Enhancing Continuous Chaos Communication Using Machine Learning in Resource-Limited Devices
Enhancing Continuous Chaos Communication Using Machine Learning in Resource-Limited Devices
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DOI：
10.1109/dcas57389.2023.10130267
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
JinHa Hwang;Nima Hosseinzadeh;A. Hedayatipour
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文献类型：
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作者：
JinHa Hwang;Nima Hosseinzadeh;A. Hedayatipour
机器学习正在迅速找到解决日常复杂问题的方法。一个这样的应用是在混沌加密领域，其中机器学习技术可以用于提高加密算法的安全性和同步性。混沌加密是一种使用混沌理论对发送器和接收器之间通信的消息进行加密的技术，使得它们在没有正确的解密密钥的情况下非常难以解密。在这里，我们首先讨论了误差校正的混沌同步使用传统的方法与86%的准确度。然后，我们使用机器学习算法，以减少错误的解密消息提取的学习模式中的加密消息，并相应地调整加密参数。使用线性回归、k均值和DB-Scan，我们可以提高解密消息的原始准确性。此外，我们使用机器学习算法来检测加密消息中的异常。在混沌加密中使用机器学习有可能大大提高加密算法的安全性。
Machine learning is rapidly finding its way into the solving of everyday complex problems. One such application is in the area of chaotic encryption, where machine learning techniques can be used to improve the security and synchronization of encryption algorithms. Chaotic encryption is a technique that uses chaos theory to encrypt messages communicated between a transmitter and a receiver, making them extremely difficult to decipher without the correct decryption key. Here, we first discuss error correction for chaotic synchronization using conventional methods with an accuracy of 86%. We then use machine learning algorithms to reduce the error of the decrypted message extracted by learning patterns in the encrypted message and adjusting the encryption parameters accordingly. Using linear regression, k-mean, and DB-Scan, We present an increase in the original accuracy achieved by the decrypted message. Additionally, we use machine learning algorithms to detect anomalies in encrypted messages. The use of machine learning in chaotic encryption has the potential to greatly improve the security of encryption algorithms.