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AI is one of the fastest growing areas of research, and its
applications have been tried and tested in areas such as stock
markets, real estate, big data, and more. However, adoption
in the area of networking has been slow due to the everchanging
nature of network traffic, making it very hard to
predict and distinguish between normal and abnormal traffic
conditions. Also, due to the characteristics of network traffic,
it is not possible to have realistic and accurate traffic datasets
which can be used for training in every scenario, as the AI
will learn the intricacies of the training data, but the training
models will not translate well to other situations, especially
into production networks. The main reason is that as traffic
patterns are unpredictable, network traffic from one day to
another day can vary greatly which may inaccurately classify
traffic as problematic when that may not be the case.
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IEEE/ICACT20210131 Slide.05
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