2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2018
DOI: 10.1109/icccnt.2018.8494186
|View full text |Cite
|
Sign up to set email alerts
|

Network Intrusion Detection Using Clustering and Gradient Boosting

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
17
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
4
1
1
1

Relationship

0
7

Authors

Journals

citations
Cited by 48 publications
(17 citation statements)
references
References 23 publications
0
17
0
Order By: Relevance
“…17 Although deep learning has garnered signi cant attention over the last years, gradient boosting, such as XGBoost, is one of the most widely used algorithms in Kaggle competitions for applying machine learning to structured tabular data. 18,19 Several studies have reported higher performance with Adaboost compared to XGBoost, despite the popularity of XGBoost. 20,21,22,23,24,25,26 Compared with the previous studies, this study proposes the following contributions.…”
Section: Discussionmentioning
confidence: 99%
“…17 Although deep learning has garnered signi cant attention over the last years, gradient boosting, such as XGBoost, is one of the most widely used algorithms in Kaggle competitions for applying machine learning to structured tabular data. 18,19 Several studies have reported higher performance with Adaboost compared to XGBoost, despite the popularity of XGBoost. 20,21,22,23,24,25,26 Compared with the previous studies, this study proposes the following contributions.…”
Section: Discussionmentioning
confidence: 99%
“…Various methods have been proposed in the literature for network anomaly detection including standard machine learning classifiers 4–29 and deep learning techniques 30–47 . Muda et al performed clustering before classification and compared the single classifiers with hybrid classifiers.…”
Section: Related Workmentioning
confidence: 99%
“…Dahiya and Srivastava compared two dimension reduction algorithms such as canonical correlation analysis and linear discriminant analysis using several classification algorithms and obtained at most 95.53% accuracy rate on UNSW‐NB dataset using canonical correlation analysis with bagging 27 . Verma et al compared several boosting algorithms using NSL‐KDD dataset, and they reached 99.86% accuracy rate using XGBoost with K‐means clustering 28 . Alrawashdeh and Khaled implemented deep restricted Boltzmann machine and deep belief network and obtained 97.9% detection rate using KDDCup99 dataset 32 .…”
Section: Related Workmentioning
confidence: 99%
“…Given this background, the literature identifies different types of IDSs: although they can operate in different manner, their common objective is the analysis and classification of each network event, distinguishing the normal activities from the intrusion ones. For instance, the literature reports intrusion detection approaches based on machine learning criteria such as gradient boosting [14], adaptive boosting [15], and random forests [16]. Other proposals involve artificial neural networks [17], probabilistic criteria [18], or data transformation/representation [19][20][21], similarly to what is done, in terms of scenario and data balance, in closely related domains [22][23][24][25][26][27][28][29][30].…”
Section: Introductionmentioning
confidence: 99%
“…Its way to operate is based on continuous exploitation of a weak learning method in order to obtain a sequence of hypotheses, which are reused on the difficult cases, with the aim to improve the classification performance. Some significant works in the intrusion detection area that exploit this algorithm can be found in [14,106].…”
mentioning
confidence: 99%