【Abstract】In this study, future cars are attempting self-driving around the world. However, hacking, such as ECUs in automobiles, creates problems that are directly connected to human life. Therefore, this study wrote a paper that detects anomalies in such cars by field. As a related study, the study investigated the vulnerabilities of the automobile security committee and automobile security standards and investigated the detection of abnormalities in the hacking of geo-train cars using artificial intelligence's LSTM and blockchain consensus algorithm. In addition, in automobile security, an algorithm was studied to predict normal and abnormal values using LSTM-based anomaly detection techniques on the premise that automobile communication networks are largely divided into internal and external networks. In the methodology, LSTM's pure propagation malicious code detection technique was used, and it worked with an artificial intelligence consensus algorithm to increase security. In addition, Unity ML conducted an experiment by constructing a virtual environment using the Beta version. The LSTM blockchain consensus node network was composed of 50,000 processes to compare performance. For the first time, 100 Grouped Tx, 500 Channels were tested for performance. For the first time, the malicious code detection rate of the existing system was verified. Accelerator, Multichannel, Sharding, Raiden, Plasma, and Trubit values were verified, and values of approximately 15,000 to 50,000 were obtained. In this paper, we studied to become a paper of great significance on hacking that threatens human life with the development of self-driving cars in the future.
【Keywords】algorithm; information security; deep learning; LSTM; blockchain
【摘要】在这项研究中，未来的汽车正在世界各地尝试自动驾驶。然而，黑客攻击（例如汽车中的 ECU）会产生与人类生活直接相关的问题。因此，这项研究写了一篇论文，按字段检测此类汽车的异常情况。作为一项相关研究，该研究调查了汽车安全委员会和汽车安全标准的漏洞，并调查了使用人工智能的 LSTM 和区块链共识算法对地球列车车厢黑客行为的异常检测。此外，在汽车安全方面，在汽车通信网络大体分为内部网络和外部网络的前提下，研究了一种利用基于LSTM的异常检测技术预测正常值和异常值的算法。在方法论中，使用了 LSTM 的纯传播恶意代码检测技术，并与人工智能共识算法协同工作以提高安全性。此外，Unity ML 通过使用 Beta 版本构建虚拟环境进行了实验。 LSTM 区块链共识节点网络由 50,000 个进程组成，用于比较性能。首次对 100 个分组发送、500 个通道进行性能测试。首次验证了现有系统的恶意代码检测率。 Accelerator、Multichannel、Sharding、Raiden、Plasma 和 Trubit 的值得到了验证，得到了大约 15,000 到 50,000 的值。在本文中，我们研究成为未来自动驾驶汽车发展威胁人类生命的黑客攻击具有重要意义的论文。
【关键词】算法;信息安全;深度学习；长短期记忆法；区块链
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