期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：9
页码：520-528
出版社：Science and Information Society (SAI)
摘要：Cyber-security, as an emerging field of research,
involves the development and management of techniques and
technologies for protection of data, information and devices.
Protection of network devices from attacks, threats and
vulnerabilities both internally and externally had led to the
development of ceaseless research into Network Intrusion
Detection System (NIDS). Therefore, an empirical study was
conducted on the effectiveness of deep learning and ensemble
methods in NIDS, thereby contributing to knowledge by
developing a NIDS through the implementation of machine and
deep-learning algorithms in various forms on recent network
datasets that contains more recent attacks types and attackers’
behaviours (UNSW-NB15 dataset). This research involves the
implementation of a deep-learning algorithm–Long Short-Term
Memory (LSTM)–and two ensemble methods (a homogeneous
method–using optimised bagged Random-Forest algorithm, and
a heterogeneous method–an Averaged Probability method of
Voting ensemble). The heterogeneous ensemble was based on
four (4) standard classifiers with different computational
characteristics (Naïve Bayes, kNN, RIPPER and Decision Tree).
The respective model implementations were applied on the
UNSW_NB15 datasets in two forms: as a two-classed attack
dataset and as a multi-attack dataset. LSTM achieved a detection
accuracy rate of 80% on the two-classed attack dataset and 72%
detection accuracy rate on the multi-attack dataset. The
homogeneous method had an accuracy rate of 98% and 87.4%
on the two-class attack dataset and the multi-attack dataset,
respectively. Moreover, the heterogeneous model had 97% and
85.23% detection accuracy rate on the two-class attack dataset
and the multi-attack dataset, respectively.
关键词：Cyber-security; intrusion detection system; deep
learning; ensemble methods; network attacks
