RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks

This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first an...

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Vydáno v:Future internet Ročník 12; číslo 3; s. 44
Hlavní autoři: Ferrag, Mohamed Amine, Maglaras, Leandros, Ahmim, Ahmed, Derdour, Makhlouf, Janicke, Helge
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.03.2020
Témata:
Access control
Accuracy
Algorithms
Classification
Classifiers
Clustering
Communications traffic
Data mining
Datasets
Decision trees
False alarms
hierarchical
hybrid ids
ids
Internet of Things
intrusion detection
Intrusion detection systems
learning machine
Machine learning
network security
Neural networks
Performance evaluation
Support vector machines
Taxonomy
ISSN:1999-5903, 1999-5903
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Shrnutí:This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier approaches which are based on decision tree and rules-based concepts, namely, REP Tree, JRip algorithm and Forest PA. Specifically, the first and second method take as inputs features of the data set, and classify the network traffic as Attack/Benign. The third classifier uses features of the initial data set in addition to the outputs of the first and the second classifier as inputs. The experimental results obtained by analyzing the proposed IDS using the CICIDS2017 dataset and BoT-IoT dataset, attest their superiority in terms of accuracy, detection rate, false alarm rate and time overhead as compared to state of the art existing schemes.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1999-5903
1999-5903
DOI:10.3390/fi12030044