Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

With the advent of the 5G network era, the convenience of mobile smartphones has become increasingly prominent, the use of mobile applications has become wider and wider, and the number of mobile applications. However, the privacy of mobile applications and the security of users' privacy information are worrying. This article aims to study the ratings of data and machine learning on the privacy security of mobile applications, and uses the experiments in this article to conduct data collection, data analysis, and summary research. This paper experimentally establishes a machine learning model to realize the prediction of privacy scores of Android applications. The establishment of this model is based on the intent of using sensitive permissions in the application and related metadata. It is to create a regression function that can implement the mapping of applications to score . Experimental data shows that the feature vector prediction model can uniquely be used to represent the actual usage and scheme of a system's specific permissions for the application.

Dynamic Packet Duplication for Industrial URLLC

The fifth-generation (5G) network is presented as one of the main options for Industry 4.0 connectivity. To comply with critical messages, 5G offers the Ultra-Reliable and Low latency Communications (URLLC) service category with a millisecond end-to-end delay and reduced probability of failure. There are several approaches to achieve these requirements; however, these come at a cost in terms of redundancy, particularly the solutions based on multi-connectivity, such as Packet Duplication (PD). Specifically, this paper proposes a Machine Learning (ML) method to predict whether PD is required at a specific data transmission to successfully send a URLLC message. This paper is focused on reducing the resource usage with respect to pure static PD. The concept was evaluated on a 5G simulator, comparing between single connection, static PD and PD with the proposed prediction model. The evaluation results show that the prediction model reduced the number of packets sent with PD by 81% while maintaining the same level of latency as a static PD technique, which derives from a more efficient usage of the network resources.

Role of 5G Communication Along With Blockchain Security in Brain-Computer Interfacing

The electroencephalogram is used in brain-computer interface (BCI) in which signal from the human brain is sensed with the help of EEG and then sent to the computer to control the external device without having any touch of muscular body parts. On the other hand, the brain chip interfacing (BCHIs) is a microelectronic chip that has physical connections with the neurons for the transfer of information. The BCI needs a reliable, high-speed network and new security tool that can assist BCI technology. 5G network and blockchain technology is ideal to support the growing needs of brain chip interfacing. Further, the Cloudmind, which is an emerging application of BCI, can be conceptualized by using blockchain technology. In this chapter, brain-computer interfaces (BCIs) are expedient to bridge the connectivity chasm between human and machine (computer) systems via 5G technologies, which offers minimal latency, faster speeds, and stronger bandwidth connectivity with strong cryptographic qualities of blockchain technologies.

Research on cyber security risk of telematics box in intelligent connected vehicle

With the rapid development of the automotive industry and the wide application of 5G network technology, there are more and more Telematics Box (T-Box) equipped with intelligent operating systems in vehicles and they are becoming more and more complex. Because it is connected to the on-board CAN bus internally and interconnects with mobile phone /PC through the cloud platform externally, the security of T-Box must be fully guaranteed, to make the automotive more secure. T-Box can realize remote control function, so the T-Box information security problem has been paid more and more attention. In this paper, the T-Box were tested from multiple dimensions by using various methods, and the results were statistically analyzed, and the corresponding protection strategies were proposed for the corresponding security risks.