Feature Engineering using Bio-Inspired Methods for the Internet Data Analytics

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Accurate classification of the Internet traffic has been a permanent concern for the Internet Service Providers for the two main reasons: network security; and network resource allocation. With the growth of the Internet traffic, in terms of the number and type of applications, traditional identification methods for the Internet data analytics face serious challenges when the newer and/or malicious applications use randomly selected ports to communicate making them increasingly difficult to identify. Thus, analysis of the Internet traffic based on its statistical behaviors using machine learning, evolutionary methods, and data mining techniques have gained much attention in academia and industry. For this, the Internet data must be captured and analyzed. The features extracted from the communications networks are very diverse and the datasets are typically large and multidimensional. Consequently, efficient transformation and computation of the significant features for adequate data analytics are important research tasks to focus on.