首页> 外文会议>Communication Systems and Network Technologies (CSNT), 2012 International Conference on >Performace Analysis of Encryption and Decryption Using Genetic Based Cancelable Non-invertible Fingerprint Based Key in MANET
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Performace Analysis of Encryption and Decryption Using Genetic Based Cancelable Non-invertible Fingerprint Based Key in MANET

机译:MANET中基于遗传可取消不可逆指纹的密钥加解密性能分析

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摘要

The rapid evolution of mobile devices and mobile ad hoc networks (MANET) requires the protecting data while transmitting in the network. To implement hard-cryptographic algorithm is challenging prospect in MANET. Biometric characteristic such as face, fingerprint, voice, iris, and retina can be potential alternative to generate a cryptographic key over a conventional cryptography and enhance the security. In this paper non-invertible key generation using cancelable fingerprint to provide security, authentication code and computation requirement is discussed. This proposed method has three advantages over previous system. First, authentication code is generated to provide authentication and integrity. Second, to provide security data is encrypted using non-invertible key generated from the fingerprint biometric of the receiver. Third, randomization is provided using genetic operator to protect against various attacks. The proposed model is analyzed against security attributes authentication, confidentiality, integrity. It is found that it withstands against cryptanalytic and brute force attack. We also tested computational cost of encryption time and decryption time required using generated key of different message size using Java standard edition and iPhone simulator. It is found that mobile devices take pretty less time so it suits the current mobile environments.
机译:移动设备和移动自组织网络(MANET)的快速发展要求在网络中传输时保护数据。在MANET中实现硬密码算法是具有挑战性的前景。诸如面部,指纹,语音,虹膜和视网膜之类的生物特征可能是在传统密码学上生成密码密钥并增强安全性的潜在替代方法。本文讨论了使用可取消指纹提供安全性,验证码和计算要求的不可逆密钥生成。与以前的系统相比,该方法具有三个优点。首先,生成身份验证代码以提供身份验证和完整性。第二,提供安全性数据是使用从接收者的指纹生物特征生成的不可逆密钥进行加密的。第三,使用遗传算子提供随机化以防止各种攻击。针对安全属性认证,机密性,完整性对所提出的模型进行了分析。发现它可以抵抗密码分析和蛮力攻击。我们还使用Java标准版和iPhone模拟器测试了使用不同消息大小的生成密钥生成所需的加密时间和解密时间的计算成本。发现移动设备花费的时间少得多,因此适合当前的移动环境。

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