Detecting malicious nodes via gradient descent and support vector machine in Internet of Things
IoT devices have become much popular in our daily lives, while attackers often invade network nodes to launch various attacks. In this work, we focus on the detection of insider attacks in IoT networks. Most existing algorithms calculate the reputation of all nodes based on the routing path. However...
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| Veröffentlicht in: | Computers & electrical engineering Jg. 77; S. 339 - 353 |
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01.07.2019
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| ISSN: | 0045-7906, 1879-0755 |
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| Abstract | IoT devices have become much popular in our daily lives, while attackers often invade network nodes to launch various attacks. In this work, we focus on the detection of insider attacks in IoT networks. Most existing algorithms calculate the reputation of all nodes based on the routing path. However, they rely heavily on the assumption that different nodes in the same routing path have equal reputation, which may be not invalid in practice and cause inaccurate detection results. To solve this issue, we formulate it as a multivariate multiple linear regression problem and use the K-means classification algorithm to detect malicious nodes. Further, we optimize the routing path and design an enhanced detection scheme. Our results indicate that our proposed methods could achieve a detection accuracy rate of 90% or above in a common case, and the enhanced scheme could reach an even lower false detection rate, i.e., below 5%. |
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| AbstractList | IoT devices have become much popular in our daily lives, while attackers often invade network nodes to launch various attacks. In this work, we focus on the detection of insider attacks in IoT networks. Most existing algorithms calculate the reputation of all nodes based on the routing path. However, they rely heavily on the assumption that different nodes in the same routing path have equal reputation, which may be not invalid in practice and cause inaccurate detection results. To solve this issue, we formulate it as a multivariate multiple linear regression problem and use the K-means classification algorithm to detect malicious nodes. Further, we optimize the routing path and design an enhanced detection scheme. Our results indicate that our proposed methods could achieve a detection accuracy rate of 90% or above in a common case, and the enhanced scheme could reach an even lower false detection rate, i.e., below 5%. |
| Author | Yang, Jingxiu Liu, Liang Meng, Weizhi |
| Author_xml | – sequence: 1 givenname: Liang surname: Liu fullname: Liu, Liang email: liangliu@nuaa.edu.cn organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China – sequence: 2 givenname: Jingxiu surname: Yang fullname: Yang, Jingxiu email: yangjingxiu613zb@163.com organization: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, 211106 Nanjing, China – sequence: 3 givenname: Weizhi surname: Meng fullname: Meng, Weizhi email: weme@dtu.dk organization: Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark |
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| Keywords | K-means Internet of things Gradient descent Machine learning Malicious node detection Support vector machine Trust management |
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| References | Schölkopf, Smola (bib0024) 2002 Rikli, Alnasser (bib0008) 2016; 12 Luo, Nagarajan (bib0019) 2018 Abdelhakim, Lightfoot, Ren, Li (bib0005) 2015 Liu, Abdelhakim, Krishnamurthy, Tipper (bib0013) 2018; 4 Jan, Nanda, Liu (bib0004) 2018; 80 Zhang, Zhu, Tang, Xiong (bib0012) 2018; 74 Abdelhakim, Liu, Krishnamurthy (bib0006) 2018 Wang, Hussain, Bertino (bib0022) 2016; 27 Ahmed, Tepe (bib0011) 2017 Ayadi, Ghorbel, BenSaleh, Obeid, Abid (bib0020) 2017 Rana, Cai, Azeem, Ditta, Yu, Khuhro (bib0007) 2018; 14 Romman, Al-Bahadili (bib0010) 2016; 8 Haykin (bib0023) 2009; vol. 3 Akbani, Korkmaz, Raju (bib0016) 2008 Dromard, Roudiere, Owezarski (bib0018) 2017; 14 Chen, Tian, Lin (bib0009) 2017; 17 Nahiyan, Kaiser, Ferens, McLeod (bib0017) 2017 Liu, Abdelhakim, Krishnamurthy, Tipper (bib0021) 2018 Javed, Afzal, Sharif, Kim (bib0001) 2018; 20 Corak, Okay, Guzel, Murt, Ozdemir (bib0002) 2018 Tseng, Chiang, Chao (bib0003) 2018; 14 Li, Meng, Kwok (bib0025) 2018; 10 Kaplantzis, Shilton, Mani, Sekercioglu (bib0015) 2008 Zawaideh, Salamah (bib0014) 2019; 32 Dromard (10.1016/j.compeleceng.2019.06.013_bib0018) 2017; 14 Romman (10.1016/j.compeleceng.2019.06.013_bib0010) 2016; 8 Tseng (10.1016/j.compeleceng.2019.06.013_bib0003) 2018; 14 Jan (10.1016/j.compeleceng.2019.06.013_bib0004) 2018; 80 Chen (10.1016/j.compeleceng.2019.06.013_bib0009) 2017; 17 Zawaideh (10.1016/j.compeleceng.2019.06.013_bib0014) 2019; 32 Liu (10.1016/j.compeleceng.2019.06.013_sbref0021) 2018 Liu (10.1016/j.compeleceng.2019.06.013_bib0013) 2018; 4 Luo (10.1016/j.compeleceng.2019.06.013_sbref0019) 2018 Ahmed (10.1016/j.compeleceng.2019.06.013_sbref0011) 2017 Akbani (10.1016/j.compeleceng.2019.06.013_bib0016) 2008 Javed (10.1016/j.compeleceng.2019.06.013_bib0001) 2018; 20 Ayadi (10.1016/j.compeleceng.2019.06.013_bib0020) 2017 Schölkopf (10.1016/j.compeleceng.2019.06.013_sbref0024) 2002 Rikli (10.1016/j.compeleceng.2019.06.013_bib0008) 2016; 12 Haykin (10.1016/j.compeleceng.2019.06.013_bib0023) 2009; vol. 3 Nahiyan (10.1016/j.compeleceng.2019.06.013_sbref0017) 2017 Li (10.1016/j.compeleceng.2019.06.013_bib0025) 2018; 10 Wang (10.1016/j.compeleceng.2019.06.013_bib0022) 2016; 27 Kaplantzis (10.1016/j.compeleceng.2019.06.013_sbref0015) 2008 Corak (10.1016/j.compeleceng.2019.06.013_sbref0002) 2018 Abdelhakim (10.1016/j.compeleceng.2019.06.013_bib0005) 2015 Rana (10.1016/j.compeleceng.2019.06.013_bib0007) 2018; 14 Zhang (10.1016/j.compeleceng.2019.06.013_bib0012) 2018; 74 Abdelhakim (10.1016/j.compeleceng.2019.06.013_sbref0006) 2018 |
| References_xml | – volume: 80 start-page: 613 year: 2018 end-page: 626 ident: bib0004 article-title: A sybil attack detection scheme for a forest wildfire monitoring application publication-title: Fut Gener Comp Syst – volume: 17 start-page: 703 year: 2017 ident: bib0009 article-title: Trust model of wireless sensor networks and its application in data fusion publication-title: Sensors – start-page: 712 year: 2018 end-page: 717 ident: bib0006 article-title: Diversity for detecting routing attacks in multi-hop networks publication-title: 2018 International conference on computing, networking and communications (ICNC) – start-page: 1 year: 2018 end-page: 6 ident: bib0019 article-title: Distributed anomaly detection using autoencoder neural networks in WSN for iot publication-title: 2018 IEEE International Conference on Communications (ICC) – start-page: 1 year: 2017 end-page: 5 ident: bib0011 article-title: Recommendation trust for improved malicious node detection in ad hoc networks publication-title: IEEE 86th VTC-Fall – start-page: 335 year: 2008 end-page: 340 ident: bib0015 article-title: Detecting selective forwarding attacks in wireless sensor networks using support vector machines publication-title: IEEE ISSNIP – volume: 14 start-page: 34 year: 2017 end-page: 47 ident: bib0018 article-title: Online and scalable unsupervised network anomaly detection method publication-title: IEEE Trans Netw Serv Manag – volume: 12 year: 2016 ident: bib0008 article-title: Lightweight trust model for the detection of concealed malicious nodes in sparse wireless ad hoc networks publication-title: Int J Distrib Sens Netw – start-page: 25 year: 2017 end-page: 30 ident: bib0017 article-title: A multi-agent based cognitive approach to unsupervised feature extraction and classification for network intrusion detection publication-title: ACC'17 – volume: 27 start-page: 405 year: 2016 end-page: 418 ident: bib0022 article-title: Dictionary based secure provenance compression for wireless sensor networks publication-title: IEEE Trans Parallel Distrib Syst – volume: vol. 3 year: 2009 ident: bib0023 publication-title: Neural networks and learning machines – volume: 74 start-page: 1779 year: 2018 end-page: 1801 ident: bib0012 article-title: A novel trust management scheme based on dempster-shafer evidence theory for malicious nodes detection in wireless sensor networks publication-title: J Supercomput – start-page: 1 year: 2002 end-page: 626 ident: bib0024 article-title: Learning with Kernels: support vector machines, regularization, optimization, and beyond – volume: 10 start-page: 1 year: 2018 end-page: 16 ident: bib0025 article-title: Investigating the influence of special on-off attacks on challenge-based collaborative intrusion detection networks publication-title: Fut Internet – start-page: 2119 year: 2008 end-page: 2123 ident: bib0016 article-title: A machine learning based reputation system for defending against malicious node behavior publication-title: GLOBECOM – volume: 4 start-page: 109 year: 2018 end-page: 125 ident: bib0013 article-title: Identifying malicious nodes in multihop IoT networks using dual link technologies and unsupervised learning publication-title: Open J Internet Things – volume: 8 start-page: 29‐40 year: 2016 ident: bib0010 article-title: Performance analysis of the neighbor weight trust determination algorithm in MANETs publication-title: Int J Netw Secur Appl – volume: 14 start-page: 16 year: 2018 end-page: 24 ident: bib0007 article-title: Wireless ad hoc network: detection of malicious node by using neighbour-based authentication approach publication-title: Int J Wirel Mob Comput – volume: 14 start-page: 56 year: 2018 end-page: 78 ident: bib0003 article-title: Black hole along with other attacks in MANETs: a survey publication-title: J Inf Process Syst – volume: 32 year: 2019 ident: bib0014 article-title: An efficient weighted trust-based malicious node detection scheme for wireless sensor networks publication-title: Int J Commun Syst – start-page: 1 year: 2017 end-page: 6 ident: bib0020 article-title: Outlier detection based on data reduction in WSNs for water pipeline publication-title: SoftCOM – volume: 20 start-page: 2062 year: 2018 end-page: 2100 ident: bib0001 article-title: Internet of things (IoT) operating systems support, networking technologies, applications, and challenges: a comparative review publication-title: IEEE Commun Surv Tuts – start-page: 1 year: 2015 end-page: 6 ident: bib0005 article-title: Reliable communications over multi-hop networks under routing attacks publication-title: GLOBECOM – start-page: 1 year: 2018 end-page: 6 ident: bib0002 article-title: Comparative analysis of IoT communication protocols publication-title: 2018 International symposium on networks, computers and communications (ISNCC) – start-page: 1 year: 2018 end-page: 6 ident: bib0021 article-title: Identifying malicious nodes in Multi-hop IoT networks using diversity and unsupervised learning publication-title: IEEE International Conference on Communications – volume: 17 start-page: 703 issue: 4 year: 2017 ident: 10.1016/j.compeleceng.2019.06.013_bib0009 article-title: Trust model of wireless sensor networks and its application in data fusion publication-title: Sensors doi: 10.3390/s17040703 – volume: 14 start-page: 34 issue: 1 year: 2017 ident: 10.1016/j.compeleceng.2019.06.013_bib0018 article-title: Online and scalable unsupervised network anomaly detection method publication-title: IEEE Trans Netw Serv Manag doi: 10.1109/TNSM.2016.2627340 – start-page: 1 year: 2017 ident: 10.1016/j.compeleceng.2019.06.013_bib0020 article-title: Outlier detection based on data reduction in WSNs for water pipeline publication-title: SoftCOM – start-page: 1 year: 2015 ident: 10.1016/j.compeleceng.2019.06.013_bib0005 article-title: Reliable communications over multi-hop networks under routing attacks publication-title: GLOBECOM – volume: 27 start-page: 405 issue: 2 year: 2016 ident: 10.1016/j.compeleceng.2019.06.013_bib0022 article-title: Dictionary based secure provenance compression for wireless sensor networks publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/TPDS.2015.2402156 – volume: 20 start-page: 2062 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0001 article-title: Internet of things (IoT) operating systems support, networking technologies, applications, and challenges: a comparative review publication-title: IEEE Commun Surv Tuts doi: 10.1109/COMST.2018.2817685 – start-page: 1 year: 2017 ident: 10.1016/j.compeleceng.2019.06.013_sbref0011 article-title: Recommendation trust for improved malicious node detection in ad hoc networks – volume: 32 issue: 3 year: 2019 ident: 10.1016/j.compeleceng.2019.06.013_bib0014 article-title: An efficient weighted trust-based malicious node detection scheme for wireless sensor networks publication-title: Int J Commun Syst doi: 10.1002/dac.3878 – volume: 14 start-page: 16 issue: 1 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0007 article-title: Wireless ad hoc network: detection of malicious node by using neighbour-based authentication approach publication-title: Int J Wirel Mob Comput doi: 10.1504/IJWMC.2018.090007 – start-page: 1 year: 2002 ident: 10.1016/j.compeleceng.2019.06.013_sbref0024 – volume: vol. 3 year: 2009 ident: 10.1016/j.compeleceng.2019.06.013_bib0023 – volume: 74 start-page: 1779 issue: 4 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0012 article-title: A novel trust management scheme based on dempster-shafer evidence theory for malicious nodes detection in wireless sensor networks publication-title: J Supercomput doi: 10.1007/s11227-017-2150-3 – volume: 80 start-page: 613 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0004 article-title: A sybil attack detection scheme for a forest wildfire monitoring application publication-title: Fut Gener Comp Syst doi: 10.1016/j.future.2016.05.034 – volume: 14 start-page: 56 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0003 article-title: Black hole along with other attacks in MANETs: a survey publication-title: J Inf Process Syst – start-page: 1 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_sbref0021 article-title: Identifying malicious nodes in Multi-hop IoT networks using diversity and unsupervised learning – volume: 8 start-page: 29‐40 issue: 4 year: 2016 ident: 10.1016/j.compeleceng.2019.06.013_bib0010 article-title: Performance analysis of the neighbor weight trust determination algorithm in MANETs publication-title: Int J Netw Secur Appl – start-page: 25 year: 2017 ident: 10.1016/j.compeleceng.2019.06.013_sbref0017 article-title: A multi-agent based cognitive approach to unsupervised feature extraction and classification for network intrusion detection – start-page: 1 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_sbref0002 article-title: Comparative analysis of IoT communication protocols – start-page: 335 year: 2008 ident: 10.1016/j.compeleceng.2019.06.013_sbref0015 article-title: Detecting selective forwarding attacks in wireless sensor networks using support vector machines – volume: 4 start-page: 109 issue: 1 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0013 article-title: Identifying malicious nodes in multihop IoT networks using dual link technologies and unsupervised learning publication-title: Open J Internet Things – start-page: 712 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_sbref0006 article-title: Diversity for detecting routing attacks in multi-hop networks – volume: 10 start-page: 1 issue: 1 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_bib0025 article-title: Investigating the influence of special on-off attacks on challenge-based collaborative intrusion detection networks publication-title: Fut Internet – start-page: 1 year: 2018 ident: 10.1016/j.compeleceng.2019.06.013_sbref0019 article-title: Distributed anomaly detection using autoencoder neural networks in WSN for iot – start-page: 2119 year: 2008 ident: 10.1016/j.compeleceng.2019.06.013_bib0016 article-title: A machine learning based reputation system for defending against malicious node behavior publication-title: GLOBECOM – volume: 12 issue: 7 year: 2016 ident: 10.1016/j.compeleceng.2019.06.013_bib0008 article-title: Lightweight trust model for the detection of concealed malicious nodes in sparse wireless ad hoc networks publication-title: Int J Distrib Sens Netw doi: 10.1177/1550147716657246 |
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| SubjectTerms | Algorithms Design optimization Gradient descent Internet of Things K-means Machine learning Malicious node detection Nodes Route planning Support vector machine Support vector machines Trust management |
| Title | Detecting malicious nodes via gradient descent and support vector machine in Internet of Things |
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