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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| Published in: | Future internet Vol. 12; no. 3; p. 44 |
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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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| Cites_doi | 10.1016/j.knosys.2014.12.023 10.1109/JIOT.2018.2882794 10.1016/j.icte.2018.02.001 10.1016/j.asej.2013.01.003 10.1016/j.jocs.2017.03.006 10.1109/TII.2016.2599841 10.1007/978-3-540-30115-8_43 10.1016/j.eswa.2017.08.002 10.1016/j.future.2019.05.041 10.1145/1961189.1961199 10.1016/j.asoc.2018.07.052 10.1007/11893028_93 10.1002/dac.3547 10.1109/ICISSEC.2016.7885840 10.1177/1550147718794615 10.1016/j.eswa.2013.08.066 10.1016/j.asoc.2016.05.044 10.1016/j.knosys.2015.01.009 10.5220/0006639801080116 10.3390/s19143119 10.1016/j.eswa.2016.09.041 10.1007/s11235-017-0315-2 10.1109/DCOSS.2019.00059 10.1002/itl2.132 10.1016/j.eswa.2010.02.102 10.14236/ewic/icscsr19.16 10.1016/j.ijcip.2014.12.002 10.1007/s00521-015-1964-2 10.1016/j.comnet.2010.12.008 10.1007/s00521-016-2418-1 10.3390/computers8030058 10.1016/j.compeleceng.2008.12.005 10.1016/j.asoc.2012.04.020 10.1007/s10618-009-0131-8 10.1504/IJSN.2016.078390 |
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| References | Kevric (ref_17) 2017; 28 Zaim (ref_9) 2009; 35 Ferrag (ref_27) 2020; 50 Adnan (ref_29) 2017; 89 Giordano (ref_28) 2018; 72 Kim (ref_14) 2014; 41 ref_36 Cruz (ref_7) 2016; 12 ref_34 ref_33 Elbasiony (ref_13) 2013; 4 Ibarguren (ref_30) 2015; 79 Chung (ref_12) 2012; 12 Chang (ref_31) 2011; 2 Folino (ref_35) 2016; 47 Ahmim (ref_19) 2018; 31 Koroniotis (ref_40) 2019; 100 ref_39 Lin (ref_15) 2015; 78 ref_38 ref_37 Ferrag (ref_2) 2017; 66 Rahmani (ref_16) 2016; 27 Ferrag (ref_25) 2018; 6 Maglaras (ref_1) 2018; 4 ref_24 Othman (ref_18) 2017; 67 ref_23 Huehn (ref_32) 2009; 19 ref_22 Wang (ref_10) 2010; 37 ref_21 Govindarajan (ref_11) 2011; 55 Ferrag (ref_3) 2016; 11 Hu (ref_6) 2018; 14 Aljawarneh (ref_20) 2018; 25 Maglaras (ref_5) 2020; 3 ref_26 ref_8 Alcaraz (ref_4) 2015; 8 |
| References_xml | – volume: 79 start-page: 51 year: 2015 ident: ref_30 article-title: Coverage-based resampling: Building robust consolidated decision trees publication-title: Knowl. Syst. doi: 10.1016/j.knosys.2014.12.023 – volume: 6 start-page: 2188 year: 2018 ident: ref_25 article-title: Blockchain technologies for the internet of things: Research issues and challenges publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2018.2882794 – volume: 4 start-page: 42 year: 2018 ident: ref_1 article-title: Cyber security of critical infrastructures publication-title: ICT Express doi: 10.1016/j.icte.2018.02.001 – volume: 4 start-page: 753 year: 2013 ident: ref_13 article-title: A hybrid network intrusion detection framework based on random forests and weighted k-means publication-title: Ain Shams Eng. J. doi: 10.1016/j.asej.2013.01.003 – volume: 25 start-page: 152 year: 2018 ident: ref_20 article-title: Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2017.03.006 – volume: 12 start-page: 2236 year: 2016 ident: ref_7 article-title: A cybersecurity detection framework for supervisory control and data acquisition systems publication-title: IEEE Trans. Ind. Inf. doi: 10.1109/TII.2016.2599841 – ident: ref_34 doi: 10.1007/978-3-540-30115-8_43 – volume: 89 start-page: 389 year: 2017 ident: ref_29 article-title: Forest PA: Constructing a decision forest by penalizing attributes used in previous trees publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.08.002 – volume: 100 start-page: 779 year: 2019 ident: ref_40 article-title: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2019.05.041 – volume: 2 start-page: 27 year: 2011 ident: ref_31 article-title: LIBSVM: A library for support vector machines publication-title: ACM Trans. Intell. Syst. Technol. doi: 10.1145/1961189.1961199 – volume: 72 start-page: 338 year: 2018 ident: ref_28 article-title: An experimental evaluation of weightless neural networks for multi-class classification publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2018.07.052 – ident: ref_37 – ident: ref_33 doi: 10.1007/11893028_93 – volume: 31 start-page: e3547 year: 2018 ident: ref_19 article-title: An intrusion detection system based on combining probability predictions of a tree of classifiers publication-title: Int. J. Commun. Syst. doi: 10.1002/dac.3547 – ident: ref_21 – ident: ref_39 doi: 10.1109/ICISSEC.2016.7885840 – volume: 14 start-page: 1550147718794615 year: 2018 ident: ref_6 article-title: A survey of intrusion detection on industrial control systems publication-title: Int. J. Distrib. Sens. Netw. doi: 10.1177/1550147718794615 – volume: 41 start-page: 1690 year: 2014 ident: ref_14 article-title: A novel hybrid intrusion detection method integrating anomaly detection with misuse detection publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2013.08.066 – volume: 47 start-page: 179 year: 2016 ident: ref_35 article-title: Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2016.05.044 – volume: 78 start-page: 13 year: 2015 ident: ref_15 article-title: CANN: An intrusion detection system based on combining cluster centers and nearest neighbors publication-title: Knowl. Syst. doi: 10.1016/j.knosys.2015.01.009 – ident: ref_24 doi: 10.5220/0006639801080116 – ident: ref_22 doi: 10.3390/s19143119 – volume: 67 start-page: 296 year: 2017 ident: ref_18 article-title: Multi-level hybrid support vector machine and extreme learning machine based on modified K-means for intrusion detection system publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2016.09.041 – volume: 66 start-page: 671 year: 2017 ident: ref_2 article-title: EPEC: An efficient privacy-preserving energy consumption scheme for smart grid communications publication-title: Telecommun. Syst. doi: 10.1007/s11235-017-0315-2 – volume: 50 start-page: 102419 year: 2020 ident: ref_27 article-title: Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study publication-title: J. Inf. Secur. Appl. – ident: ref_8 doi: 10.1109/DCOSS.2019.00059 – volume: 3 start-page: e132 year: 2020 ident: ref_5 article-title: Teaching the process of building an Intrusion Detection System using data from a small-scale SCADA testbed publication-title: Internet Technol. Lett. doi: 10.1002/itl2.132 – volume: 37 start-page: 6225 year: 2010 ident: ref_10 article-title: A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2010.02.102 – ident: ref_26 doi: 10.14236/ewic/icscsr19.16 – ident: ref_38 – volume: 8 start-page: 53 year: 2015 ident: ref_4 article-title: Critical infrastructure protection: Requirements and challenges for the 21st century publication-title: Int. J. Crit. Infrastruct. Prot. doi: 10.1016/j.ijcip.2014.12.002 – volume: 27 start-page: 1669 year: 2016 ident: ref_16 article-title: A hybrid method consisting of GA and SVM for intrusion detection system publication-title: Neural Comput. Appl. doi: 10.1007/s00521-015-1964-2 – ident: ref_36 – volume: 55 start-page: 1662 year: 2011 ident: ref_11 article-title: Intrusion detection using neural based hybrid classification methods publication-title: Comput. Netw. doi: 10.1016/j.comnet.2010.12.008 – volume: 28 start-page: 1051 year: 2017 ident: ref_17 article-title: An effective combining classifier approach using tree algorithms for network intrusion detection publication-title: Neural Comput. Appl. doi: 10.1007/s00521-016-2418-1 – ident: ref_23 doi: 10.3390/computers8030058 – volume: 35 start-page: 517 year: 2009 ident: ref_9 article-title: A hybrid intrusion detection system design for computer network security publication-title: Comput. Electr. Eng. doi: 10.1016/j.compeleceng.2008.12.005 – volume: 12 start-page: 3014 year: 2012 ident: ref_12 article-title: A hybrid network intrusion detection system using simplified swarm optimization (SSO) publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2012.04.020 – volume: 19 start-page: 293 year: 2009 ident: ref_32 article-title: FURIA: An Algorithm for Unordered Fuzzy Rule Induction publication-title: Data Min. Knowl. Discov. doi: 10.1007/s10618-009-0131-8 – volume: 11 start-page: 107 year: 2016 ident: ref_3 article-title: EPSA: An efficient and privacy-preserving scheme against wormhole attack on reactive routing for mobile ad hoc social networks publication-title: Int. J. Secur. Netw. doi: 10.1504/IJSN.2016.078390 |
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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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