Work involving supervised machine learning to classify network traffic. Data are hand-classified (based upon flow content) to one of a number of categories. A combination of data set (hand-assigned) category and descriptions of the classified flows (such as flow length, port numbers, time between consecutive flows) are used to train the classifier. To give a better insight of the technique itself, initial assumptions are made as well as applying two other techniques in reality. One is to improve the quality and separation of the input of information leading to an increase in accuracy of the Naive Bayes classifier technique.
● Dominance of smartphones as the “communications hub” for social media, video consumption, tracking IoT/digitization applications (et al.), as well as traditional voice. Smartphones will represent 44 percent of global IP traffic by 2022 (up from 18 percent in 2017). This trend demonstrates the effect that smartphones have on how consumers and businesses users access and use the Internet and IP networks.
Integral to these verticals and looking into the future are the game-changing IoT devices and connections. According to the WBA Alliance, there is a need to find a dynamic way for IoT devices to search for a computable network and automatically roam between Wi-Fi and mobile networks at scale without intervention. Additionally, interest in Wi-Fi advertising and location services is growing as service providers search for new ways to monetize Wi-Fi and generate new revenue streams. It’s also clear there is a growing awareness and acceptance among consumers that data on their location, movement and behavior can be exchanged for free Wi-Fi.
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