论文标题
Penerapan Metode基于SVM的机器学习UNTUK MENGALISA PENGGUNA数据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)
论文作者
论文摘要
互联网使用是支持校园表现和活动的重要要求。要控制Internet使用情况，有必要了解Internet使用的分布。通过利用许多机器学习算法和WEKA软件，该研究是通过观察和从校园中的WiFi热点获取数据来进行的。使用基于SVM的分类方法利用了支持向量机（SVM）拥有的分类方法。这项研究旨在对Internet使用情况进行分类，以便可以从此分类中进行广泛访问的目的地网络，协议和带宽。通过Wireshark软件检索Internet流量数据。 WEKA处理了Internet流量的数据处理和数据处理。结果表明：1）在第一周I 133,196用户的UBD Internet使用情况，第二周304,042周，2）使用目的地网络24,150和协议37,321,3）的目的地网络通常是172.21.206.143（第I周）和172.21.1.172.234（ii im iss and IS），STC，and Least and IS s and iss and 4）一种用于对网络数据包模式进行分类的良好数据挖掘方法，以根据目标网络和协议产生网络流量分类。
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.
