Coexistence behavior of asymmetric attractors in hyperbolic-type memristive Hopfield neural network and its application in image encryption

The neuron model has been widely employed in neural-morphic computing systems and chaotic circuits.This study aims to develop a novel circuit simulation of a three-neuron Hopfield neural network(HNN)with coupled hyperbolic memristors through the modification of a single coupling connection weight.The bistable mode of the hyperbolic memristive HNN(mHNN),characterized by the coexistence of asymmetric chaos and periodic attractors,is effectively demonstrated through the utilization of conventional nonlinear analysis techniques.These techniques include bifurcation diagrams,two-parameter maximum Lyapunov exponent plots,local attractor basins,and phase trajectory diagrams.Moreover,an encryption technique for color images is devised by leveraging the mHNN model and asymmetric structural attractors.This method demonstrates significant benefits in correlation,information entropy,and resistance to differential attacks,providing strong evidence for its effectiveness in encryption.Additionally,an improved modular circuit design method is employed to create the analog equivalent circuit of the memristive HNN.The correctness of the circuit design is confirmed through Multisim simulations,which align with numerical simulations conducted in Matlab.

HGNN-ETC: Higher-Order Graph Neural Network Based on Chronological Relationships for Encrypted Traffic Classification

Encrypted traffic plays a crucial role in safeguarding network security and user privacy.However,encrypting malicious traffic can lead to numerous security issues,making the effective classification of encrypted traffic essential.Existing methods for detecting encrypted traffic face two significant challenges.First,relying solely on the original byte information for classification fails to leverage the rich temporal relationships within network traffic.Second,machine learning and convolutional neural network methods lack sufficient network expression capabilities,hindering the full exploration of traffic’s potential characteristics.To address these limitations,this study introduces a traffic classification method that utilizes time relationships and a higher-order graph neural network,termed HGNN-ETC.This approach fully exploits the original byte information and chronological relationships of traffic packets,transforming traffic data into a graph structure to provide the model with more comprehensive context information.HGNN-ETC employs an innovative k-dimensional graph neural network to effectively capture the multi-scale structural features of traffic graphs,enabling more accurate classification.We select the ISCXVPN and the USTC-TK2016 dataset for our experiments.The results show that compared with other state-of-the-art methods,our method can obtain a better classification effect on different datasets,and the accuracy rate is about 97.00%.In addition,by analyzing the impact of varying input specifications on classification performance,we determine the optimal network data truncation strategy and confirm the model’s excellent generalization ability on different datasets.

An Image Encryption Algorithm Based on BP Neural Network and Hyperchaotic System

To reduce the bandwidth and storage resources of image information in communication transmission, and improve the secure communication of information. In this paper, an image compression and encryption algorithm based on fractional-order memristive hyperchaotic system and BP neural network is proposed. In this algorithm, the image pixel values are compressed by BP neural network, the chaotic sequences of the fractional-order memristive hyperchaotic system are used to diffuse the pixel values. The experimental simulation results indicate that the proposed algorithm not only can effectively compress and encrypt image, but also have better security features. Therefore, this work provides theoretical guidance and experimental basis for the safe transmission and storage of image information in practical communication.

FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

CONSTRUCTION OF THE ENCRYPTION MATRIX BASED ON CHEBYSHEV CHAOTIC NEURAL NETWORKS

The paper proposes a novel algorithm to get the encryption matrix. Firstly, a chaotic sequence generated by Chebyshev chaotic neural networks is converted into a series of low-order integer matrices from which available encryption matrices are selected. Then, a higher order encryption matrix relating real world application is constructed by means of tensor production method based on selected encryption matrices. The results show that the proposed algorithm can produce a "one-time pad cipher" encryption matrix with high security; and the encryption results have good chaos and auto-correlation with the natural frequency of the plaintext being hidden and homogenized.

Combo Packet:An Encryption Traffic Classification Method Based on Contextual Information

With the increasing proportion of encrypted traffic in cyberspace, the classification of encrypted traffic has becomea core key technology in network supervision. In recent years, many different solutions have emerged in this field.Most methods identify and classify traffic by extracting spatiotemporal characteristics of data flows or byte-levelfeatures of packets. However, due to changes in data transmission mediums, such as fiber optics and satellites,temporal features can exhibit significant variations due to changes in communication links and transmissionquality. Additionally, partial spatial features can change due to reasons like data reordering and retransmission.Faced with these challenges, identifying encrypted traffic solely based on packet byte-level features is significantlydifficult. To address this, we propose a universal packet-level encrypted traffic identification method, ComboPacket. This method utilizes convolutional neural networks to extract deep features of the current packet andits contextual information and employs spatial and channel attention mechanisms to select and locate effectivefeatures. Experimental data shows that Combo Packet can effectively distinguish between encrypted traffic servicecategories (e.g., File Transfer Protocol, FTP, and Peer-to-Peer, P2P) and encrypted traffic application categories (e.g.,BitTorrent and Skype). Validated on the ISCX VPN-non VPN dataset, it achieves classification accuracies of 97.0%and 97.1% for service and application categories, respectively. It also provides shorter training times and higherrecognition speeds. The performance and recognition capabilities of Combo Packet are significantly superior tothe existing classification methods mentioned.

Verifiable Privacy-Preserving Neural Network on Encrypted Data

The widespread acceptance of machine learning,particularly of neural networks leads to great success in many areas,such as recommender systems,medical predictions,and recognition.It is becoming possible for any individual with a personal electronic device and Internet access to complete complex machine learning tasks using cloud servers.However,it must be taken into consideration that the data from clients may be exposed to cloud servers.Recent work to preserve data confidentiality has allowed for the outsourcing of services using homomorphic encryption schemes.But these architectures are based on honest but curious cloud servers,which are unable to tell whether cloud servers have completed the computation delegated to the cloud server.This paper proposes a verifiable neural network framework which focuses on solving the problem of data confidentiality and training integrity in machine learning.Specifically,we first leverage homomorphic encryption and extended diagonal packing method to realize a privacy-preserving neural network model efficiently,it enables the user training over encrypted data,thereby protecting the user’s private data.Then,considering the problem that malicious cloud servers are likely to return a wrong result for saving cost,we also integrate a training validation modular Proof-of-Learning,a strategy for verifying the correctness of computations performed during training.Moreover,we introduce practical byzantine fault tolerance to complete the verification progress without a verifiable center.Finally,we conduct a series of experiments to evaluate the performance of the proposed framework,the results show that our construction supports the verifiable training of PPNN based on HE without introducing much computational cost.

Classification Method of Encrypted Traffic Based on Deep Neural Network

With the widespread use of network traffic encryption technology, the traditional traffic classification method has gradually become invalid, which increases the difficulty of network management and poses a serious threat to network security. This paper analyzes the traffic encrypted and transmitted by VPN and explores its classification method. By extracting the timing characteristics of the encrypted traffic, the classification model of the deep neural network was used to classify the traffic of seven different categories in the encrypted traffic, and compared with the commonly used naive Bayesian classification algorithm. At the same time, the batch size that affects the training of deep neural network models was studied. Experiments show that the classification ability of encrypted traffic classification model based on deep neural network is much better than the naive Bayesian method. During training, the batch size has different effects on the deep neural network model. When the batch size is 40, the deep neural network model has the best classification ability.

A novel image block cryptosystem based on a spatiotemporal chaotic system and a chaotic neural network

In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network （CNN）. The employed CNN comprises a 4-neuron layer called a chaotic neuron layer （CNL）, where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice （CML）, which can be easily implemented in parallel by hard- ware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosys- tem is secure and practical, and suitable for image encryption.

Neural Cryptography with Fog Computing Network for Health Monitoring Using IoMT

Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

Secured Health Data Transmission Using Lagrange Interpolation and Artificial Neural Network

In recent decades,the cloud computing contributes a prominent role in health care sector as the patient health records are transferred and collected using cloud computing services.The doctors have switched to cloud computing as it provides multiple advantageous measures including wide storage space and easy availability without any limitations.This necessitates the medical field to be redesigned by cloud technology to preserve information about patient’s critical diseases,electrocardiogram(ECG)reports,and payment details.The proposed work utilizes a hybrid cloud pattern to share Massachusetts Institute of Technology-Beth Israel Hospital(MIT-BIH)resources over the private and public cloud.The stored data are categorized as significant and non-significant by Artificial Neural Networks(ANN).The significant data undergoes encryption by Lagrange key management which automatically generates the key and stores it in the hidden layer.Upon receiving the request from a secondary user,the primary user verifies the authentication of the request and transmits the key via Gmail to the secondary user.Once the key matches the key in the hidden layer,the preserved information will be shared between the users.Due to the enhanced privacy preserving key generation,the proposed work prevents the tracking of keys by malicious users.The outcomes reveal that the introduced work provides improved success rate with reduced computational time.

A Secure IoT-Cloud Based Healthcare System for Disease Classification Using Neural Network

The integration of the Internet of Things(IoT)and cloud computing is the most popular growing technology in the IT world.IoT integrated cloud com-puting technology can be used in smart cities,health care,smart homes,environ-mental monitoring,etc.In recent days,IoT integrated cloud can be used in the health care system for remote patient care,emergency care,disease prediction,pharmacy management,etc.but,still,security of patient data and disease predic-tion accuracy is a major concern.Numerous machine learning approaches were used for effective early disease prediction.However,machine learning takes more time and less performance while classification.In this research work,the Attribute based Searchable Honey Encryption with Functional Neural Network(ABSHE-FNN)framework is proposed to analyze the disease and provide stronger security in IoT-cloud healthcare data.In this work,the Cardiovascular Disease and Pima Indians diabetes dataset are used for heart and diabetic disease classification.Initi-ally,means-mode normalization removes the noise and normalizes the IoT data,which helps to enhance the quality of data.Rectified Linear Unit(RLU)was applied to adjust the feature weight to reduce the training cost and error classifi-cation.This proposed ABSHE-FNN technique provides better security and achieves 92.79%disease classification accuracy compared to existing techniques.

Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks

As an optical processor,a diffractive deep neural network(D2NN)utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing,completing its tasks at the speed of light propagation through thin optical layers.With sufficient degrees of freedom,D2NNs can perform arbitrary complex-valued linear transformations using spatially coherent light.Similarly,D2NNs can also perform arbitrary linear intensity transformations with spatially incoherent illumination;however,under spatially incoherent light,these transformations are nonnegative,acting on diffraction-limited optical intensity patterns at the input field of view.Here,we expand the use of spatially incoherent D2NNs to complex-valued information processing for executing arbitrary complex-valued linear transformations using spatially incoherent light.Through simulations,we show that as the number of optimized diffractive features increases beyond a threshold dictated by the multiplication of the input and output space-bandwidth products,a spatially incoherent diffractive visual processor can approximate any complex-valued linear transformation and be used for all-optical image encryption using incoherent illumination.The findings are important for the all-optical processing of information under natural light using various forms of diffractive surface-based optical processors.

A novel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNA sequence operations

A novel color image encryption algorithm based on dynamic deoxyribonucleic acid(DNA)encoding and chaos is presented.A three-neuron fractional-order discrete Hopfield neural network(FODHNN)is employed as a pseudo-random chaotic sequence generator.Its initial value is obtained with the secret key generated by a fiveparameter external key and a hash code of the plain image.The external key includes both the FODHNN discrete step size and order.The hash is computed with the SHA-2 function.This ensures a large secret key space and improves the algorithm sensitivity to the plain image.Furthermore,a new three-dimensional projection confusion method is proposed to scramble the pixels among red,green,and blue color components.DNA encoding and diffusion are used to diffuse the image information.Pseudo-random sequences generated by FODHNN are employed to determine the encoding rules for each pixel and to ensure the diversity of the encoding methods.Finally,confusion II and XOR are used to ensure the security of the encryption.Experimental results and the security analysis show that the proposed algorithm has better performance than those reported in the literature and can resist typical attacks.

双分支多阶段时空特征融合的加密流量分类方法

针对当前加密流量识别分类研究对时空特征提取不充分以及网络架构引起特征信息损失的情况,该文提出了一种基于双分支多阶段时空特征融合(DBMS-SFF)的加密流量识别分类方法.本方法聚焦于加密流量的“字节-数据包-会话流”层次结构特点,设计两个并行的网络分支进行特征提取,分支一采用门控循环神经网络(GRU)及其变种BiGRU分别提取数据包内部相邻字节、相邻数据包之间的时序特征,并在此基础上利用多头注意力机制赋予关键特征更大的权重.分支二使用异于常规感受野的方式,利用不规则大小卷积核组成的多尺度卷积神经网络(CNN)作用于流的“字节-数据包”两个阶段对空间信息进行表征.在公开数据集上ISCXVPN-nonVPN2016实验表明,本方法的模型总体准确率为97.6%,平均F1得分值97.5%,均显著高于对比的模型.

基于并联融合模型的加密流量分类方法

网络流量加密的广泛应用给加密流量快速准确分类带来了新的挑战。针对该问题,提出了一种并联融合卷积神经网络与循环神经网络的分类方法,卷积神经网络与循环神经网络分别提取加密流量的空间特征与时序特征,根据时空特征对加密流量进行分类,实验表明,该文所提出模型相较于现有的单一模型和串联融合模型在识别准确率上分别提高了14.07%和2.79%,训练效率下降了10%以内,该文所提出模型性能优于现有模型。

一种多特征融合的加密流量快速分类方法

网络流量识别是网络管理和安全服务的基础.随着互联网的不断扩展及其复杂性的增加,传统基于规则的识别方法或流行为特征的方法正在面临着巨大挑战.受自然语言处理(Nature Language Processing, NLP)启发,本文提出了一种多特征融合的加密流量快速分类方法 .该方法通过融合数据包和字节序列特征来完成网络流的特征表示,采用双元字节编码将所选特征扩展为双字节序列,增加了字节的上下文语义特征;通过与数据包特征处理相适应的池化方法来最大限度保留数据包的特征信息,从而使所提模型具有更强的抗噪能力和更精确的分类能力.本文方法分别在ISCX-2016和一个包含66个热门应用程序的私有数据集(ETD66)上进行验证,并与其他模型展开比较.结果表明:本文所提方法在ISCX-2016及ETD66上的测试精度和性能都明显优于其他流量分类模型,分别取得了98.2%和98.6%的识别准确率,从而证明了所提方法的特征提取能力和强泛化能力.

基于函数加密的密文卷积神经网络模型

目前,多数的外包卷积神经网络(CNN)模型采用同态加密、安全多方计算等方法来保护敏感数据的隐私性。然而,上述方法存在计算与通信开销过大而引起的系统效率较低的问题。利用函数加密的低开销特点,构建了基于函数加密的密文卷积神经网络模型。首先,设计了内积函数加密算法和基本运算函数加密算法,实现了密文数据的内积、乘法、减法等基本运算,降低了计算与通信开销;然后,设计了针对基本运算的安全卷积计算协议和安全损失优化协议,实现了卷积层的密文前向传播和输出层的密文反向传播;最后,给出了模型的安全训练和分类方法,通过将以上安全协议进行模块化顺序组合的方式实现CNN对密文数据的训练和分类,该方法可以同时保护用户数据和标签的机密性。理论分析和实验结果表明,所提模型能够在保证正确性和安全性的前提下实现密文数据的训练和分类。

同态加密在深度学习中的应用综述

随着深度学习在各种领域中的广泛应用,数据隐私和安全性问题变得日益重要。同态加密作为一种能够在加密数据上直接进行计算的加密技术,为解决这一问题提供了可能的解决方案。综述了深度学习与同态加密结合的方法,探讨了如何在加密环境中有效应用深度学习模型。介绍了同态加密技术的基础知识,涵盖了其基本原理、不同分类(部分同态加密、有限同态加密、全同态加密)以及全同态加密的发展历程。详细介绍了深度学习中的关键模型,包括卷积神经网络和Transformer模型。探讨了同态加密与深度学习结合的步骤以及如何将深度学习的各个层(如卷积层、注意力层、激活函数层)适配于同态加密环境。重点综述了现有的将卷积神经网络和Transformer与同态加密结合的具体方法,探讨了在加密数据上进行深度学习计算的实现方案以及为了提升效率和精度而采用的性能优化策略,并总结了每种方法的优势和局限性。总结了当前研究的进展,并对未来的研究方向进行了展望。