基于SVM的机器学习方法Untuk Menganalisa Pengguna Data Trafik Internet（Studi Kasus Jaringan Internet Wlan Mahasiswa Bina Darma）
Penerapan Metode SVM-Based Machine Learning Untuk Menganalisa Pengguna Data Trafik Internet (Studi Kasus Jaringan Internet Wlan Mahasiswa Bina Darma)
摘要：
使用互联网是支持校园表现和活动的重要要求。为了控制互联网的使用，有必要了解互联网使用的分布。利用多种机器学习算法和WEKA软件，通过对校园WiFi热点的观察和数据采集进行研究。基于支持向量机的分类方法利用了支持向量机（SVM）的分类方法。本研究旨在对互联网使用数据进行分类，以便从这种分类中可以了解在某些时间广泛访问的目的地网络，协议和带宽。互联网流量数据通过Wireshark软件检索。而数据处理和互联网流量的数据处理由WEKA处理。结果显示：1）第一周133，196用户，第二周304，042用户的UBD互联网使用，2）使用目的地网络24，150和使用协议37，321，3）经常寻址的目的地网络是172.21.206.143（第一周）和172.21.172.234（第二周），TCP经常使用的协议，支持向量机方法是一种很好的数据采掘方法，用于对网络数据包模式进行分类，从而根据目的网络和协议产生网络流量分类。
英文摘要：
Internet usage is an important requirement that supports the performance and activities on campus. To control internet usage, it is necessary to know the distribution of internet usage. By utilizing a number of machine learning algorithms and WEKA software, the research is carried out by observation and taking data from wifi hotspots on campus. The classification method using SVM-Based utilizes the classification method owned by Support Vector Machine (SVM). This study aims to classify data on internet usage so that from this classification can be known destination network, protocol, and bandwidth that are widely accessed at certain times. Internet traffic data is retrieved through Wireshark software. Whereas data processing and data processing of internet traffic is processed by WEKA. The results showed: 1) UBD internet usage in the week I 133,196 users, week II 304,042 users,2) Use of Destination Network 24,150 and Use of Protocol 37,321,3) Destination networks that are often addressed are 172.21.206.143 (the week I) and 172.21.172.234 (week II), protocols that are often used by TCP, and4) SVM method is a good data mining method for classifying network packet patterns so as to produce network traffic classification according to destination network and protocol.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
