Читать книгу Intelligent Connectivity - Abdulrahman Yarali - Страница 46

One of the essential factors that have become apparent over time is that connectivity on the network is perhaps the most important factor. Not only do they reflect how well a specific technology performs, but they also address the possibility of horizontal usage and pervasiveness at large. Not only are these the factors that affect the entire case of technological innovation and advancement of what will happen, but also how effective they actually will be must be considered in full detail (Al‐Falahy and Alani 2017). Experts note that 5G connectivity's goal should reflect upon the widespread impact that 4G had over time (Hassabis et al. 2017); inasmuch as what will lead to the ubiquity of IoT and many other revolutionary technologies across the board. This will inimitably bring forth the question of cybersecurity, as has already been delineated beforehand.

However, the potential for creating change has become evident through the basic condition that the 5G network connectivity is still in its infancy. There must be some requirements that would require a proper form of addressing this (Chen and Zhao 2014). A prominent factor among these is setting up policy benchmarks that significantly reflect everything essential about the requirements that would not just pervade through the 5G networks but also the technologies that will operate upon it (O'Leary 2013). This might indicate an increase in the goals set by numerous organizations and individuals, but cybersecurity concerns on the network are most likely to affect more people in more critical ways.

The prevailing thought is that to address the cybersecurity issues, there is a need for AI routines implementation. Particularly, the machine learning aspects should play a very important role in such a significant need for detecting security threats across the different aspects of the 5G network (Jiang et al. 2017). The network will have multiple layers of both inputs and outputs and implement necessary perspectives that will speak about the continual monitoring of the different nodes that pervade all across the network at large (Dong et al. 2017). Moreover, proper machine learning should be able to “learn” about these threats, even when they might not be evident under any condition, which will inimitably identify these attacks in real‐time. Additionally, it should also indicate whether the overall conditions that pervade across the entire field should be updated (Hansen et al. 2015). This is an essential aspect of ensuring proper cybersecurity because the remedial measures become developed and implemented spontaneously and responsively.

One cannot deny the sheer advantage of having such an approach in the first place. However, some considerations need to be made. For one, Mobile Operators should be the initial purveyors of AI routines because they are responsible for managing all issues and factors that may arise within a network (Jiang et al. 2017). Another major concern is the scenario of whether the developments that the routines develop by themselves will be possible when considering the exponential increase of coverage, complexities, and domains that 5G technology will bring forth (Dong et al. 2017). This indicates that there needs to be significant effort put in to develop the operators' AI capabilities (Pagé and Dricot 2016). This will inimitably mean that the AI technologies will also undergo a critical increase in their capabilities and experience full flexibilities and versatilities in terms of the volume and type of problems they might face at large.