructions. This paper presents a novel framework and an automatic tool based on mixed integer linear programming to search for mixture differential distinguishers for general constructions. This framework considers what equality patterns among quadruples can make a distinguisher and traces how the patterns propagate through cipher components with accurate probability *** this tool, a 5-round AES distinguishing attack with lower complexity and more 6-round distinguishing attacks in the chosen plaintext scenarios are deduced. We prove that no exchange-type or mixture differential distinguisher exists for 7 and above rounds AES if the details of the Sbox and Mix Columns matrix are not taken into account.

关键词： Ciphers Estimation Encryption Mixed integer linear programming Complexity theory Standards

Automatically identifying imperfections and attacks in practical quantum key distribution systems via machine learning

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Science China(Information Sciences) 2024年第10期67卷 359-372页

作者： Jiaxin XU Xiao MA Jingyang LIU Chunhui ZHANG Hongwei LI Xingyu ZHOU Qin WANG Institute of Quantum Information and Technology Nanjing University of Posts and Telecommunications Telecommunication and Networks National Engineering Research CenterNanjing University of Posts and Telecommunications Henan Key laboratory of Quantum Information and cryptography Strategic Support Force Information Engineering University

The realistic security of quantum key distribution(QKD) systems is currently a hot research topic in the field of quantum communications. There are always defects in practical devices, and eavesdroppers can make use of the security risk points of various devices to obtain key information. To date, current types of security analysis tend to analyze each security risk point individually, thereby posing great challenges for the overall security evaluation of QKD systems. In this paper, for the first time, we employ machine learning algorithms to identify the defects of different devices and certain attacks in real time, with an accuracy of 98%. It provides a novel solution for the practical security evaluation of QKD systems, thereby addressing the bottleneck problem of multiple risk points being difficult to address simultaneously in QKD systems,thus paving the way for the future large-scale application of quantum communication networks.

关键词： quantum key distribution device imperfections eavasdropping attacks real-time detection machine learning algorithms

Security analysis of satellite-to-ground reference-frame-independent quantum key distribution with beam wandering

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Chinese Physics B 2024年第8期33卷 192-201页

作者：周淳赵燕美杨晓亮陆宜飞周雨姜晓磊汪海涛汪洋李家骥江木生汪翔张海龙李宏伟鲍皖苏 Henan Key laboratory of Quantum Information and cryptography SSF IEUZhengzhou 450001China Synergetic Innovation Centre of Quantum Information and Quantum Physics University of Science and Technology of ChinaHefei 230026China

The reference-frame-independent(RFI)quantum key distribution(QKD)is suitable for satellite-based links by removing the active alignment on the reference ***,how the beam wandering influences the performance of RFI-QKD remains a pending issue in satellite-to-ground *** this paper,based on the mathematical model for characterizing beam wandering,we present the security analysis for satellite-to-ground RFI-QKD and analytically derive formulas for calculating the secret key rate with beam *** simulation results show that the performance of RFI-QKD is better than the Bennett–Brassard 1984(BB84)QKD with beam wandering in asymptotic ***,the degree of influences of beam wandering is specifically presented for satellite-to-ground RFI-QKD when statistical fluctuations are taken into *** work can provide theoretical support for the realization of RFI-QKD using satellite-to-ground links and have implications for the construction of large-scale satellite-based quantum networks.

关键词： quantum key distribution satellite-to-ground beam wandering

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A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

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Computers, Materials & Continua 2024年第8期80卷 2161-2179页

作者： Wen Jiang Mingshu Zhang Xu’an Wang Wei Bin Xiong Zhang Kelan Ren Facheng Yan College of cryptography Engineering Engineering University of People’s Armed PoliceXi’an710086China Key laboratory of Network and Information Security Engineering University of People’s Armed PoliceXi’an710086China Key laboratory of CTC&Information Engineering Engineering University of People’s Armed PoliceMinistry of EducationXi’an710086China

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more *** detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific *** order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is *** method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature *** algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news ***-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the *** then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that ***,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for *** experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

关键词： Fake news detection domain-related emotional features semantic features feature fusion

An Efficient and Secure Privacy-Preserving Federated Learning Framework Based on Multiplicative Double Privacy Masking

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Computers, Materials & Continua 2024年第9期80卷 4729-4748页

作者： Cong Shen Wei Zhang Tanping Zhou Yiming Zhang Lingling Zhang College of cryptography Engineering Engineering University of PAPXi’an710086China Key laboratory of People’s Armed Police for Cryptology and Information Security Xi’an710086China College of Information Engineering Engineering University of PAPXi’an710086China

With the increasing awareness of privacy protection and the improvement of relevant laws,federal learning has gradually become a new choice for cross-agency and cross-device machine *** order to solve the problems of privacy leakage,high computational overhead and high traffic in some federated learning schemes,this paper proposes amultiplicative double privacymask algorithm which is convenient for homomorphic addition *** combination of homomorphic encryption and secret sharing ensures that the server cannot compromise user privacy from the private gradient uploaded by the *** the same time,the proposed TQRR(Top-Q-Random-R)gradient selection algorithm is used to filter the gradient of encryption and upload efficiently,which reduces the computing overhead of 51.78%and the traffic of 64.87%on the premise of ensuring the accuracy of themodel,whichmakes the framework of privacy protection federated learning lighter to adapt to more miniaturized federated learning terminals.

关键词： Federated learning privacy protection homomorphic encryption double mask secret sharing gradient selection

Anomaly Detection in Imbalanced Encrypted Traffic with Few Packet Metadata-Based Feature Extraction

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Computer Modeling in Engineering & Sciences 2024年第10期141卷 585-607页

作者： Min-Gyu Kim Hwankuk Kim Department of Financial Information Security Kookmin UniversitySeoul02707Republic of Korea Department of Information Security cryptography Mathematics Kookmin UniversitySeoul02707Republic of Korea

In the IoT(Internet of Things)domain,the increased use of encryption protocols such as SSL/TLS,VPN(Virtual Private Network),and Tor has led to a rise in attacks leveraging encrypted *** research on anomaly detection using AI(Artificial Intelligence)is actively progressing,the encrypted nature of the data poses challenges for labeling,resulting in data imbalance and biased feature extraction toward specific *** study proposes a reconstruction error-based anomaly detection method using an autoencoder(AE)that utilizes packet metadata excluding specific node *** proposed method omits biased packet metadata such as IP and Port and trains the detection model using only normal data,leveraging a small amount of packet *** makes it well-suited for direct application in IoT environments due to its low resource *** experiments comparing feature extraction methods for AE-based anomaly detection,we found that using flowbased features significantly improves accuracy,precision,F1 score,and AUC(Area Under the Receiver Operating Characteristic Curve)score compared to packet-based ***,for flow-based features,the proposed method showed a 30.17%increase in F1 score and improved false positive rates compared to Isolation Forest and ***,the proposedmethod demonstrated a 32.43%higherAUCwhen using packet features and a 111.39%higher AUC when using flow features,compared to previously proposed oversampling *** study highlights the impact of feature extraction methods on attack detection in imbalanced,encrypted traffic environments and emphasizes that the one-class method using AE is more effective for attack detection and reducing false positives compared to traditional oversampling methods.

关键词： One-class anomaly detection feature extraction auto-encoder encrypted traffic CICIoT2023

Robust Information Hiding Based on Neural Style Transfer with Artificial Intelligence

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Computers, Materials & Continua 2024年第5期79卷 1925-1938页

作者： Xiong Zhang Minqing Zhang Xu AnWang Wen Jiang Chao Jiang Pan Yang College of cryptography Engineering Engineering University of People’s Armed PoliceXi’an710086China Key laboratory of People’s Armed Police for Cryptology and Information Security Xi’an710086China Key laboratory of CTC&Information Engineering(Engineering University of People’s Armed Police) Ministry of EducationXi’an710086China Staff Department of People’s Armed Police Ningxia Corps Yinchuan750000China

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during *** algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during *** method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded *** process effectively enhances the concealment and imperceptibility of confidential information,thereby improving the security of such information during transmission and reducing security ***,we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical *** adversarial training,the algorithm is strengthened to enhance its resilience against attacks and overall robustness,ensuring better protection against potential *** results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding ***,it ensures the quality and fidelity of the stego *** method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information.

关键词： Information hiding neural style transfer robustness