Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks
Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malici...
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| Vydáno v: | IET communications Ročník 14; číslo 5; s. 888 - 895 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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The Institution of Engineering and Technology
17.03.2020
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| ISSN: | 1751-8628, 1751-8636 |
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| Abstract | Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio. |
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| AbstractList | Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus only on the detection of the known types of attacks. However, it neglects to recognise the new types of attacks, which are introduced by malicious users leading to vulnerability and information loss in the network. In order to address this challenge, a new intrusion detection system, which detects the known and unknown types of attacks using an intelligent decision tree classification algorithm, has been proposed. For this purpose, a novel feature selection algorithm named dynamic recursive feature selection algorithm, which selects an optimal number of features from the data set is proposed. In addition, an intelligent fuzzy temporal decision tree algorithm is also proposed by extending the decision tree algorithm and integrated with convolution neural networks to detect the intruders effectively. The experimental analysis carried out using KDD cup data set and network trace data set demonstrates the effectiveness of this proposed approach. It proved that the false positive rate, energy consumption, and delay are reduced in the proposed work. In addition, the proposed system increases the network performance through increased packet delivery ratio. |
| Author | Nancy, Periasamy Muthurajkumar, S Ganapathy, S Santhosh Kumar, S.V.N Selvi, M Arputharaj, Kannan |
| Author_xml | – sequence: 1 givenname: Periasamy surname: Nancy fullname: Nancy, Periasamy email: nancyp01@yahoo.com organization: 1Department of Computer Technology, MIT Campus, Anna University, Chennai- 600044, India – sequence: 2 givenname: S surname: Muthurajkumar fullname: Muthurajkumar, S organization: 2Department of Computer Technology, Anna University, MIT Campus, Chennai, India – sequence: 3 givenname: S surname: Ganapathy fullname: Ganapathy, S organization: 3School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India – sequence: 4 givenname: S.V.N surname: Santhosh Kumar fullname: Santhosh Kumar, S.V.N organization: 4School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India – sequence: 5 givenname: M surname: Selvi fullname: Selvi, M organization: 5School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India – sequence: 6 givenname: Kannan surname: Arputharaj fullname: Arputharaj, Kannan organization: 5School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India |
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| Cites_doi | 10.1016/j.jfranklin.2017.06.006 10.1016/j.jcss.2014.02.008 10.1016/j.cose.2017.10.011 10.1016/j.compeleceng.2018.03.036 10.1109/IJCNN.2009.5178804 10.1016/j.comnet.2018.01.007 10.1016/j.neucom.2016.08.042 10.1016/j.asoc.2018.02.051 10.7551/mitpress/3905.001.0001 10.3233/IFS-130803 10.1007/s11276-017-1549-3 10.1016/j.procs.2018.05.198 10.1016/j.procs.2018.05.069 10.1016/j.comnet.2018.02.028 10.1016/j.compeleceng.2017.09.028 10.1016/j.comcom.2012.04.012 10.1016/j.future.2013.09.015 10.1007/s10586-018-2181-4 10.1016/j.cam.2016.04.023 10.1007/s10586-017-1191-y 10.1145/2133360.2133364 10.1109/TMC.2010.44 10.1016/j.eswa.2017.07.005 10.1016/j.eswa.2017.11.041 |
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| Keywords | telecommunication security intelligent decision tree classification algorithm pattern classification KDD cup data set false positive rate wireless sensor networks data mining fuzzy neural nets convolution neural networks dynamic recursive feature selection algorithm packet delivery ratio fuzzy temporal decision tree classification intrusion detection system security of data intelligent fuzzy temporal decision tree algorithm network performance feature extraction decision trees convolutional neural nets learning (artificial intelligence) network trace data energy consumption |
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| References | Fahad, A.; Tari, Z.; Khalil, I. (C15) 2014; 36 Jin, Y.; Duffield, N.; Erman, J. (C19) 2012; 6 Ganapathy, S.; Kulothungan, K.; Kumar, S.M. (C13) 2013; 271 Selvi, M.; Velvizhy, P.; Ganapathy, S. (C26) 2019; 22 Hamed, T.; Dara, R.; Kremer, S.C. (C16) 2018; 73 Singh, H.R.; Biswas, S.K.; Purkayastha, B. (C21) 2017; 309 Muthuraj Kumar, S.; Ganapathy, S.; Vijayalakshmi, M. (C27) 2017; 96 Sethukkarasi, R.; Ganapathy, S.; Yogesh, P. (C20) 2014; 26 Zou, X.; Cao, J.; Guo, Q. (C23) 2018; 65 Herrera-Semenets, V.; Pérez-García, O.A.; Hernández-León, R. (C24) 2018; 95 Logambigai, R.; Ganapathy, S.; Kannan, A. (C28) 2018; 68 Rajendren, R.; Santhosh Kumar, S.V.N.; Palanichimy, Y. (C12) 2019; 22 Wang, Y.; Xiang, Y.; Zhang, J. (C14) 2014; 80 Li, F.; Wu, J. (C7) 2010; 9 Shi, H.; Li, H.; Zhang, D. (C17) 2018; 132 Hasan Haldar, N.A.; Khan, F.A.; Ali, A. (C22) 2017; 220 Santhosh Kumar, S.V.N.; Palanichimy, Y. (C25) 2018; 24 Akashdeep Manzoor, I.; Kumar, N. (C3) 2017; 88 Sharma, N.; Jain, V.; Mishra, A. (C9) 2018; 132 Indoli, S.; Goswamib, A.K.; Mishrab, S.P. (C8) 2018; 132 Zhang, H.; Lu, G.; Qassrawi, M.T. (C11) 2012; 35 Hajisalem, V.; Babaie, S. (C1) 2018; 136 Yin, C.; Zhu, Y.; Fei, J. (C10) 2017; 5 Roshan, S.; Miche, Y.; Akusok, A. (C2) 2018; 355 Maldonadoa, S.; López, J. (C4) 2018; 67 2017; 5 2017; 88 2009 2014; 26 1995 2018; 67 2012; 35 2018; 65 2018; 24 2018; 68 2018; 132 2017; 309 2017; 96 2014; 80 2000 2019; 22 2018; 355 2018; 136 2014; 36 2018; 73 2018; 95 2017; 220 2013 2012; 6 2013; 271 2010; 9 e_1_2_7_6_1 e_1_2_7_5_1 e_1_2_7_4_1 e_1_2_7_3_1 Ganapathy S. (e_1_2_7_14_1) 2013; 271 e_1_2_7_9_1 e_1_2_7_8_1 e_1_2_7_7_1 e_1_2_7_19_1 e_1_2_7_18_1 e_1_2_7_17_1 e_1_2_7_16_1 e_1_2_7_2_1 e_1_2_7_15_1 e_1_2_7_13_1 Rice J.A. (e_1_2_7_30_1) 2013 e_1_2_7_12_1 e_1_2_7_10_1 e_1_2_7_26_1 e_1_2_7_27_1 e_1_2_7_29_1 Muthuraj Kumar S. (e_1_2_7_28_1) 2017; 96 Yin C. (e_1_2_7_11_1) 2017; 5 e_1_2_7_25_1 e_1_2_7_24_1 e_1_2_7_23_1 e_1_2_7_22_1 e_1_2_7_21_1 e_1_2_7_20_1 |
| References_xml | – volume: 355 start-page: 1752 year: 2018 end-page: 1779 ident: C2 article-title: Adaptive and online network intrusion detection system using clustering and extreme learning machines publication-title: J. Franklin Inst. – volume: 136 start-page: 37 year: 2018 end-page: 50 ident: C1 article-title: A hybrid intrusion detection system based on ABC–AFS algorithm for misuse and anomaly detection publication-title: Comput. Netw. – volume: 35 start-page: 1457 year: 2012 end-page: 1471 ident: C11 article-title: Feature selection for optimizing traffic classification publication-title: Comput. Commun. – volume: 271 start-page: 1 issue: 1 year: 2013 end-page: 16 ident: C13 article-title: Intelligent feature selection and classification techniques for intrusion detection in networks: a survey publication-title: EURASIP J. Wirel. Commun. Netw. – volume: 68 start-page: 62 year: 2018 end-page: 75 ident: C28 article-title: Energy-efficient grid-based routing algorithm using intelligent fuzzy rules for wireless sensor networks publication-title: Comput. Electr. Eng. – volume: 132 start-page: 81 year: 2018 end-page: 98 ident: C17 article-title: An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification publication-title: Comput. Net. – volume: 220 start-page: 221 year: 2017 end-page: 235 ident: C22 article-title: Arrhythmia classification using Mahalanobis distance based improved fuzzy C-means clustering for mobile health monitoring systems publication-title: Neurocomputing – volume: 22 start-page: 10839 year: 2019 end-page: 10848 ident: C26 article-title: A rule-based delay constrained energy efficient routing technique for wireless sensor networks publication-title: Cluster Comput. – volume: 22 start-page: 423 issue: 1 year: 2019 end-page: 434 ident: C12 article-title: Detection of DoS attacks in cloud networks using an intelligent rule-based classification system publication-title: Cluster Comput.. – volume: 88 start-page: 249 year: 2017 end-page: 257 ident: C3 article-title: A feature reduced intrusion detection system using ANN classifier publication-title: Expert Syst. Appl. – volume: 67 start-page: 94 year: 2018 end-page: 105 ident: C4 article-title: Dealing with high-dimensional class-imbalanced datasets: embedded feature selection for SVM classification publication-title: Appl. Soft Comput. – volume: 309 start-page: 683 year: 2017 end-page: 694 ident: C21 article-title: A neuro-fuzzy classification technique using dynamic clustering and GSS rule generation publication-title: J. Comput. Appl. Math. – volume: 96 start-page: 1752 issue: 2 year: 2017 end-page: 1769 ident: C27 article-title: An intelligent secured and energy efficient routing algorithm for MANETs publication-title: Wirel. Pers. Commun. – volume: 24 start-page: 1343 issue: 4 year: 2018 end-page: 1360 ident: C25 article-title: Energy efficient and secured distributed data dissemination using hop by authentication in WSN publication-title: Wirel. Netw. – volume: 9 start-page: 1035 issue: 7 year: 2010 end-page: 1048 ident: C7 article-title: Uncertainty modeling and reduction in MANETs publication-title: IEEE Trans. Mob. Comput. – volume: 80 start-page: 1021 year: 2014 end-page: 1036 ident: C14 article-title: Internet traffic clustering with side information publication-title: J. Comput. Syst. Sci. – volume: 6 start-page: 1 year: 2012 end-page: 34 ident: C19 article-title: A modular machine learning system for flow-level traffic classification in large network's publication-title: ACM Trans. Knowl. Discov. Data – volume: 73 start-page: 137 year: 2018 end-page: 155 ident: C16 article-title: Network intrusion detection system based on recursive feature addition and bigram technique publication-title: Comput. Secur. – volume: 36 start-page: 156 year: 2014 end-page: 169 ident: C15 article-title: An optimal and stable feature selection approach for traffic classification based on multi-criterion fusion publication-title: Future Gener. Comput. Syst. – volume: 26 start-page: 1167 issue: 3 year: 2014 end-page: 1178 ident: C20 article-title: An intelligent neuro-fuzzy temporal knowledge representation model for mining temporal patterns publication-title: J. Intell. Fuzzy Syst. – volume: 65 start-page: 67 year: 2018 end-page: 78 ident: C23 article-title: A novel network security algorithm based on improved support vector machine from smart city perspective publication-title: Comput. Electr. Eng. – volume: 95 start-page: 272 year: 2018 end-page: 279 ident: C24 article-title: A data reduction strategy and its application on the scan and backscatter detection using rule-based classifiers publication-title: Expert Syst. Appl. – volume: 5 start-page: 21954 year: 2017 end-page: 21961 ident: C10 article-title: A deep learning approach for intrusion detection using recurrent neural networks publication-title: Comput. Commun. – volume: 132 start-page: 377 year: 2018 end-page: 384 ident: C9 article-title: An analysis of convolutional neural networks for image classification publication-title: Procedia Comput. Sci. – volume: 132 start-page: 679 year: 2018 end-page: 688 ident: C8 article-title: Conceptual understanding of convolutional neural network-deep learning approach publication-title: Procedia Comput. Sci. – volume: 35 start-page: 1457 year: 2012 end-page: 1471 article-title: Feature selection for optimizing traffic classification publication-title: Comput. Commun. – start-page: 1573 year: 2009 end-page: 1579 – volume: 80 start-page: 1021 year: 2014 end-page: 1036 article-title: Internet traffic clustering with side information publication-title: J. Comput. Syst. Sci. – volume: 132 start-page: 81 year: 2018 end-page: 98 article-title: An efficient feature generation approach based on deep learning and feature selection techniques for traffic classification publication-title: Comput. Net. – volume: 132 start-page: 377 year: 2018 end-page: 384 article-title: An analysis of convolutional neural networks for image classification publication-title: Procedia Comput. Sci. – volume: 132 start-page: 679 year: 2018 end-page: 688 article-title: Conceptual understanding of convolutional neural network‐deep learning approach publication-title: Procedia Comput. Sci. – volume: 96 start-page: 1752 issue: 2 year: 2017 end-page: 1769 article-title: An intelligent secured and energy efficient routing algorithm for MANETs publication-title: Wirel. Pers. Commun. – volume: 9 start-page: 1035 issue: 7 year: 2010 end-page: 1048 article-title: Uncertainty modeling and reduction in MANETs publication-title: IEEE Trans. Mob. Comput. – volume: 67 start-page: 94 year: 2018 end-page: 105 article-title: Dealing with high‐dimensional class‐imbalanced datasets: embedded feature selection for SVM classification publication-title: Appl. Soft Comput. – volume: 5 start-page: 21954 year: 2017 end-page: 21961 article-title: A deep learning approach for intrusion detection using recurrent neural networks publication-title: Comput. Commun. – volume: 65 start-page: 67 year: 2018 end-page: 78 article-title: A novel network security algorithm based on improved support vector machine from smart city perspective publication-title: Comput. Electr. Eng. – volume: 26 start-page: 1167 issue: 3 year: 2014 end-page: 1178 article-title: An intelligent neuro‐fuzzy temporal knowledge representation model for mining temporal patterns publication-title: J. Intell. Fuzzy Syst. – volume: 73 start-page: 137 year: 2018 end-page: 155 article-title: Network intrusion detection system based on recursive feature addition and bigram technique publication-title: Comput. Secur. – volume: 88 start-page: 249 year: 2017 end-page: 257 article-title: A feature reduced intrusion detection system using ANN classifier publication-title: Expert Syst. Appl. – volume: 220 start-page: 221 year: 2017 end-page: 235 article-title: Arrhythmia classification using Mahalanobis distance based improved fuzzy C‐means clustering for mobile health monitoring systems publication-title: Neurocomputing – volume: 22 start-page: 10839 year: 2019 end-page: 10848 article-title: A rule‐based delay constrained energy efficient routing technique for wireless sensor networks publication-title: Cluster Comput. – year: 1995 – volume: 68 start-page: 62 year: 2018 end-page: 75 article-title: Energy‐efficient grid‐based routing algorithm using intelligent fuzzy rules for wireless sensor networks publication-title: Comput. Electr. Eng. – volume: 271 start-page: 1 issue: 1 year: 2013 end-page: 16 article-title: Intelligent feature selection and classification techniques for intrusion detection in networks: a survey publication-title: EURASIP J. Wirel. Commun. Netw. – volume: 355 start-page: 1752 year: 2018 end-page: 1779 article-title: Adaptive and online network intrusion detection system using clustering and extreme learning machines publication-title: J. Franklin Inst. – volume: 95 start-page: 272 year: 2018 end-page: 279 article-title: A data reduction strategy and its application on the scan and backscatter detection using rule‐based classifiers publication-title: Expert Syst. Appl. – volume: 136 start-page: 37 year: 2018 end-page: 50 article-title: A hybrid intrusion detection system based on ABC–AFS algorithm for misuse and anomaly detection publication-title: Comput. Netw. – volume: 36 start-page: 156 year: 2014 end-page: 169 article-title: An optimal and stable feature selection approach for traffic classification based on multi‐criterion fusion publication-title: Future Gener. Comput. Syst. – volume: 309 start-page: 683 year: 2017 end-page: 694 article-title: A neuro‐fuzzy classification technique using dynamic clustering and GSS rule generation publication-title: J. Comput. Appl. Math. – start-page: 1 year: 2000 end-page: 11 – volume: 22 start-page: 423 issue: 1 year: 2019 end-page: 434 article-title: Detection of DoS attacks in cloud networks using an intelligent rule‐based classification system publication-title: Cluster Comput. – volume: 6 start-page: 1 year: 2012 end-page: 34 article-title: A modular machine learning system for flow‐level traffic classification in large network's publication-title: ACM Trans. Knowl. Discov. Data – volume: 24 start-page: 1343 issue: 4 year: 2018 end-page: 1360 article-title: Energy efficient and secured distributed data dissemination using hop by authentication in WSN publication-title: Wirel. Netw. – year: 2013 – ident: e_1_2_7_3_1 doi: 10.1016/j.jfranklin.2017.06.006 – ident: e_1_2_7_15_1 doi: 10.1016/j.jcss.2014.02.008 – ident: e_1_2_7_17_1 doi: 10.1016/j.cose.2017.10.011 – ident: e_1_2_7_29_1 doi: 10.1016/j.compeleceng.2018.03.036 – ident: e_1_2_7_19_1 doi: 10.1109/IJCNN.2009.5178804 – ident: e_1_2_7_18_1 doi: 10.1016/j.comnet.2018.01.007 – ident: e_1_2_7_23_1 doi: 10.1016/j.neucom.2016.08.042 – ident: e_1_2_7_5_1 doi: 10.1016/j.asoc.2018.02.051 – ident: e_1_2_7_6_1 doi: 10.7551/mitpress/3905.001.0001 – ident: e_1_2_7_21_1 doi: 10.3233/IFS-130803 – ident: e_1_2_7_26_1 doi: 10.1007/s11276-017-1549-3 – ident: e_1_2_7_10_1 doi: 10.1016/j.procs.2018.05.198 – ident: e_1_2_7_9_1 doi: 10.1016/j.procs.2018.05.069 – ident: e_1_2_7_7_1 – volume: 5 start-page: 21954 year: 2017 ident: e_1_2_7_11_1 article-title: A deep learning approach for intrusion detection using recurrent neural networks publication-title: Comput. Commun. – ident: e_1_2_7_2_1 doi: 10.1016/j.comnet.2018.02.028 – volume: 271 start-page: 1 issue: 1 year: 2013 ident: e_1_2_7_14_1 article-title: Intelligent feature selection and classification techniques for intrusion detection in networks: a survey publication-title: EURASIP J. Wirel. Commun. Netw. – ident: e_1_2_7_24_1 doi: 10.1016/j.compeleceng.2017.09.028 – volume: 96 start-page: 1752 issue: 2 year: 2017 ident: e_1_2_7_28_1 article-title: An intelligent secured and energy efficient routing algorithm for MANETs publication-title: Wirel. Pers. Commun. – ident: e_1_2_7_12_1 doi: 10.1016/j.comcom.2012.04.012 – volume-title: Mathematical statistics and data analysis year: 2013 ident: e_1_2_7_30_1 – ident: e_1_2_7_16_1 doi: 10.1016/j.future.2013.09.015 – ident: e_1_2_7_13_1 doi: 10.1007/s10586-018-2181-4 – ident: e_1_2_7_22_1 doi: 10.1016/j.cam.2016.04.023 – ident: e_1_2_7_27_1 doi: 10.1007/s10586-017-1191-y – ident: e_1_2_7_20_1 doi: 10.1145/2133360.2133364 – ident: e_1_2_7_8_1 doi: 10.1109/TMC.2010.44 – ident: e_1_2_7_4_1 doi: 10.1016/j.eswa.2017.07.005 – ident: e_1_2_7_25_1 doi: 10.1016/j.eswa.2017.11.041 |
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| Snippet | Intrusion detection systems assume a noteworthy job in the provision of security in wireless Sensor networks. The existing intrusion detection systems focus... |
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| SubjectTerms | convolution neural networks convolutional neural nets data mining decision trees dynamic recursive feature selection algorithm energy consumption false positive rate feature extraction fuzzy neural nets fuzzy temporal decision tree classification intelligent decision tree classification algorithm intelligent fuzzy temporal decision tree algorithm intrusion detection system KDD cup data set learning (artificial intelligence) network performance network trace data packet delivery ratio pattern classification Research Article security of data telecommunication security wireless sensor networks |
| Title | Intrusion detection using dynamic feature selection and fuzzy temporal decision tree classification for wireless sensor networks |
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