Improving the Feature Set in IoT Intrusion Detection Problem Based on FP-Growth Algorithm

This paper proposes and develops a new method to improve the feature set in IoT intrusion detection problem based on FP-Growth algorithm. Extracting a good feature set makes an important contribution to improving the accuracy of the IoT intrusion detection problem. Current studies on the detection o...

Full description

Saved in:
Bibliographic Details
Main Authors: Hong Van, Le Thi, Van Huong, Pham, Thuan, Le Duc, Hieu Minh, Nguyen
Format: Conference Proceeding
Language:English
Subjects:
convolutional neural network (CNN)
Correlation
Data mining
Deep learning
Feature extraction
FP-Growth
improve feature
Intrusion detection
IoT intrusion detection
Itemsets
Mathematical model
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper proposes and develops a new method to improve the feature set in IoT intrusion detection problem based on FP-Growth algorithm. Extracting a good feature set makes an important contribution to improving the accuracy of the IoT intrusion detection problem. Current studies on the detection of new IoT attacks focus on extracting independent features without considering the relationship between specific features and groups of features. Therefore, in the paper, we analyze and evaluate the original feature set to find the relationship between feature groups and add new features generated from relationships with a certain threshold to the original feature set to create the improved feature set. The detection of relationships reaching threshold is based on combination law mining technique according to FP-Growth algorithm. The improved feature set was tested with detection method based on convolution neural network with accuracy up to 98.2% and 1.45% improvement compared to the original feature set.
ISSN:2162-1039
DOI:10.1109/ATC50776.2020.9255431