In actual operation, there are security risks to the data of the network control system, mainly in the form of possible eavesdropping of signals in the transmission channel and parameters in the controller leading to data leakage. In this paper, we propose a scheme for encrypting linear controllers using fully homomorphic encryption, which effectively removes these security risks and substantially improves the security of networked control systems. Meanwhile, this paper uses precomputation to handle data encryption, which eliminates the encryption time and solves the drawback of fully homomorphic encryption that it is difficult to apply due to the efficiency problem. Compared to previous schemes with precomputation, for the first time, we propose two methods to mitigate the problem of the slight security degradation caused by precomputation, which makes our scheme more secure. Finally, we provide numerical simulation results to support our scheme, and the data show that the encrypted controller achieves normal control and improves safety and efficiency.

The utilization of cryptography in applications has assumed paramount importance with the escalating security standards for Android applications. The adept utilization of cryptographic APIs can significantly enhance application security; however, in practice, software developers frequently misuse these APIs due to their inadequate grasp of cryptography. A study reveals that a staggering 88% of Android applications exhibit some form of cryptographic misuse. Although certain tools have been proposed to detect such misuse, most of them rely on manually devised rules which are susceptible to errors and require researchers possessing an exhaustive comprehension of cryptography. In this study, we propose a research methodology founded on a neural network model to pinpoint code related to cryptography by employing program slices as a dataset. We subsequently employ active learning, rooted in clustering, to select the portion of the data harboring security issues for annotation in accordance with the Android cryptography usage guidelines. Ultimately, we feed the dataset into a transformer and multilayer perceptron (MLP) to derive the classification outcome. Comparative experiments are also conducted to assess the model’s efficacy in comparison to other existing approaches. Furthermore, planned combination tests utilizing supplementary techniques aim to validate the model’s generalizability.