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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| Vydané v: | Future internet Ročník 12; číslo 3; s. 44 |
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| Hlavní autori: | , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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MDPI AG
01.03.2020
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| ISSN: | 1999-5903, 1999-5903 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Derdour, Makhlouf Janicke, Helge Ferrag, Mohamed Amine Ahmim, Ahmed Maglaras, Leandros |
| Author_xml | – sequence: 1 givenname: Mohamed Amine orcidid: 0000-0002-0632-3172 surname: Ferrag fullname: Ferrag, Mohamed Amine – sequence: 2 givenname: Leandros orcidid: 0000-0001-5360-9782 surname: Maglaras fullname: Maglaras, Leandros – sequence: 3 givenname: Ahmed orcidid: 0000-0001-5519-8868 surname: Ahmim fullname: Ahmim, Ahmed – sequence: 4 givenname: Makhlouf orcidid: 0000-0001-6622-4355 surname: Derdour fullname: Derdour, Makhlouf – sequence: 5 givenname: Helge orcidid: 0000-0002-1345-2829 surname: Janicke fullname: Janicke, Helge |
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| Snippet | This paper proposes a novel intrusion detection system (IDS), named RDTIDS, for Internet-of-Things (IoT) networks. The RDTIDS combines different classifier... |
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| SubjectTerms | 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 |
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| Title | RDTIDS: Rules and Decision Tree-Based Intrusion Detection System for Internet-of-Things Networks |
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