Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest

An intrusion is any set of actions intended to compromise the confidentiality, integrity, or availability of a resource. Network intrusions are prevalent, increasingly sophisticated, and are adept at hiding from detection. To counteract this ever-evolving threat, Network-based Intrusion Detection Sy...

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Bibliographic Details
Main Author:	Lee, Nicholas Ming Ze
Format:	Thesis
Published:	2019
Subjects:	QA75.5-76.95 Electronic computers Computer science
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id	my-mmu-ep.7747
record_format	uketd_dc
spelling	my-mmu-ep.77472020-09-21T19:23:22Z Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest 2019-09 Lee, Nicholas Ming Ze QA75.5-76.95 Electronic computers. Computer science An intrusion is any set of actions intended to compromise the confidentiality, integrity, or availability of a resource. Network intrusions are prevalent, increasingly sophisticated, and are adept at hiding from detection. To counteract this ever-evolving threat, Network-based Intrusion Detection System (NIDS) has since become a significant topic of research. Most network attacks are not caused by a single event, but a sequence of events. In order to mine the data from a sequence of events, conventional data mining approach is often inapplicable. In the case of conventional data mining, the relationship between the conditional attributes and the decision attribute is observed and investigated within the same record. In contrast, temporal classification considers the value of a class attribute based on the values of other attributes by taking advantage of the inherent sequences in the records. Thus, in this study, we present an approach in detecting network intrusions through the use of machine learning techniques. In addition to the human-engineered features that are extracted and aggregated to the latest time steps, our approach also factors in the previous events to classify an observation. Through a trained sequence model such as Long Short-Term Memory (LSTM) or Temporal Convolutional Network (TCN), high-level features can be retrieved from the outputs as the representation of past observations; and thereby allowing the Random Forest classifier to improve its predictive quality by incorporating both the original inputs and the learned representation into its training procedure. 2019-09 Thesis http://shdl.mmu.edu.my/7747/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Information Science & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Lee, Nicholas Ming Ze Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest
description	An intrusion is any set of actions intended to compromise the confidentiality, integrity, or availability of a resource. Network intrusions are prevalent, increasingly sophisticated, and are adept at hiding from detection. To counteract this ever-evolving threat, Network-based Intrusion Detection System (NIDS) has since become a significant topic of research. Most network attacks are not caused by a single event, but a sequence of events. In order to mine the data from a sequence of events, conventional data mining approach is often inapplicable. In the case of conventional data mining, the relationship between the conditional attributes and the decision attribute is observed and investigated within the same record. In contrast, temporal classification considers the value of a class attribute based on the values of other attributes by taking advantage of the inherent sequences in the records. Thus, in this study, we present an approach in detecting network intrusions through the use of machine learning techniques. In addition to the human-engineered features that are extracted and aggregated to the latest time steps, our approach also factors in the previous events to classify an observation. Through a trained sequence model such as Long Short-Term Memory (LSTM) or Temporal Convolutional Network (TCN), high-level features can be retrieved from the outputs as the representation of past observations; and thereby allowing the Random Forest classifier to improve its predictive quality by incorporating both the original inputs and the learned representation into its training procedure.
format	Thesis
qualification_level	Master's degree
author	Lee, Nicholas Ming Ze
author_facet	Lee, Nicholas Ming Ze
author_sort	Lee, Nicholas Ming Ze
title	Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest
title_short	Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest
title_full	Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest
title_fullStr	Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest
title_full_unstemmed	Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest
title_sort	temporal based network intrusion detection with recurrent neural network and random forest
granting_institution	Multimedia University
granting_department	Faculty of Information Science & Technology
publishDate	2019
_version_	1747829672826109952

Temporal Based Network Intrusion Detection With Recurrent Neural Network And Random Forest

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