{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:31:15Z","timestamp":1777696275620,"version":"3.51.4"},"reference-count":39,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IDA"],"published-print":{"date-parts":[[2022,11,12]]},"abstract":"<jats:p>The Internet of Things (IoT) devices have limited resources and are vulnerable to attacks, so optimizing their network topology to resist random failures and malicious attacks has become a key issue. The scale-free network model has strong resistance to random attacks, but it is very vulnerable to malicious attacks. The existing studies mostly adopt heuristic algorithms to optimize the ability of scale-free networks to resist malicious attacks, but their high computational cost cannot meet the timeliness requirements of the real IoT. Therefore, this paper proposes an intelligent topology robustness optimization model based on a graph convolutional network (ROGCN). The model extracts the onion-like structural features of the highly robust network topology from the data set through supervised learning, and on this basis, different search strategies are designed to meet the needs of different IoT scenarios. The extensive experimental results demonstrate that ROGCN can more effectively improve the robustness of scale-free IoT networks against malicious attacks compared to two existing heuristic algorithms, with a lower computational cost.<\/jats:p>","DOI":"10.3233\/ida-216222","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T11:34:01Z","timestamp":1667561641000},"page":"1683-1701","source":"Crossref","is-referenced-by-count":8,"title":["Graph convolutional networks-based robustness optimization for scale-free Internet of Things"],"prefix":"10.1177","volume":"26","author":[{"given":"Yabin","family":"Peng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caixia","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiteng","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-216222_ref1","doi-asserted-by":"crossref","unstructured":"N. Gupta, S. Sharma, P.K. Juneja and U. Garg, SDNFV 5G-IoT: A Framework for the Next Generation 5G enabled IoT, in: IEEE International Conference on Advances in Computing, Communication & Materials (ICACCM), Dehradun, India, 2020, pp. 289\u2013294.","DOI":"10.1109\/ICACCM50413.2020.9213047"},{"issue":"1","key":"10.3233\/IDA-216222_ref2","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TWC.2015.2473165","article-title":"A provably secure, efficient, and flexible authentication scheme for ad hoc wireless sensor networks","volume":"15","author":"Chang","year":"2016","journal-title":"IEEE Transactions on Wireless Communications"},{"key":"10.3233\/IDA-216222_ref3","doi-asserted-by":"crossref","unstructured":"J. Sathishkumar and D.R. Patel, Enhanced location privacy algorithm for wireless sensor network in Internet of Things, in: International Conference on Internet of Things and Applications (IOTA), Pune, India, 2016, pp. 208\u2013212.","DOI":"10.1109\/IOTA.2016.7562723"},{"issue":"1","key":"10.3233\/IDA-216222_ref4","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3390\/s17010117","article-title":"A two-phase coverage-enhancing algorithm for hybrid wireless sensor networks","volume":"17","author":"Zhang","year":"2017","journal-title":"Sensors"},{"issue":"4","key":"10.3233\/IDA-216222_ref5","doi-asserted-by":"crossref","first-page":"2347","DOI":"10.1109\/COMST.2015.2444095","article-title":"Internet of things: A survey on enabling technologies, protocols, and applications","volume":"17","author":"Al-Fuqaha","year":"2015","journal-title":"IEEE Communications Surveys & Tutorials"},{"issue":"2","key":"10.3233\/IDA-216222_ref6","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1109\/JIOT.2018.2869847","article-title":"Internet of Things (IoT) Cybersecurity Research: A Review of Current Research Topics","volume":"6","author":"Lu","year":"2019","journal-title":"IEEE Internet of Things Journal"},{"issue":"8","key":"10.3233\/IDA-216222_ref7","doi-asserted-by":"crossref","first-page":"5103","DOI":"10.1109\/TIT.2011.2158874","article-title":"Cognitive networks achieve throughput scaling of a homogeneous network","volume":"57","author":"Jeon","year":"2011","journal-title":"IEEE Transactions on Information Theory"},{"issue":"4","key":"10.3233\/IDA-216222_ref8","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1109\/TPDS.2013.105","article-title":"Constructing limited scale-free topologies over peer-to-peer networks","volume":"25","author":"Bulut","year":"2014","journal-title":"IEEE Transactions on Parallel and Distributed Systems"},{"key":"10.3233\/IDA-216222_ref9","doi-asserted-by":"crossref","unstructured":"J. Luo, H. Feng and C. Zuo, Analysis on the invulnerability of network based on scale-free network, in: IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), Chongqing, China, 2018, pp.\u00a01519\u20131522.","DOI":"10.1109\/IAEAC.2018.8577931"},{"key":"10.3233\/IDA-216222_ref10","doi-asserted-by":"crossref","first-page":"125612","DOI":"10.1016\/j.physa.2020.125612","article-title":"Percolation on interdependent networks with cliques and weak interdependence","volume":"566","author":"Zang","year":"2021","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"2","key":"10.3233\/IDA-216222_ref11","first-page":"539","article-title":"A two-phase multiobjective evolutionary algorithm for enhancing the robustness of scale-free networks against multiple malicious attacks","volume":"47","author":"Zhou","year":"2017","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"1","key":"10.3233\/IDA-216222_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1742-5468\/2011\/01\/P01027","article-title":"Onion-like network topology enhances robustness against malicious attacks","volume":"2011","author":"Herrmann","year":"2011","journal-title":"Journal of Statistical Mechanics: Theory and Experiment"},{"key":"10.3233\/IDA-216222_ref13","doi-asserted-by":"crossref","unstructured":"P. Buesser, F. Daolio and M. Tomassini, Optimizing the robustness of scale-free networks with simulated annealing, in: Proc. 10th Int. Conf. Adapt. Natural Comput. Algorithms (ICANNGA), Slovenia, 2011, pp. 167\u2013176.","DOI":"10.1007\/978-3-642-20267-4_18"},{"key":"10.3233\/IDA-216222_ref14","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.physa.2014.05.002","article-title":"A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks","volume":"410","author":"Zhou","year":"2014","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"issue":"3","key":"10.3233\/IDA-216222_ref15","doi-asserted-by":"crossref","first-page":"1028","DOI":"10.1109\/TNET.2019.2907243","article-title":"Robustness optimization scheme with multi-population co-evolution for scale-free wireless sensor networks","volume":"27","author":"Qiu","year":"2019","journal-title":"IEEE\/ACM Transactions on Networking"},{"issue":"12","key":"10.3233\/IDA-216222_ref16","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MCOM.2017.1700247","article-title":"A data-driven robustness algorithm for the internet of things in smart cities","volume":"55","author":"Qiu","year":"2017","journal-title":"IEEE Communications Magazine"},{"issue":"4","key":"10.3233\/IDA-216222_ref17","doi-asserted-by":"crossref","first-page":"046109","DOI":"10.1103\/PhysRevE.85.046109","article-title":"Robustness of onion-like correlated networks against targeted attacks","volume":"85","author":"Tanizawa","year":"2012","journal-title":"Physical Review E Statistical Nonlinear & Soft Matter Physics"},{"issue":"4","key":"10.3233\/IDA-216222_ref18","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: Going beyond Euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Processing Magazine"},{"issue":"2","key":"10.3233\/IDA-216222_ref19","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.ins.2021.06.086","article-title":"Parameter discrepancy hypothesis: Adversarial attack for graph data","volume":"577","author":"Wu","year":"2021","journal-title":"Information Sciences"},{"issue":"3","key":"10.3233\/IDA-216222_ref20","doi-asserted-by":"crossref","first-page":"739","DOI":"10.3233\/IDA-195006","article-title":"A directed link prediction method using graph convolutional network based on social ranking theory","volume":"25","author":"Wu","year":"2021","journal-title":"Intelligent Data Analysis"},{"issue":"21","key":"10.3233\/IDA-216222_ref21","doi-asserted-by":"crossref","first-page":"5501","DOI":"10.1016\/j.physa.2013.06.063","article-title":"Complex scale-free networks with tunable power-law exponent and clustering","volume":"392","author":"Colman","year":"2013","journal-title":"Physica A: Statistical Mechanics and Its Applications"},{"issue":"2","key":"10.3233\/IDA-216222_ref22","doi-asserted-by":"crossref","first-page":"439","DOI":"10.3233\/IDA-173400","article-title":"USI-AUC: An evaluation criterion of community detection based on a novel link-prediction method","volume":"22","author":"Wu","year":"2018","journal-title":"Intelligent Data Analysis"},{"issue":"5439","key":"10.3233\/IDA-216222_ref23","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Barab\u00e1si","year":"1999","journal-title":"Science"},{"issue":"1","key":"10.3233\/IDA-216222_ref24","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MSP.2015.7","article-title":"Low-energy security: Limits and opportunities in the internet of things","volume":"13","author":"Trappe","year":"2015","journal-title":"IEEE Security & Privacy"},{"issue":"5","key":"10.3233\/IDA-216222_ref25","doi-asserted-by":"crossref","first-page":"2944","DOI":"10.1109\/TNET.2017.2713530","article-title":"Rose: Robustness strategy for scale-free wireless sensor networks","volume":"25","author":"Qiu","year":"2017","journal-title":"IEEE\/ACM Transactions on Networking"},{"issue":"5","key":"10.3233\/IDA-216222_ref26","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1177\/0020294019837991","article-title":"Assessing risks and threats with layered approach to Internet of Things security","volume":"52","author":"Aydos","year":"2019","journal-title":"Measurement & Control"},{"key":"10.3233\/IDA-216222_ref27","doi-asserted-by":"crossref","unstructured":"P. Holme, B.J. Kim, C.N. Yoon and S.K. Han, Attack vulnerability of complex networks, Physical review E 65(5) (2002), 056109.","DOI":"10.1103\/PhysRevE.65.056109"},{"key":"10.3233\/IDA-216222_ref28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2020.12.003","article-title":"Percolation on complex networks: Theory and application","volume":"907","author":"Li","year":"2021","journal-title":"Physics Reports"},{"issue":"10","key":"10.3233\/IDA-216222_ref29","doi-asserted-by":"crossref","first-page":"3838","DOI":"10.1073\/pnas.1009440108","article-title":"Mitigation of malicious attacks on networks","volume":"108","author":"Schneider","year":"2011","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"10.3233\/IDA-216222_ref30","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.physa.2018.02.173","article-title":"A heuristic algorithm for enhancing the robustness of scale-free networks based on edge classification","volume":"503","author":"Rong","year":"2018","journal-title":"Physica A: Statistical Mechanics and its Applications"},{"key":"10.3233\/IDA-216222_ref31","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.asoc.2014.08.025","article-title":"A comparative review of approaches to prevent premature convergence in GA","volume":"24","author":"Pandey","year":"2014","journal-title":"Applied Soft Computing"},{"key":"10.3233\/IDA-216222_ref32","doi-asserted-by":"crossref","unstructured":"T. Qiu, Z. Lu, K. Li, G. Xue and D.O. Wu, An Adaptive Robustness Evolution Algorithm with Self-Competition for Scale-Free Internet of Things, in: IEEE INFOCOM 2020-IEEE Conference on Computer Communications, IEEE, 2020, pp. 2106\u20132115.","DOI":"10.1109\/INFOCOM41043.2020.9155426"},{"key":"10.3233\/IDA-216222_ref33","doi-asserted-by":"crossref","first-page":"11241","DOI":"10.1038\/s41598-018-29626-w","article-title":"Onion-like networks are both robust and resilient","volume":"8","author":"Hayashi","year":"2018","journal-title":"Scientific Reports"},{"issue":"6","key":"10.3233\/IDA-216222_ref34","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Communications of the Acm"},{"key":"10.3233\/IDA-216222_ref35","doi-asserted-by":"crossref","unstructured":"T. Derr, Y. Ma and J. Tang, Signed Graph Convolutional Networks, in: IEEE International Conference on Data Mining (ICDM), Singapore, 2018, pp. 929\u2013934.","DOI":"10.1109\/ICDM.2018.00113"},{"issue":"3","key":"10.3233\/IDA-216222_ref36","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1109\/TKDE.2018.2878247","article-title":"Deep inductive graph representation learning","volume":"32","author":"Rossi","year":"2020","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"10.3233\/IDA-216222_ref40","doi-asserted-by":"crossref","unstructured":"K. He, X. Zhang, S. Ren and J. Sun, Deep Residual Learning for Image Recognition, in: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"10.3233\/IDA-216222_ref41","doi-asserted-by":"crossref","unstructured":"W. Yamany, M. Fawzy, A. Tharwat and A.E. Hassanien, Moth-flame optimization for training Multi-Layer Perceptrons, in: 2015 11th International Computer Engineering Conference (ICENCO), Cairo, Egypt, 2015, pp. 267\u2013272.","DOI":"10.1109\/ICENCO.2015.7416360"},{"key":"10.3233\/IDA-216222_ref42","doi-asserted-by":"crossref","unstructured":"M.R. Rezaei-Dastjerdehei, A. Mijani and E. Fatemizadeh, Addressing Imbalance in Multi-Label Classification Using Weighted Cross Entropy Loss Function, in: 2020 27th National and 5th International Iranian Conference on Biomedical Engineering (ICBME), Tehran, Iran, 2020, pp. 333\u2013338.","DOI":"10.1109\/ICBME51989.2020.9319440"}],"container-title":["Intelligent Data Analysis"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/IDA-216222","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:19:43Z","timestamp":1777454383000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/IDA-216222"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":39,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/ida-216222","relation":{},"ISSN":["1088-467X","1571-4128"],"issn-type":[{"value":"1088-467X","type":"print"},{"value":"1571-4128","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,12]]}}}