SECURE VIDEO CODED SYSTEM MODEL

In this paper, overall system model, shown in Figure (1), of video compression-encryption-transmitter/decompression-dencryption-receiver was designed and implemented. The modified video codec system has used and in addition to compression/decompression, theencryption/decryption video signal by using chaotic neural network (CNN) algorithm was done. Both of quantized vector data and motion vector data have been encrypted by CNN. The compressed and encrypted video data stream has been sent to receiver by using orthogonal frequency division multiplexing (OFDM) modulation technique. The system model was designed according to video signal sample size of 176 × 144 (QCIFstandard format) with rate of 30 frames per second. Overall system model integrates and operates successfully with acceptable performance results

Using Neural Networks to Create and Test Pseudorandom Number Generators

The article presents an overview of modern researches in the field of neural cryptography in relation to pseudorandom number generators (PRNG). Various types of PRNGs and their implementation are provided. We provide the criteria, due to which the PRNG can be considered cryptographically secure (CSPRNG). There are reasons for using certain types of generators. We briefly describe the theory underlying neural networks (NN). We carry the comparison of the NN architectures in the application to the tasks of creating a PRNG and testing output sequences out. Various sets of statistical tests for the analysis of output sequences from PRNG are presented. We consider the results of the most significant articles on the creation of a PRNGs based on the NN. We study articles that based on both classical recurrent networks (Elman, LSTM) and modern generative-adversarial network (GAN). The study of the methods of testing the RNG with the help of NN is implemented. We consider methods of analyzing the output sequences of the RNG and the negative consequences of underestimating the importance of this stage. We describe trends in the neural cryptography, such as the study of numbers that were originally considered random (for example, the number π) and the analysis of the output sequences of quantum random number generators (QRNG) for the presence of patterns

Chaotic Attractor Neural Network-based Public-key Authentication Protocol for RFID

物联网的时代即将来临,国家“十二五“发展规划明确将物联网上升到国家战略高度。作为物联网关键技术的射频识别(rfId,rAdIO frEQuEnCy IdEnTIfICATIOn)系统的安全问题变得越来越重要。通过分析多种rfId认证协议的优缺点,基于神经网络混沌吸引子公钥加密算法提出一种新的rfId认证协议,对该协议的安全性和性能同其他安全协议进行了比较分析,结果表明该协议可以为rfId系统提供更好的安全性和较快的加解密速度,且性能较佳。With the arrival of IoT(the Internet of Things) the security issue,as the critical technology of RFID,becomes increasingly important,and China's 12th Five-Year Development Plan explicitly raises IoT to the national strategic level.This paper proposes a new chaotic attractor neural network-based public-key encryption authentication protocol for RFID security and discusses in detail the protocol security and performance.The analysis results show that the proposed protocol could provide fairly good security and relatively rapid encryption and decryption for the RFID system,and thus is of even better performance.福建省自然科学基金项目(No.A0640005);侨办基金(No.10QZR02);泉州市科技计划(No.2011G6

Hybrid Cryptosystem based on Chaotic Attractors of Neural Networks

把dIffE-HEllMAn密钥交换协议和流密码算法相结合,设计了一种基于神经网络混沌吸引子的混合加密算法。算法采用基于混沌吸引子的dIffE-HEllMAn公钥体制,保证了密钥分发的安全性,同时拥有流密码速度快的优点,提高了加密速度,因此实用性较好,能够满足下一代通信实时快速的需求。分析了算法的安全性和加解密效率,利用VC编程实现算法,并对仿真生成的密钥流和密文进行测试。实验结果表明,算法具有较好的安全性和加解密速度。By combining key-exchange agreement and stream cipher algorithms,a hybrid encryption algorithm based on chaotic attractors of neural networks is designed.This algorithm,with chaotic attractor-based Diffe-Hellman public-key crypto system,could ensure the security of key distribution while maintaining the high encryption speed of the stream cipher,and thus is of fairly good practicability.The security and the encryption efficiency of the new algorithm are analyzed and discussed.The algorithm is implemented by using VC program,and the simulated key stream and cipher text are tested.The experimental results show that the proposed crypto algorithm is of feasibility and fairly high encryption/decryption speed.侨办基金(No.10QZR02);泉州市科技计划(No.2011G6

Encryption of MPEG-2 Video Signal based on Chaotic Neural Network

In this paper, a cipher system based on chaotic neural network (CNN) is used to encrypt and construct a stream cipher of compressed MPEG-2 video signal. The symmetric cipher algorithm transforms the plaintext compressed video data) into the unintelligible form under the control of key; this algorithm has high security and simple architecture with low cost hardware. However, if the size of neural network is increased, the required execution time for CNN encryption and decryption process will be decreased. The whole system model can keep the original file and provide good video quality and reduce the required bit rate which is very suitable to limited bandwidth channel. The proposed system - is also suitable for secure video transmission applications and wireless multimedia communication

神经网络在网络通信中的应用

根据神经网络的原理和特点,说明在网络通信中采用神经网络技术进行应用研究的优势,并根据其特点从三个方面介绍了神经网络在网络通信中的应用,最后分析目前神经网络技术在网络通信中应用研究的现状和发展趋势

Chaotic Neural Network and its Application in Secure Communications

【中文摘要】 具有高度复杂非线性动力学特性的混沌神经网络系统已成为近年来进行加密通信应用研究的热点课题。本文首先概括了混沌神经网络的一些主要理论模型及其非线性动力学系统的特点和复杂性 ,并分析人们如何利用混沌神经网络系统的这些复杂非线性特点 ,如混沌同步和混沌吸引子等 ,进行加密通信的基本算法原理 ,最后总结有关混沌神经网络及其加密通信应用中所需要进一步研究的一些课题 【英文摘要】 The chaotic neural networks with the characteristics of very complicated nonlinear dynamics have been a hot project of application to secure communications in recent years. This paper firstly deals with some main theoretic models, the characteristics and complexities of nonlinear dynamics of them. It analyzes the basic principles which chaotic neural networks are applied to secure communications using the complex property of nonlinear dynamics of them such as chaotic synchronization and chaotic attractors. ...国家自然科学基金 (No.69886002 ,60076015 ); 福建省自然科学基金 (No.A0010019)资

Genetic Algorithm Stream Cipher Key Generation Using NIST Functions

Stream ciphers are beneficial because of their efficiency, speed, and low resource utilization. However, stream ciphers are vulnerable to many attacks if they do not use strong keys for encryption and decryption. Thus, one way to increase the security of stream ciphers is to improve the key generation algorithm. This study sought to evaluate the keys produced by a genetic algorithm stream cipher when individual and combinations of fitness functions are used. Furthermore, this study identified which fitness function is the best for a specific scenario. For the genetic algorithm, the fitness tests are thirteen of the tests defined in NIST SP 800-22rla. The thirteen different fitness functions were inputted into the genetic algorithm stream cipher one at a time. Next, 50 total keys of varying bit sizes were generated. These keys were evaluated by using the Hamming distance between the keys and time that it took for key generation. After each individual fitness function was evaluated, two combinations of five tests were created and used as a single fitness function. The two combinations were the best performing NIST functions for Hamming distance and time for 256-bit keys. Sensitivity analysis was then performed to find the best possible combination of the NIST functions. Based on the results, using different individual functions or a combination of functions as a fitness functions changed the Hamming distance between the keys and the time that it takes to generate a key. Furthermore, using sensitivity analysis results for the top two, three, four, and five combinations for Hamming distance and time, prediction equations were created and used to predict values for other combinations and key sizes

A new symmetric probabilistic encryption scheme based on chaotic attractors of neural networks

A new probabilistic symmetric-key encryption scheme based on chaotic-classified properties of Hopfield neural networks is described. In an overstoraged Hopfield Neural Network (OHNN) the phenomenon of chaotic-attractors is well documented and messages in the attraction domain of an attractor are unpredictably related to each other. By performing permutation operations on the neural synaptic matrix, several interesting chaotic-classified properties of OHNN were found and these were exploited in developing a new cryptography technique. By keeping the permutation operation of the neural synaptic matrix as the secret key, we introduce a new probabilistic encryption scheme for a symmetric-key cryptosystem. Security and encryption efficiency of the new scheme are discussed