Network Intrusion Detection System Using Principal Component Analysis Algorithm and Decision Tree Classifier

被引：2
|
作者：
Osho, Oyeyemi [1 ]
Hong, Sungbum [2 ,3 ]
Kwembe, Tor A. [4 ]
机构：
[1] Jackson State Univ, Dept Computat Data Enabled Sci & Engn CDS&E, Jackson, MS 39217 USA
[2] Jackson State Univ, Dept Elect & Comp Engn, Jackson, MS 39217 USA
[3] Jackson State Univ, Dept Comp Sci, Jackson, MS 39217 USA
[4] Jackson State Univ, Dept Math & Stat Sci, Jackson, MS 39217 USA
来源：
2021 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2021) | 2021年
基金：
美国国家科学基金会;
关键词：
Intrusion Detection; Anomaly Detection; Principal Component Analysis; CICIDS2017; Decision Tree Classifier;
D O I：
10.1109/CSCI54926.2021.00117
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Network Intrusion Detection Systems (IDS) have become expedient for network security and ensures the safety of all connected devices. Network Intrusion Detection System (IDS) alludes to observing network data information swiftly,, detecting any intrusion pattern and preventing any harmful effect of anomaly intrusion that will cost the network. To combat this issue, we present in this concept paper an IDS based on the Principal Component Analysis (PCA) and Decision Tree Classifier algorithm, a supervised machine teaming model to detect intrusion in the Network.
引用
收藏
页码：273 / 279
页数：7
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