基于RF-RNN模型的DNS隐蔽信道检测方法

为提高检测隐蔽信道的灵敏度,提出一种基于随机森林(Random Forest,RF)和循环神经网络(Recurrent Neural Network,RNN)的域名系统(Domain Name System,DNS)隐蔽信道检测方法。该方法采用域名检测作为主要手段,使用RF模型对域名进行分类,通过深度学习方法挖掘更高阶的特征表示。实验结果表明,与单一模型相比,该方法在检测准确性和健壮性方面均取得了显著提升。

一种基于DNS的零信任增强认证系统设计

针对当前大量HTTPS应用复用证书存在安全风险问题,借鉴了零信任模型中安全策略动态授权的思路,提出了一种基于现有互联网基础设施DNS来扩展增强认证功能的方案,通过在现有DNS权威服务器上额外配置增强的认证信息来对HTTPS访问请求进行动态认证,从而能实时验证当前HTTPS证书的安全状态。该方案通过可信易得的DNS基础设施解决了当前普遍存在的HTTPS证书复用带来的安全问题,是一种灵活高效并且可扩展的零信任安全增强认证架构。

DNS信道传输加密技术:现状、趋势和挑战

DNS作为重要的互联网基础设施,其明文传输的特点带来很多隐私安全风险.DoH、DoT、DoQ等DNS信道传输加密技术致力于防止DNS数据被泄露或篡改,并保证DNS消息来源的可靠性.首先从DNS消息格式、数据存储和管理、系统架构和部署等6个方面分析明文DNS存在的隐私安全问题,并对已有的相关技术和协议进行总结.其次分析DNS信道传输加密技术的实现原理及应用现状,进而基于多角度评测指标对各加密协议在不同网络条件下的性能表现进行讨论.同时通过填充机制的局限性、加密流量识别和基于指纹的加密活动分析等方向探讨DNS信道传输加密技术的隐私保护效果.此外从部署规范、恶意流量对加密技术的利用和攻击、隐私和网络安全管理之间的矛盾,以及加密后影响隐私安全的其他因素等方面总结DNS信道传输加密技术存在的问题、挑战和相关解决方案.最后总结加密DNS服务的发现、递归解析器到权威服务器之间的加密、服务器端的隐私保护、基于HTTP/3的DNS等后续需要着重关注的研究方向.

DNS核心稳定训练结合局部针刺运动对脑卒中后偏瘫患者步行及姿势控制能力的影响

目的:探讨动态神经肌肉稳定技术(Dynamic Neuromuscular Stabilization,DNS)下的核心稳定训练结合局部针刺运动方案对脑卒中后偏瘫患者步行及姿势控制能力的影响。方法:选取2022年3月~2022年9月在郴州市第一人民医院就诊的脑卒中患者90例,按随机数字表分为对照组(n=45)和观察组(n=45),对照组采用常规康复训练,观察组在此基础上增加DNS核心稳定训练结合局部针刺运动疗法。治疗8周后,对比并分析治疗前后两组患者步态时空参数中步长、步速、步频参数;步态时相参数中双支撑相占步态周期百分比、患侧单支撑相占步态周期百分比、步行周期参数;脑卒中患者姿势控制量表(Posture assessment of stoke scale,PASS)量表、Tinnetti(Performance-Oriented Assessment of Mobility)量表及Fugl-Meyer下肢运动功能(FMA-LE)量表评分数据。结果:治疗后,两组步长、步速、步频参数、双支撑相占步态周期百分比、患侧单支撑相占步态周期百分比、Tinnetti评分、PASS评分及FMA评分均显著高于治疗前(P<0.05),步行周期显著低于治疗前(P<0.05),且观察组治疗后步长、步速、步频参数、双支撑相占步态周期百分比、患侧单支撑相占步态周期百分比、PASS评分、Tinnetti评分及FMALE评分均显著高于对照组(P<0.05),步行周期则低于对照组(P<0.05)。结论:DNS核心稳定训练结合局部针刺运动可提高脑卒中后偏瘫患者核心稳定及姿势控制能力,改善步行功能。

Estimation-free spatial-domain image reconstruction of structured illumination microscopy

Structured illumination microscopy(SIM)achieves super-resolution(SR)by modulating the high-frequency information of the sample into the passband of the optical system and subsequent image reconstruction.The traditional Wiener-filtering-based reconstruction algorithm operates in the Fourier domain,it requires prior knowledge of the sinusoidal illumination patterns which makes the time-consuming procedure of parameter estimation to raw datasets necessary,besides,the parameter estimation is sensitive to noise or aberration-induced pattern distortion which leads to reconstruction artifacts.Here,we propose a spatial-domain image reconstruction method that does not require parameter estimation but calculates patterns from raw datasets,and a reconstructed image can be obtained just by calculating the spatial covariance of differential calculated patterns and differential filtered datasets(the notch filtering operation is performed to the raw datasets for attenuating and compensating the optical transfer function(OTF)).Experiments on reconstructing raw datasets including nonbiological,biological,and simulated samples demonstrate that our method has SR capability,high reconstruction speed,and high robustness to aberration and noise.

Delineating homogeneous domains of fractured rocks using topological manifolds and deep learning

Determining homogeneous domains statistically is helpful for engineering geological modeling and rock mass stability evaluation.In this text,a technique that can integrate lithology,geotechnical and structural information is proposed to delineate homogeneous domains.This technique is then applied to a high and steep slope along a road.First,geological and geotechnical domains were described based on lithology,faults,and shear zones.Next,topological manifolds were used to eliminate the incompatibility between orientations and other parameters(i.e.trace length and roughness)so that the data concerning various properties of each discontinuity can be matched and characterized in the same Euclidean space.Thus,the influence of implicit combined effect in between parameter sequences on the homogeneous domains could be considered.Deep learning technique was employed to quantify abstract features of the characterization images of discontinuity properties,and to assess the similarity of rock mass structures.The results show that the technique can effectively distinguish structural variations and outperform conventional methods.It can handle multisource engineering geological information and multiple discontinuity parameters.This technique can also minimize the interference of human factors and delineate homogeneous domains based on orientations or multi-parameter with arbitrary distributions to satisfy different engineering requirements.

User Station Security Protection Method Based on Random Domain Name Detection and Active Defense

The power monitoring system is the most important production management system in the power industry. As an important part of the power monitoring system, the user station that lacks grid binding will become an important target of network attacks. In order to perceive the network attack events on the user station side in time, a method combining real-time detection and active defense of random domain names on the user station side was proposed. Capsule network (CapsNet) combined with long short-term memory network (LSTM) was used to classify the domain names extracted from the traffic data. When a random domain name is detected, it sent instructions to routers and switched to update their security policies through the remote terminal protocol (Telnet), or shut down the service interfaces of routers and switched to block network attacks. The experimental results showed that the use of CapsNet combined with LSTM classification algorithm can achieve 99.16% accuracy and 98% recall rate in random domain name detection. Through the Telnet protocol, routers and switches can be linked to make active defense without interrupting services.

基于AWX的DNS自动化运维管理探究

为提高企业IT系统的效率、可靠性及安全性,基于AWX的DNS自动化运维管理,研究AWX作为自动化工具在DNS管理中的应用,利用其功能简化DNS配置及部署,为IT基础设施的发展及创新提供参考。

Consistency between domain wall oscillation modes and spin wave modes in nanostrips

Investigations on domain wall(DW) and spin wave(SW) modes in a series of nanostrips with different widths and thicknesses have been carried out using micromagnetic simulation. The simulation results show that the frequencies of SW modes and the corresponding DW modes are consistent with each other if they have the same node number along the width direction. This consistency is more pronounced in wide and thin nanostrips, favoring the DW motion driven by SWs.Further analysis of the moving behavior of a DW driven by SWs is also carried out. The average DW speed can reach a larger value of ~ 140 m/s under two different SW sources. We argue that this study is very meaningful for the potential application of DW motion driven by SWs.

Low-Rank Optimal Transport for Robust Domain Adaptation

When encountering the distribution shift between the source(training) and target(test) domains, domain adaptation attempts to adjust the classifiers to be capable of dealing with different domains. Previous domain adaptation research has achieved a lot of success both in theory and practice under the assumption that all the examples in the source domain are welllabeled and of high quality. However, the methods consistently lose robustness in noisy settings where data from the source domain have corrupted labels or features which is common in reality. Therefore, robust domain adaptation has been introduced to deal with such problems. In this paper, we attempt to solve two interrelated problems with robust domain adaptation:distribution shift across domains and sample noises of the source domain. To disentangle these challenges, an optimal transport approach with low-rank constraints is applied to guide the domain adaptation model training process to avoid noisy information influence. For the domain shift problem, the optimal transport mechanism can learn the joint data representations between the source and target domains using a measurement of discrepancy and preserve the discriminative information. The rank constraint on the transport matrix can help recover the corrupted subspace structures and eliminate the noise to some extent when dealing with corrupted source data. The solution to this relaxed and regularized optimal transport framework is a convex optimization problem that can be solved using the Augmented Lagrange Multiplier method, whose convergence can be mathematically proved. The effectiveness of the proposed method is evaluated through extensive experiments on both synthetic and real-world datasets.

基于无监督学习的DNS隧道检测

文章利用局域网中DNS服务器的历史流量数据通过无监督训练生成DNS隧道检测模型,计算待检测流量数据所对应的特征向量与DNS隧道检测模型提供的正常流量矩阵之间的马氏距离,并基于马氏距离来进行DNS隧道异常识别。根据局域网中内部DNS服务器的历史流量数据进行DNS隧道检测模型的训练,从而使得训练后的DNS隧道检测模型适应于不同的网络环境,提高了对DNS隧道异常的识别精度,保证了DNS隧道检测的准确性。

Multiple Matching Attenuation Based on Curvelet Domain Extended Filtering

The paper develops a multiple matching attenuation method based on extended filtering in the curvelet domain,which combines the traditional Wiener filtering method with the matching attenuation method in curvelet domain.Firstly,the method uses the predicted multiple data to generate the Hilbert transform records,time derivative records and time derivative records of Hilbert transform.Then,the above records are transformed into the curvelet domain and multiple matching attenuation based on least squares extended filtering is performed.Finally,the attenuation results are transformed back into the time-space domain.Tests on the model data and field data show that the method proposed in the paper effectively suppress the multiples while preserving the primaries well.Furthermore,it has higher accuracy in eliminating multiple reflections,which is more suitable for the multiple attenuation tasks in the areas with complex structures compared to the time-space domain extended filtering method and the conventional curvelet transform method.

BIG HANKEL OPERATORS ON HARDY SPACES OF STRONGLY PSEUDOCONVEX DOMAINS

In this article,we investigate the(big) Hankel operator H_(f) on the Hardy spaces of bounded strongly pseudoconvex domains Ω in C^(n).We observe that H_(f ) is bounded on H~p(Ω)(1 <p <∞) if f belongs to BMO and we obtain some characterizations for Hf on H^(2)(Ω) of other pseudoconvex domains.In these arguments,Amar's L^(p)-estimations and Berndtsson's L^(2)-estimations for solutions of the ■_(b)-equation play a crucial role.In addition,we solve Gleason's problem for Hardy spaces H^(p)(Ω)(1 ≤p≤∞) of bounded strongly pseudoconvex domains.

Diabetic retinopathy identification based on multi-sourcefree domain adaptation

AIM:To address the challenges of data labeling difficulties,data privacy,and necessary large amount of labeled data for deep learning methods in diabetic retinopathy(DR)identification,the aim of this study is to develop a source-free domain adaptation(SFDA)method for efficient and effective DR identification from unlabeled data.METHODS:A multi-SFDA method was proposed for DR identification.This method integrates multiple source models,which are trained from the same source domain,to generate synthetic pseudo labels for the unlabeled target domain.Besides,a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances.Validation is performed using three color fundus photograph datasets(APTOS2019,DDR,and EyePACS).RESULTS:The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks.It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains.CONCLUSION:The multi-SFDA method provides an effective approach to overcome the challenges in DR identification.The method not only addresses difficulties in data labeling and privacy issues,but also reduces the need for large amounts of labeled data required by deep learning methods,making it a practical tool for early detection and preservation of vision in diabetic patients.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

ON THE SOBOLEV DOLBEAULT COHOMOLOGY OF A DOMAIN WITH PSEUDOCONCAVE BOUNDARIES

In this note,we mainly make use of a method devised by Shaw[15]for studying Sobolev Dolbeault cohomologies of a pseudoconcave domain of the type Ω=Ω\∪_(j=1^(m))Ω_(j),where Ω and {Ω_(j)}_(j=1^(m)■Ω are bounded pseudoconvex domains in ℂ^(n) with smooth boundaries,and Ω_(1),…,Ω_(m) are mutually disjoint.The main results can also be quickly obtained by virtue of[5].

MINIMIZERS OF L^(2)-SUBCRITICAL VARIATIONAL PROBLEMS WITH SPATIALLY DECAYING NONLINEARITIES IN BOUNDED DOMAINS

This paper is concerned with the minimizers of L^(2)-subcritical constraint variar tional problems with spatially decaying nonlinearities in a bounded domain Ω of R~N(N≥1).We prove that the problem admits minimizers for any M> 0.Moreover,the limiting behavior of minimizers as M→∞ is also analyzed rigorously.

A Time-Domain Numerical Simulation for Free Motion Responses of Two Ships Advancing in Head Waves

The constant panel method within the framework of potential flow theory in the time domain is developed for solving the hydrodynamic interactions between two parallel ships with forward speed.When solving problems within a time domain framework,the free water surface needs to simultaneously satisfy both the kinematic and dynamic boundary conditions of the free water surface.This provides conditions for adding artificial damping layers.Using the Runge−Kutta method to solve equations related to time.An upwind differential scheme is used in the present method to deal with the convection terms on the free surface to prevent waves upstream.Through the comparison with the available experimental data and other numerical methods,the present method is proved to have good mesh convergence,and satisfactory results can be obtained.The constant panel method is applied to calculate the hydrodynamic interaction responses of two parallel ships advancing in head waves.Numerical simulations are conducted on the effects of forward speed,different longitudinal and lateral distances on the motion response of two modified Wigley ships in head waves.Then further investigations are conducted on the effects of different ship types on the motion response.

Bearing Fault Diagnosis Based on Deep Discriminative Adversarial Domain Adaptation Neural Networks

Intelligent diagnosis driven by big data for mechanical fault is an important means to ensure the safe operation ofequipment. In these methods, deep learning-based machinery fault diagnosis approaches have received increasingattention and achieved some results. It might lead to insufficient performance for using transfer learning alone andcause misclassification of target samples for domain bias when building deep models to learn domain-invariantfeatures. To address the above problems, a deep discriminative adversarial domain adaptation neural networkfor the bearing fault diagnosis model is proposed (DDADAN). In this method, the raw vibration data are firstlyconverted into frequency domain data by Fast Fourier Transform, and an improved deep convolutional neuralnetwork with wide first-layer kernels is used as a feature extractor to extract deep fault features. Then, domaininvariant features are learned from the fault data with correlation alignment-based domain adversarial training.Furthermore, to enhance the discriminative property of features, discriminative feature learning is embeddedinto this network to make the features compact, as well as separable between classes within the class. Finally, theperformance and anti-noise capability of the proposedmethod are evaluated using two sets of bearing fault datasets.The results demonstrate that the proposed method is capable of handling domain offset caused by differentworkingconditions and maintaining more than 97.53% accuracy on various transfer tasks. Furthermore, the proposedmethod can achieve high diagnostic accuracy under varying noise levels.

Enhancing Relational Triple Extraction in Specific Domains:Semantic Enhancement and Synergy of Large Language Models and Small Pre-Trained Language Models

In the process of constructing domain-specific knowledge graphs,the task of relational triple extraction plays a critical role in transforming unstructured text into structured information.Existing relational triple extraction models facemultiple challenges when processing domain-specific data,including insufficient utilization of semantic interaction information between entities and relations,difficulties in handling challenging samples,and the scarcity of domain-specific datasets.To address these issues,our study introduces three innovative components:Relation semantic enhancement,data augmentation,and a voting strategy,all designed to significantly improve the model’s performance in tackling domain-specific relational triple extraction tasks.We first propose an innovative attention interaction module.This method significantly enhances the semantic interaction capabilities between entities and relations by integrating semantic information fromrelation labels.Second,we propose a voting strategy that effectively combines the strengths of large languagemodels(LLMs)and fine-tuned small pre-trained language models(SLMs)to reevaluate challenging samples,thereby improving the model’s adaptability in specific domains.Additionally,we explore the use of LLMs for data augmentation,aiming to generate domain-specific datasets to alleviate the scarcity of domain data.Experiments conducted on three domain-specific datasets demonstrate that our model outperforms existing comparative models in several aspects,with F1 scores exceeding the State of the Art models by 2%,1.6%,and 0.6%,respectively,validating the effectiveness and generalizability of our approach.