{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T12:30:22Z","timestamp":1742646622258,"version":"3.37.3"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,7,1]],"date-time":"2024-07-01T00:00:00Z","timestamp":1719792000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Internet of Things"],"published-print":{"date-parts":[[2024,7]]},"DOI":"10.1016\/j.iot.2024.101214","type":"journal-article","created":{"date-parts":[[2024,5,7]],"date-time":"2024-05-07T16:47:11Z","timestamp":1715100431000},"page":"101214","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks"],"prefix":"10.1016","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1481-683X","authenticated-orcid":false,"given":"Mortada","family":"Termos","sequence":"first","affiliation":[]},{"given":"Zakariya","family":"Ghalmane","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0114-210X","authenticated-orcid":false,"given":"Mohamed-el-Amine","family":"Brahmia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2284-5034","authenticated-orcid":false,"given":"Ahmad","family":"Fadlallah","sequence":"additional","affiliation":[]},{"given":"Ali","family":"Jaber","sequence":"additional","affiliation":[]},{"given":"Mourad","family":"Zghal","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"19","key":"10.1016\/j.iot.2024.101214_b1","doi-asserted-by":"crossref","DOI":"10.3390\/s22197433","article-title":"Internet of things: Security and solutions survey","volume":"22","author":"Sadhu","year":"2022","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.iot.2024.101214_b2","doi-asserted-by":"crossref","DOI":"10.3390\/s23052415","article-title":"Survey on intrusion detection systems based on machine learning techniques for the protection of critical infrastructure","volume":"23","author":"Pinto","year":"2023","journal-title":"Sensors"},{"key":"10.1016\/j.iot.2024.101214_b3","doi-asserted-by":"crossref","first-page":"121173","DOI":"10.1109\/ACCESS.2022.3220622","article-title":"Machine and deep learning solutions for intrusion detection and prevention in IoTs: A survey","volume":"10","author":"Jayalaxmi","year":"2022","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b4","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1007\/s11036-022-01937-3","article-title":"Internet of things (IoT) security intelligence: A comprehensive overview, machine learning solutions and research directions","volume":"28","author":"Sarker","year":"2023","journal-title":"Mob. Netw. Appl."},{"year":"2018","series-title":"Networks","author":"Newman","key":"10.1016\/j.iot.2024.101214_b5"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b6","doi-asserted-by":"crossref","first-page":"72","DOI":"10.3390\/forecast4010005","article-title":"SIMLR: Machine learning inside the SIR model for COVID-19 forecasting","volume":"4","author":"Vega","year":"2022","journal-title":"Forecasting"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b7","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1007\/s13278-019-0591-9","article-title":"Immunization of networks with non-overlapping community structure","volume":"9","author":"Ghalmane","year":"2019","journal-title":"Soc. Netw. Anal. Min."},{"key":"10.1016\/j.iot.2024.101214_b8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2023.04.002","article-title":"The structure and dynamics of networks with higher order interactions","volume":"1018","author":"Boccaletti","year":"2023","journal-title":"Phys. Rep."},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b9","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1186\/s42400-019-0038-7","article-title":"Survey of intrusion detection systems: techniques, datasets and challenges","volume":"2","author":"Khraisat","year":"2019","journal-title":"Cybersecurity"},{"issue":"9","key":"10.1016\/j.iot.2024.101214_b10","doi-asserted-by":"crossref","DOI":"10.1145\/3472753","article-title":"A survey on data-driven network intrusion detection","volume":"54","author":"Chou","year":"2021","journal-title":"ACM Comput. Surv."},{"issue":"16","key":"10.1016\/j.iot.2024.101214_b11","doi-asserted-by":"crossref","DOI":"10.3390\/app12168162","article-title":"A survey of CNN-based network intrusion detection","volume":"12","author":"Mohammadpour","year":"2022","journal-title":"Appl. Sci."},{"issue":"12","key":"10.1016\/j.iot.2024.101214_b12","first-page":"1271","article-title":"Intrusion detection algorithm based on convolutional neural network","volume":"37","author":"Fan","year":"2017","journal-title":"DEStech Trans. Eng. Technol. Res. ICETA"},{"issue":"3","key":"10.1016\/j.iot.2024.101214_b13","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1089\/big.2020.0263","article-title":"A convolutional neural network for improved anomaly-based network intrusion detection","volume":"9","author":"Al-Turaiki","year":"2021","journal-title":"Big Data"},{"issue":"2","key":"10.1016\/j.iot.2024.101214_b14","doi-asserted-by":"crossref","first-page":"1823","DOI":"10.32604\/cmc.2021.012877","article-title":"Few-shot learning for discovering anomalous behaviors in edge networks","volume":"69","author":"Gamal","year":"2021","journal-title":"Comput. Mater. Continua"},{"key":"10.1016\/j.iot.2024.101214_b15","doi-asserted-by":"crossref","first-page":"21954","DOI":"10.1109\/ACCESS.2017.2762418","article-title":"A deep learning approach for intrusion detection using recurrent neural networks","volume":"5","author":"Yin","year":"2017","journal-title":"IEEE Access"},{"key":"10.1016\/j.iot.2024.101214_b16","doi-asserted-by":"crossref","unstructured":"S. Althubiti, W. Nick, J. Mason, X. Yuan, A. Esterline, Applying Long Short-Term Memory Recurrent Neural Network for Intrusion Detection, in: SoutheastCon 2018, 2018, pp. 1\u20135, http:\/\/dx.doi.org\/10.1109\/SECON.2018.8478898.","DOI":"10.1109\/SECON.2018.8478898"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b17","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s40537-021-00448-4","article-title":"Intrusion detection systems using long short-term memory (LSTM)","volume":"8","author":"Laghrissi","year":"2021","journal-title":"J. Big Data"},{"key":"10.1016\/j.iot.2024.101214_b18","doi-asserted-by":"crossref","first-page":"62722","DOI":"10.1109\/ACCESS.2022.3176317","article-title":"Design and development of RNN anomaly detection model for IoT networks","volume":"10","author":"Ullah","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.iot.2024.101214_b19","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/8890306","article-title":"DL-IDS: Extracting features using CNN-LSTM hybrid network for intrusion detection system","volume":"2020","author":"Sun","year":"2020","journal-title":"Secur. Commun. Netw."},{"issue":"5","key":"10.1016\/j.iot.2024.101214_b20","doi-asserted-by":"crossref","first-page":"567","DOI":"10.1007\/s10207-019-00475-6","article-title":"A novel graph-based approach for IoT botnet detection","volume":"19","author":"Nguyen","year":"2020","journal-title":"Int. J. Inf. Secur."},{"key":"10.1016\/j.iot.2024.101214_b21","doi-asserted-by":"crossref","first-page":"99166","DOI":"10.1109\/ACCESS.2021.3094183","article-title":"Botnet detection approach using graph-based machine learning","volume":"9","author":"Alharbi","year":"2021","journal-title":"IEEE Access"},{"key":"10.1016\/j.iot.2024.101214_b22","doi-asserted-by":"crossref","first-page":"39213","DOI":"10.1109\/ACCESS.2023.3268519","article-title":"G-IDCS: Graph-based intrusion detection and classification system for CAN protocol","volume":"11","author":"Park","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.iot.2024.101214_b23","series-title":"2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress","first-page":"0471","article-title":"Intrusion detection system for IoT based on complex networks and machine learning","author":"Termos","year":"2023"},{"issue":"3","key":"10.1016\/j.iot.2024.101214_b24","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1007\/s11571-020-09626-1","article-title":"Complex networks and deep learning for EEG signal analysis","volume":"15","author":"Gao","year":"2021","journal-title":"Cogn. Neurodyn."},{"key":"10.1016\/j.iot.2024.101214_b25","series-title":"Self-Organizing Coalitions for Managing Complexity: Agent-Based Simulation of Evolutionary Game Theory Models using Dynamic Social Networks for Interdisciplinary Applications","first-page":"35","article-title":"Complex networks","author":"Burguillo","year":"2018"},{"key":"10.1016\/j.iot.2024.101214_b26","series-title":"Computational Intelligence: Theories, Applications and Future Directions - Volume I","first-page":"287","article-title":"A comparative analysis of community detection algorithms on social networks","author":"Nerurkar","year":"2019"},{"key":"10.1016\/j.iot.2024.101214_b27","doi-asserted-by":"crossref","first-page":"129717","DOI":"10.1109\/ACCESS.2020.3009525","article-title":"Interplay between hierarchy and centrality in complex networks","volume":"8","author":"Rajeh","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.iot.2024.101214_b28","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevE.78.046110","article-title":"Benchmark graphs for testing community detection algorithms","volume":"78","author":"Lancichinetti","year":"2008","journal-title":"Phys. Rev. E"},{"key":"10.1016\/j.iot.2024.101214_b29","series-title":"2011 IEEE 27th International Conference on Data Engineering","first-page":"51","article-title":"Efficient core decomposition in massive networks","author":"Cheng","year":"2011"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b30","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1140\/epjds\/s13688-019-0195-7","article-title":"Centrality in modular networks","volume":"8","author":"Ghalmane","year":"2019","journal-title":"EPJ Data Sci."},{"key":"10.1016\/j.iot.2024.101214_b31","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.physa.2016.01.066","article-title":"Centrality measures for networks with community structure","volume":"452","author":"Gupta","year":"2016","journal-title":"Phys. A"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b32","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1109\/TNSE.2020.3049068","article-title":"Measuring node contribution to community structure with modularity vitality","volume":"8","author":"Magelinski","year":"2021","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"10.1016\/j.iot.2024.101214_b33","doi-asserted-by":"crossref","first-page":"7390","DOI":"10.1109\/ACCESS.2018.2794324","article-title":"Identifying influential nodes based on community structure to speed up the dissemination of information in complex network","volume":"6","author":"Tulu","year":"2018","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.iot.2024.101214_b34","doi-asserted-by":"crossref","first-page":"10088","DOI":"10.1038\/s41598-021-89549-x","article-title":"Characterizing the interactions between classical and community-aware centrality measures in complex networks","volume":"11","author":"Rajeh","year":"2021","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.iot.2024.101214_b35","doi-asserted-by":"crossref","first-page":"1273","DOI":"10.1007\/s11135-022-01416-7","article-title":"Comparative evaluation of community-aware centrality measures","volume":"57","author":"Rajeh","year":"2023","journal-title":"Quality & Quantity"},{"key":"10.1016\/j.iot.2024.101214_b36","series-title":"2020 International Workshop on Electronic Communication and Artificial Intelligence","first-page":"98","article-title":"LSTM and GRU neural network performance comparison study: Taking yelp review dataset as an example","author":"Yang","year":"2020"},{"issue":"4","key":"10.1016\/j.iot.2024.101214_b37","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: a review","volume":"33","author":"Ismail Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"year":"2019","series-title":"ToN IoT Datasets","author":"Moustafa","key":"10.1016\/j.iot.2024.101214_b38"},{"key":"10.1016\/j.iot.2024.101214_b39","first-page":"108","article-title":"Toward generating a new intrusion detection dataset and intrusion traffic characterization","volume":"1","author":"Sharafaldin","year":"2018","journal-title":"ICISSp"},{"key":"10.1016\/j.iot.2024.101214_b40","doi-asserted-by":"crossref","DOI":"10.1016\/j.bdr.2022.100359","article-title":"Evaluating standard feature sets towards increased generalisability and explainability of ML-based network intrusion detection","volume":"30","author":"Sarhan","year":"2022","journal-title":"Big Data Res."}],"container-title":["Internet of Things"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2542660524001550?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2542660524001550?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,3]],"date-time":"2024-10-03T13:44:06Z","timestamp":1727963046000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2542660524001550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":40,"alternative-id":["S2542660524001550"],"URL":"https:\/\/doi.org\/10.1016\/j.iot.2024.101214","relation":{},"ISSN":["2542-6605"],"issn-type":[{"type":"print","value":"2542-6605"}],"subject":[],"published":{"date-parts":[[2024,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"GDLC: A new Graph Deep Learning framework based on centrality measures for intrusion detection in IoT networks","name":"articletitle","label":"Article Title"},{"value":"Internet of Things","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.iot.2024.101214","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101214"}}