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Network activities recognition has always been a significant component ofintrusion detection. However, with the increasing network traffic flow andcomplexity of network behavior, it is becoming more and more difficult toidentify the specific behavior quickly and accurately by user networkmonitoring software. It also requires the system security staff to pay closeattention to the latest intrusion monitoring technology and methods. All ofthese greatly increase the difficulty and complexity of intrusion detectiontasks. The application of machine learning methods based on supervisedclassification technology would help to liberate the network security stafffrom the heavy and boring tasks. A finetuned model would accurately recognizeuser behavior, which could provide persistent monitoring with a relative highaccuracy and good adaptability. Finally, the results of network activitiesrecognition by J48 and Na\"ive Bayes algorithms are introduced and evaluated.

Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Na\"ive Bayes Algorithm