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SAT-Based Automatic Searching for Differential and Linear Trails: Applying to CRAX

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Chinese Journal of Electronics 2024年第1期33卷 72-79页

作者： Yiyi HAN Caibing WANG Zhongfeng NIU Lei HU State Key Laboratory of information Security Institute of Information Engineering Chinese Academy of Sciences school of Cyber Security University of Chinese Academy of Sciences

Boolean satisfiability problem(SAT) is now widely applied in differential cryptanalysis and linear cryptanalysis for various cipher *** generated many excellent results for some ciphers,for example,*** this research,we study the differential and linear propagations through the operations of addition,rotation and XOR(ARX),and construct the SAT *** apply the models to CRAX to search differential trails and linear trails *** this sense,our contnbution can be broadly divided into two *** give the bounds for differential and linear cryptanalysis of Alzette both up to 12 steps,by which we present a 3-round differential attack and a 3-round linear attack for *** construct a 4-round key-recovery attack for CRAX with time complexity289times of 4-round encryption and data complexity 225.

关键词： Ciphers Encryption Complexity theory Time complexity

Modifying Lyapunov exponent of chaotic map by self-cascading

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Science China(Technological sciences) 2024年第7期67卷 2203-2214页

作者： YI ChenLong LI ChunBiao LI YongXin XIA Ming HUA ZhongYun school of Electronic and information Engineering Nanjing University of Information Science&TechnologyNanjing 210044China school of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing 210044China school of Computer Science and Technology Harbin Institute of Technology Shenzhen Graduate SchoolShenzhen 518055China

The self-cascade(SC) method is an effective technique for chaos enhancement and complexity increasing in chaos ***, the controllable self-cascade(CSC) method allows for more accurate control of Lyapunov exponents of the discrete map. In this work, the SC and CSC systems of the original map are derived, which enhance the chaotic performance while preserving the fundamental dynamical characteristics of the original map. Higher Lyapunov exponent of chaotic sequences corresponding to higher frequency are obtained in SC and CSC systems. Meanwhile, the Lyapunov exponent could be linearly controlled with greater flexibility in the CSC system. The verification of the numerical simulation and theoretical analysis is carried out based on the platform of CH32.

关键词： self-cascade controllable self-cascade chaos enhancement chaos control

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Multi-instance partial-label learning: towards exploiting dual inexact supervision

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Science China(information sciences) 2024年第3期67卷 48-61页

作者： Wei TANG Weijia ZHANG Min-Ling ZHANG school of Computer Science and Engineering Southeast University Key Laboratory of Computer Network and information Integration (Southeast University) Ministry of Education school of information and Physical sciences The University of Newcastle

Weakly supervised machine learning algorithms are able to learn from ambiguous samples or labels, e.g., multi-instance learning or partial-label learning. However, in some real-world tasks, each training sample is associated with not only multiple instances but also a candidate label set that contains one groundtruth label and some false positive labels. Specifically, at least one instance pertains to the ground-truth label while no instance belongs to the false positive labels. In this paper, we formalize such problems as multi-instance partial-label learning(MIPL). Existing multi-instance learning algorithms and partial-label learning algorithms are suboptimal for solving MIPL problems since the former fails to disambiguate a candidate label set, and the latter cannot handle a multi-instance bag. To address these issues, a tailored algorithm named MIPLGP, i.e., multi-instance partial-label learning with Gaussian processes, is *** first assigns each instance with a candidate label set in an augmented label space, then transforms the candidate label set into a logarithmic space to yield the disambiguated and continuous labels via an exclusive disambiguation strategy, and last induces a model based on the Gaussian processes. Experimental results on various datasets validate that MIPLGP is superior to well-established multi-instance learning and partial-label learning algorithms for solving MIPL problems.

关键词： machine learning multi-instance partial-label learning multi-instance learning partial-label learning Gaussian processes

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Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

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Computers, Materials & Continua 2024年第3期78卷 3431-3448页

作者： Mahmood A.Mahmood Khalaf Alsalem Department of information Systems College of Computer and Information SciencesJouf UniversitySakakahKingdom of Saudi Arabia Department of information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityGizaEgypt

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic *** detection of these diseases is essential for effective *** propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf *** proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf *** model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning *** the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and *** wavelet transformation,the augmented images are decomposed into three frequency *** pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning *** models were trained using the approximate images of the third-level sub-band of the wavelet *** the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout *** proposed model was evaluated using a dataset of images of healthy and infected olive *** achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the *** finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

关键词： Olive leaf diseases wavelet transform deep learning feature fusion

Identifying malicious traffic under concept drift based on intraclass consistency enhanced variational autoencoder

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Science China(information sciences) 2024年第8期67卷 238-252页

作者： Xiang LUO Chang LIU Gaopeng GOU Gang XIONG Zhen LI Binxing FANG Institute of information Engineering Chinese Academy of Sciences school of Cyber Security University of Chinese Academy of Sciences school of Computer Science and Technology Harbin Institute of Technology (Shenzhen)

Accurate identification of malicious traffic is crucial for implementing effective defense countermeasures and has led to extensive research efforts. However, the continuously evolving techniques employed by adversaries have introduced the issues of concept drift, which significantly affects the performance of existing methods. To tackle this challenge, some researchers have focused on improving the separability of malicious traffic representation and designing drift detectors to reduce the number of false ***, these methods often overlook the importance of enhancing the generalization and intraclass consistency in the representation. Additionally, the detectors are not sufficiently sensitive to the variations among different malicious traffic classes, which results in poor performance and limited robustness. In this paper, we propose intraclass consistency enhanced variational autoencoder with Class-Perception detector(ICE-CP) to identify malicious traffic under concept drift. It comprises two key modules during training:intraclass consistency enhanced(ICE) representation learning and Class-Perception(CP) detector construction. In the first module, we employ a variational autoencoder(VAE) in conjunction with Kullback-Leibler(KL)-divergence and cross-entropy loss to model the distribution of each input malicious traffic flow. This approach simultaneously enhances the generalization, interclass consistency, and intraclass differences in the learned representation. Consequently, we obtain a compact representation and a trained classifier for nondrifting malicious traffic. In the second module, we design the CP detector, which generates a centroid and threshold for each malicious traffic class separately based on the learned representation, depicting the boundaries between drifting and non-drifting malicious traffic. During testing, we utilize the trained classifier to predict malicious traffic classes for the testing samples. Then, we use the CP det

关键词： concept drift cyberspace security intrusion detection malicious traffic identification variational autoencoder

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Double DQN Method For Botnet Traffic Detection System

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Computers, Materials & Continua 2024年第4期79卷 509-530页

作者： Yutao Hu Yuntao Zhao Yongxin Feng Xiangyu Ma school of information Science and Engineering Shenyang Ligong UniversityShenyang110159China graduate school Shenyang Ligong UniversityShenyang110159China

In the face of the increasingly severe Botnet problem on the Internet,how to effectively detect Botnet traffic in realtime has become a critical *** the existing deepQnetwork(DQN)algorithminDeep reinforcement learning can solve the problem of real-time updating,its prediction results are always higher than the actual *** Botnet traffic detection,although it performs well in the training set,the accuracy rate of predicting traffic is as high as%;however,in the test set,its accuracy has declined,and it is impossible to adjust its prediction strategy on time based on new data ***,in the new dataset,its accuracy has declined ***,this paper proposes a Botnet traffic detection system based on double-layer DQN(DDQN).Two Q-values are designed to adjust the model in policy and action,respectively,to achieve real-time model updates and improve the universality and robustness of the model under different data *** show that compared with the DQN model,when using DDQN,the Q-value is not too high,and the detectionmodel has improved the accuracy and precision of Botnet ***,when using Botnet data sets other than the test set,the accuracy and precision of theDDQNmodel are still higher than DQN.

关键词： DQN DDQN deep reinforcement learning botnet detection feature classification

Analysis of phase preservation and interferometric offset test in sparse SAR imaging

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Science China(information sciences) 2024年第2期67卷 219-231页

作者： Zhongqiu XU Bingchen ZHANG Guangzuo LI Xueli ZHAN Yanfei BAO Yirong WU Key Laboratory of Spatial information Processing and Application System Technology Chinese Academy of Sciences Aerospace information Research Institute Chinese Academy of Sciences school of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of Sciences PLA Academy of Military sciences

Sparse synthetic aperture radar(SAR) imaging has emerged as a reliable microwave imaging scheme in the recent decade and excels in down-sampling reconstruction and full-sampling performance improvements such as noise, sidelobe, speckle, and ambiguity suppression. To utilize complex image products of sparse reconstruction for improvement in polarimetric, interferometric, and tomographic SAR imaging, it is necessary to evaluate the phase preservation of sparse SAR imaging. In this study, we first introduce the general alternating direction method of multipliers(ADMM) as the universal framework for sparse reconstruction algorithms and adopt chirp scaling algorithm(CSA)-based azimuth-range decouple operators to avoid expensive data storage and processing. Further, we theoretically analyze the phase preservation of the sparse reconstruction algorithm through a comparison with the reconstruction results of CSA. Finally,we conduct the interferometric offset test on the sparse reconstruction results of simulated and real Gaofen-3(GF-3) SAR data, demonstrating the phase-preserving ability of sparse methods.

关键词： sparse SAR imaging phase preservation interferometric offset test general ADMM azimuth-range decouple

UNIFORM SUPERCONVERGENCE ANALYSIS OF A TWO-GRID MIXED FINITE ELEMENT METHOD FOR THE TIME-DEPENDENT BI-WAVE PROBLEM MODELING D-WAVE SUPERCONDUCTORS

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Journal of Computational Mathematics 2024年第2期42卷 415-431页

作者： Yanmi Wu Dongyang Shi PLA information Engineering University Zhengzhou 450001China school of Mathematics and information sciences Yantai UniversityYantai 264005China

In this paper,a two-grid mixed finite element method(MFEM)of implicit Backward Euler(BE)formula is presented for the fourth order time-dependent singularly perturbed Bi-wave problem for d-wave superconductors by the nonconforming EQ_(1)^(rot) *** this approach,the original nonlinear system is solved on the coarse mesh through the Newton iteration method,and then the linear system is computed on the fine mesh with Taylor’s *** on the high accuracy results of the chosen element,the uniform superclose and superconvergent estimates in the broken H^(1)-norm are derived,which are independent of the negative powers of the perturbation parameter appeared in the considered *** results illustrate that the computing cost of the proposed two-grid method is much less than that of the conventional Galerkin MFEM without loss of accuracy.

关键词： Time-dependent Bi-wave problem Two-grid mixed finite element method Uniform superclose and superconvergent estimates

Robust zero-watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 40-53页

作者： Yiyi Yuan Jingbing Li Jing Liu Uzair Aslam Bhatti Zilong Liu Yen-wei Chen school of information and Communication Engineering Hainan UniversityHaikouChina Research Center for Healthcare Data Science Zhejiang LabHangzhouChina graduate school of information Science and Engineering Ritsumeikan University ShigaJapan

In the intricate network environment,the secure transmission of medical images faces challenges such as information leakage and malicious tampering,significantly impacting the accuracy of disease diagnoses by medical *** address this problem,the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi-stage discrete wavelet transform(DWT),Daisy descriptor,and discrete cosine transform(DCT).The algorithm initially encrypts the original medical image through DWT-DCT and Logistic ***,a 3-stage DWT transformation is applied to the encrypted medical image,with the centre point of the LL3 sub-band within its low-frequency component serving as the sampling *** Daisy descriptor matrix for this point is then ***,a DCT transformation is performed on the Daisy descriptor matrix,and the low-frequency portion is processed using the perceptual hashing algorithm to generate a 32-bit binary feature vector for the medical *** scheme utilises cryptographic knowledge and zero-watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image,which meets the basic re-quirements of medical image *** embedding and extraction of water-marks are accomplished in a mere 0.160 and 0.411s,respectively,with minimal computational *** results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks,with a notable performance in resisting rotation attacks.

关键词： daisy descriptor DCT DWT encryption domain medical image zero-watermarking