Paper Info
Title
Intrusion detection of multiple attack classes using a deep neural net ensemble
Abstract
An intrusion detection system (IDS) is a necessity to protect against network attacks. The system monitors the activity within a network of connected computers in order to analyze the activity for intrusive patterns. Should an `attack' event happen, then the system has to respond accordingly. Different machine learning techniques have been proposed in the past roughly falling into two categories namely clustering algorithms and classification algorithms. In this paper, the IDS is designed with a neural network ensemble method to classify the different attacks. The neural network ensemble method comprises autoencoder, deep belief neural network, deep neural network, and an extreme learning machine. The NSL-KDD data set is used to measure the detection rate and false alarm rate of the implemented neural network ensemble method. The detection rate and false alarm rate are the two important measure for IDSs, however, several other measures are also reported on such as confusion matrix, classification accuracy, and AUC (area under curve).
Year
DOI
Venue
2017
10.1109/SSCI.2017.8280825
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
Keywords
Field
DocType
intrusive patterns,attack event,IDS,deep belief neural network,extreme learning machine,detection rate,false alarm rate,multiple attack classes,deep neural net ensemble,intrusion detection system,network attacks,machine learning techniques,neural network ensemble method
Autoencoder,Confusion matrix,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Constant false alarm rate,Statistical classification,Cluster analysis,Artificial neural network,Intrusion detection system
Conference
ISBN
Citations 
PageRank 
978-1-5386-2727-3
5
0.41
References 
Authors
16
1
Name
Order
Citations
PageRank
Simone A Ludwig11309179.41