resulted in extra revenue of 175 billion forints under the new tax based on the consultancy firm eNet. According to Yahoo News, economy minister Mihály Varga defended the move saying "the tax was fair as it reflected a shift by consumers to the Internet away from phone lines" and that "150 forints on each transferred gigabyte of data – was needed to plug holes in the 2015 budget of one of the EU’s most indebted nations". Some people argue that the new plan on Internet tax would prove disadvantageous to the country’s economic development, limit access to information and hinder the freedom of expression. Approximately 36,000 people have signed up to take part in an event on Facebook to be held outside the Economy Ministry to protest against the possible tax. Traffic classification Traffic classification describes the methods of classifying traffic by observing features passively in the traffic and line with particular classification goals. There might be some that only have a vulgar classification goal. For example, whether it is bulk transfer, peer-to-peer file-sharing, or transaction-orientated. Some others will set a finer-grained classification goal, for instance, the exact number of applications represented by the traffic. Traffic features included port number, application payload, temporal, packet size, and the characteristic of the traffic. There is a vast range of methods to allocate Internet traffic including exact traffic, for instance, port (computer networking) number, payload, heuristic, or statistical machine learning. Accurate network traffic classification is elementary to quite a few Internet activities, from security monitoring to accounting and from the quality of service to providing operators with useful forecasts for long-term provisioning. Yet, classification schemes are extremely complex to operate accurately due to the shortage of available knowledge of the network. For example, the packet header-related information is always insufficient to allow for a precise methodology. Bayesian analysis techniques 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