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In response to mobile terminal threats, this work is innovative, and gives practical solutions and prototype systems. The work first analyzes in detail the entire process of the generation, transmission and use of user-sensitive data within the Android system. In order to achieve resource sharing and data transmission between different applications and processes, the Android system provides a mechanism for inter-process communication based on Binder. Sensitive data is transmitted through Binder as a channel in the system. But sensitive data is carried out in the form of clear text during Binder transmission, which allows malware to easily intercept and tamper with sensitive data based on Binder communication, such as SMS content and GPS location information. In response to this problem, based on the above research, this work innovatively proposes and implements an adaptive transparent encryption protection scheme for sensitive data in the Android system from generation to use throughout the life cycle, effectively preventing sensitive data from being. The threat of theft and tampering by malicious third parties guarantees the privacy and integrity of user sensitive data during the internal transmission of the system. In addition, the system provides users with a simple and flexible operating experience, allowing users to independently protect specific types of sensitive data in specified applications, enhancing the ease of use and practicality of the system.

Anomaly detection systems can accurately identify malicious network traffic, providing network security. With the development of internet technology, network attacks are becoming more and more sourced and complicated, making it difficult for traditional anomaly detection systems to effectively analyze and identify abnormal traffic. At present, deep neural network (DNN) technology achieved great results in terms of anomaly detection, and it can achieve automatic detection. However, there still exists misclassified traffic in the prediction results of deep neural networks, resulting in redundant alarm information. This paper designs a two-level anomaly detection system based on deep neural network and association analysis. We made a comprehensive evaluation of experiments using DNNs and other neural networks based on publicly available datasets. Through the experiments, we chose DNN-4 as an important part of our system, which has high precision and accuracy in identifying malicious traffic. The Apriori algorithm can mine rules between various discretized features and normal labels, which can be used to filter the classified traffic and reduce the false positive rate. Finally, we designed an intrusion detection system based on DNN-4 and association rules. We conducted experiments on the public training set NSL-KDD, which is considered as a modified dataset for the KDDCup 1999. The results show that our detection system has great precision in malicious traffic detection, and it achieves the effect of reducing the number of false alarms.