Paper Info
Title
A Dimension Reduction Model and Classifier for Anomaly-Based Intrusion Detection in Internet of Things.
Abstract
Internet of Things (IoT) devices and services have gained wide spread growth in many commercial and mission critical applications. The devices and services suffer from intrusions, attacks and malicious activities. To protect valuable data transmitted through IoT networks and usersu0027privacy, intrusion detection systems (IDS) should be developed to match with the characteristics of IoT, which requires real-time monitoring. This paper proposes a novel model for intrusion detection which is based on dimensionreduction algorithm and a classifier, which can be used as an online machine learning algorithm. The proposed model uses Principal Component Analysis (PCA) to reduce dimensions of dataset from a large number of features to a small number. To develop a classifier, softmax regression and k-nearestneighbour algorithms are applied and compared. Experimental results using KDD Cup 99 Data Set show that our proposed model performs optimally in labelling benign behaviours and identifying malicious behaviours. Thecomputing complexity and time performance approve that the model can be used to detect intrusions in IoT.
Year
Venue
Field
2017
DASC/PiCom/DataCom/CyberSciTech
Data mining,Data modeling,Online machine learning,Dimensionality reduction,Softmax function,Computer science,Artificial intelligence,Mission critical,Statistical classification,Classifier (linguistics),Intrusion detection system,Machine learning
DocType
Citations 
PageRank 
Conference
2
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Shengchu Zhao120.35
Wei Li222725.46
Tanveer A. Zia38013.50
Albert Y. Zomaya45709454.84