{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,5,31]],"date-time":"2024-05-31T00:24:01Z","timestamp":1717115041636},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T00:00:00Z","timestamp":1711152000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Computing"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s00607-024-01269-y","type":"journal-article","created":{"date-parts":[[2024,3,23]],"date-time":"2024-03-23T10:01:47Z","timestamp":1711188107000},"page":"1933-1962","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Matyas\u2013Meyer Oseas based device profiling for anomaly detection via deep reinforcement learning (MMODPAD-DRL) in zero trust security network"],"prefix":"10.1007","volume":"106","author":[{"given":"Rajesh Kumar","family":"Dhanaraj","sequence":"first","affiliation":[]},{"given":"Anamika","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Anand","family":"Nayyar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,23]]},"reference":[{"key":"1269_CR1","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.iotcps.2022.09.001","volume":"2","author":"R Sharma","year":"2022","unstructured":"Sharma R, Vill\u00e1nyi B (2022) Consistent round hash optimized SRP-6a-based end-to-end mutual authentication for secure data transfer in industry 4.0. Internet Things Cyber-Phys Syst 2:170\u2013179","journal-title":"Internet Things Cyber-Phys Syst"},{"key":"1269_CR2","doi-asserted-by":"publisher","first-page":"45893","DOI":"10.1109\/ACCESS.2022.3169137","volume":"10","author":"TH Szymanski","year":"2022","unstructured":"Szymanski TH (2022) The \u201ccyber security via determinism\u201d paradigm for a quantum safe zero trust deterministic internet of things (IoT). IEEE Access 10:45893\u201345930","journal-title":"IEEE Access"},{"issue":"21","key":"1269_CR3","doi-asserted-by":"publisher","first-page":"8543","DOI":"10.3390\/s22218543","volume":"22","author":"S Sun","year":"2022","unstructured":"Sun S, Liu C, Zhu Y, He H, Xiao S, Wen J (2022) Deep reinforcement learning for the detection of abnormal data in smart meters. Sensors 22(21):8543","journal-title":"Sensors"},{"key":"1269_CR4","doi-asserted-by":"publisher","first-page":"109068","DOI":"10.1016\/j.comnet.2022.109068","volume":"212","author":"P Garc\u00eda-Teodoro","year":"2022","unstructured":"Garc\u00eda-Teodoro P, Camacho J, Maci\u00e1-Fern\u00e1ndez G, G\u00f3mez-Hern\u00e1ndez JA, L\u00f3pez-Mar\u00edn VJ (2022) A novel zero-trust network access control scheme based on the security profile of devices and users. Comput Netw 212:109068","journal-title":"Comput Netw"},{"key":"1269_CR5","doi-asserted-by":"publisher","first-page":"23","DOI":"10.17781\/P002537","volume":"8","author":"MA Muhammad","year":"2019","unstructured":"Muhammad MA, Ayesh A (2019) A behaviour profiling based technique for network access control systems. Int J Cyber-Secur Digit Forens (IJCSDF) 8:23\u201330","journal-title":"Int J Cyber-Secur Digit Forens (IJCSDF)"},{"key":"1269_CR6","doi-asserted-by":"publisher","first-page":"109358","DOI":"10.1016\/j.comnet.2022.109358","volume":"217","author":"K Ramezanpour","year":"2022","unstructured":"Ramezanpour K, Jagannath J (2022) Intelligent zero trust architecture for 5G\/6G networks: principles, challenges, and the role of machine learning in the context of O-RAN. Comput Netw 217:109358","journal-title":"Comput Netw"},{"issue":"18","key":"1269_CR7","doi-asserted-by":"publisher","first-page":"11213","DOI":"10.3390\/su141811213","volume":"14","author":"S Sarkar","year":"2022","unstructured":"Sarkar S, Choudhary G, Shandilya SK, Hussain A, Kim H (2022) Security of zero trust networks in cloud computing: a comparative review. Sustainability 14(18):11213","journal-title":"Sustainability"},{"key":"1269_CR8","doi-asserted-by":"crossref","unstructured":"Tien CW, Huang TY, Chen PC, Wang JH (2020) Automatic device identification and anomaly detection with machine learning techniques in smart factories. In:\u00a02020 IEEE international conference on big data (big data). IEEE, pp 3539\u20133544","DOI":"10.1109\/BigData50022.2020.9378168"},{"key":"1269_CR9","first-page":"1","volume":"2021","author":"Q Ma","year":"2021","unstructured":"Ma Q, Sun C, Cui B (2021) A novel model for anomaly detection in network traffic based on support vector machine and clustering. Secur Commun Netw 2021:1\u201311","journal-title":"Secur Commun Netw"},{"key":"1269_CR10","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10199-5","author":"S Li","year":"2022","unstructured":"Li S, Iqbal M, Saxena N (2022) Future industry internet of things with zero-trust security. Inf Syst Front. https:\/\/doi.org\/10.1007\/s10796-021-10199-5","journal-title":"Inf Syst Front"},{"key":"1269_CR11","unstructured":"Pang G, van den Hengel A, Shen C, Cao L (2020) Deep reinforcement learning for unknown anomaly detection. arXiv:2009.06847"},{"key":"1269_CR12","doi-asserted-by":"publisher","first-page":"124017","DOI":"10.1007\/s10796-021-10199-5","volume":"10","author":"K Arshad","year":"2022","unstructured":"Arshad K, Ali RF, Muneer A, Aziz IA, Naseer S, Khan NS, Taib SM (2022) Deep reinforcement learning for anomaly detection: a systematic review. IEEE Access 10:124017\u2013124035. https:\/\/doi.org\/10.1109\/ACCESS.2022.3224023","journal-title":"IEEE Access"},{"key":"1269_CR13","doi-asserted-by":"crossref","unstructured":"Dadkhah S, Mahdikhani H, Danso PK, Zohourian A, Truong KA, Ghorbani AA (2022) Towards the development of a realistic multidimensional IoT profiling dataset. In: 2022 19th annual international conference on privacy, security & trust (PST). IEEE, pp 1\u201311","DOI":"10.1109\/PST55820.2022.9851966"},{"issue":"10","key":"1269_CR14","doi-asserted-by":"publisher","first-page":"155013292211337","DOI":"10.1177\/15501329221133765","volume":"18","author":"S Han","year":"2022","unstructured":"Han S, Wu Q, Yang Y (2022) Machine learning for Internet of things anomaly detection under low-quality data. Int J Distrib Sens Netw 18(10):15501329221133764","journal-title":"Int J Distrib Sens Netw"},{"key":"1269_CR15","doi-asserted-by":"crossref","unstructured":"Muhammad M, Daniel Ani U, Abdullahi AA, Radanliev P (2021) Device-type profiling for network access control systems using clustering-based multivariate gaussian outlier score. In: The 5th international conference on future networks & distributed systems, pp 270\u2013279","DOI":"10.1145\/3508072.3508113"},{"key":"1269_CR16","doi-asserted-by":"publisher","first-page":"100568","DOI":"10.1016\/j.iot.2022.100568","volume":"19","author":"A Chatterjee","year":"2022","unstructured":"Chatterjee A, Ahmed BS (2022) IoT anomaly detection methods and applications: a survey. Internet Things 19:100568","journal-title":"Internet Things"},{"issue":"1","key":"1269_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13677-022-00300-x","volume":"11","author":"Y Xie","year":"2022","unstructured":"Xie Y, Zhang K, Kou H, Mokarram MJ (2022) Private anomaly detection of student health conditions based on wearable sensors in mobile cloud computing. J Cloud Comput 11(1):1\u201312","journal-title":"J Cloud Comput"},{"key":"1269_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.jjimei.2022.100094","author":"S-SC Vinay Singh","year":"2022","unstructured":"Vinay Singh S-SC (2022) How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries\u2013a review and research agenda. Int J Inf Manag Data Insights. https:\/\/doi.org\/10.1016\/j.jjimei.2022.100094","journal-title":"Int J Inf Manag Data Insights"},{"issue":"11","key":"1269_CR19","doi-asserted-by":"publisher","first-page":"e1010695","DOI":"10.1371\/journal.pcbi.1010695","volume":"18","author":"NJ Treloar","year":"2022","unstructured":"Treloar NJ, Braniff N, Ingalls B, Barnes CP (2022) Deep reinforcement learning for optimal experimental design in biology. PLoS Comput Biol 18(11):e1010695","journal-title":"PLoS Comput Biol"},{"key":"1269_CR20","doi-asserted-by":"publisher","first-page":"108668","DOI":"10.1016\/j.comnet.2021.108668","volume":"203","author":"O Hireche","year":"2022","unstructured":"Hireche O, Benza\u00efd C, Taleb T (2022) Deep data plane programming and AI for zero-trust self-driven networking in beyond 5G. Comput Netw 203:108668","journal-title":"Comput Netw"},{"key":"1269_CR21","doi-asserted-by":"crossref","unstructured":"Guembe B, Azeta A, Osamor V (2022) Explainable artificial intelligence, the fourth pillar of zero trust security. Available at SSRN 4331547","DOI":"10.2139\/ssrn.4331547"},{"key":"1269_CR22","doi-asserted-by":"publisher","first-page":"114488","DOI":"10.1016\/j.eswa.2020.114488","volume":"168","author":"H Kwon","year":"2021","unstructured":"Kwon H, Lee S, Jeong D (2021) User profiling via application usage pattern on digital devices for digital forensics. Expert Syst Appl 168:114488","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1269_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00346-1","volume":"7","author":"K Al Jallad","year":"2020","unstructured":"Al Jallad K, Aljnidi M, Desouki MS (2020) Anomaly detection optimization using big data and deep learning to reduce false-positive. J Big Data 7(1):1\u201312","journal-title":"J Big Data"},{"issue":"9","key":"1269_CR24","doi-asserted-by":"publisher","first-page":"5728","DOI":"10.1109\/TII.2022.3155656","volume":"18","author":"S De","year":"2022","unstructured":"De S, Bermudez-Edo M, Xu H, Cai Z (2022) Deep generative models in the industrial internet of things: a survey. IEEE Trans Industr Inf 18(9):5728\u20135737","journal-title":"IEEE Trans Industr Inf"},{"key":"1269_CR25","doi-asserted-by":"publisher","first-page":"108693","DOI":"10.1016\/j.comnet.2021.108693","volume":"204","author":"V Rey","year":"2022","unstructured":"Rey V, S\u00e1nchez PMS, Celdr\u00e1n AH, Bovet G (2022) Federated learning for malware detection in IoT devices. Comput Netw 204:108693","journal-title":"Comput Netw"},{"issue":"1","key":"1269_CR26","first-page":"79","volume":"10","author":"AA Arabi","year":"2022","unstructured":"Arabi AA, Nyamasvisva TE, Valloo S (2022) Zero trust security implementation considerations in decentralised network resources for institutions of higher learning. Int J Infrastructure Res Manag 10(1):79\u201390. https:\/\/iukl.edu.my\/rmc\/publications\/ijirm\/","journal-title":"Infrastructure Res Manag"},{"key":"1269_CR27","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6476274","volume":"2022","author":"Y He","year":"2022","unstructured":"He Y, Huang D, Chen L, Ni Y, Ma X (2022) A survey on zero trust architecture: challenges and future trends. Wirel Commun Mob Com 2022:6476274. https:\/\/doi.org\/10.1155\/2022\/6476274","journal-title":"Wirel Commun Mob Com"},{"issue":"12","key":"1269_CR28","doi-asserted-by":"publisher","first-page":"9395","DOI":"10.1016\/j.aej.2022.02.063","volume":"61","author":"YK Saheed","year":"2022","unstructured":"Saheed YK, Abiodun AI, Misra S, Holone MK, Colomo-Palacios R (2022) A machine learning-based intrusion detection for detecting internet of things network attacks. Alex Eng J 61(12):9395\u20139409","journal-title":"Alex Eng J"},{"issue":"10","key":"1269_CR29","doi-asserted-by":"publisher","first-page":"1604","DOI":"10.3390\/electronics11101604","volume":"11","author":"K Lakshmanna","year":"2022","unstructured":"Lakshmanna K, Kaluri R, Gundluru N, Alzamil ZS, Rajput DS, Khan AA, Alhussen A (2022) A review on deep learning techniques for IoT data. Electronics 11(10):1604","journal-title":"Electronics"},{"key":"1269_CR30","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9896689","volume":"2022","author":"L Fang","year":"2022","unstructured":"Fang L, Wu C, Kang Y, Ou W, Zhou D, Ye J (2022) Zero-trust-based protection scheme for users in internet of vehicles. Secur Commun Netw 2022:9896689. https:\/\/doi.org\/10.1155\/2022\/9896689","journal-title":"Secur Commun Netw"},{"key":"1269_CR31","doi-asserted-by":"publisher","first-page":"118498","DOI":"10.1109\/ACCESS.2022.3220852","volume":"10","author":"Y Goh","year":"2022","unstructured":"Goh Y, Yun J, Jung D, Chung JM (2022) Secure trust-based delegated consensus for blockchain frameworks using deep reinforcement learning. IEEE Access 10:118498\u2013118511","journal-title":"IEEE Access"},{"issue":"5","key":"1269_CR32","doi-asserted-by":"publisher","first-page":"3170","DOI":"10.1109\/JIOT.2020.3013306","volume":"8","author":"T Han","year":"2020","unstructured":"Han T, Muhammad K, Hussain T, Lloret J, Baik SW (2020) An efficient deep learning framework for intelligent energy management in IoT networks. IEEE Internet Things J 8(5):3170\u20133179","journal-title":"IEEE Internet Things J"},{"key":"1269_CR33","doi-asserted-by":"publisher","first-page":"66374","DOI":"10.1109\/ACCESS.2022.3185049","volume":"10","author":"M Alabadi","year":"2022","unstructured":"Alabadi M, Habbal A, Wei X (2022) Industrial internet of things: requirements, architecture, challenges, and future research directions. IEEE Access 10:66374\u201366400. https:\/\/doi.org\/10.1109\/ACCESS.2022.3185049","journal-title":"IEEE Access"},{"issue":"4","key":"1269_CR34","doi-asserted-by":"publisher","first-page":"71","DOI":"10.3390\/jsan11040071","volume":"11","author":"G Rathee","year":"2022","unstructured":"Rathee G, Kerrache CA, Ferrag MA (2022) A blockchain-based intrusion detection system using viterbi algorithm and indirect trust for iiot systems. J Sens Actuator Netw 11(4):71","journal-title":"J Sens Actuator Netw"},{"key":"1269_CR35","doi-asserted-by":"publisher","first-page":"65156","DOI":"10.1109\/ACCESS.2022.3183647","volume":"10","author":"P Wei","year":"2022","unstructured":"Wei P, Guo K, Li Y, Wang J, Feng W, Jin S, Liang YC (2022) Reinforcement learning-empowered mobile edge computing for 6G edge intelligence. IEEE Access 10:65156\u201365192","journal-title":"IEEE Access"},{"issue":"4","key":"1269_CR36","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1109\/COMST.2020.3011208","volume":"22","author":"K Tange","year":"2020","unstructured":"Tange K, De Donno M, Fafoutis X, Dragoni N (2020) A systematic survey of industrial Internet of Things security: requirements and fog computing opportunities. IEEE Commun Surv Tutor 22(4):2489\u20132520","journal-title":"IEEE Commun Surv Tutor"},{"key":"1269_CR37","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/6616279","volume":"2021","author":"H Qinxia","year":"2021","unstructured":"Qinxia H, Nazir S, Li M, Ullah H, Lianlian W, Ahmad S (2021) AI-enabled sensing and decision-making for IoT systems. Complexity 2021:1\u20139","journal-title":"Complexity"},{"key":"1269_CR38","first-page":"12","volume":"10","author":"P Parameswarappa","year":"2022","unstructured":"Parameswarappa P (2022) Artificial intelligence based zero trust network. Netw Secur 10:12","journal-title":"Netw Secur"},{"key":"1269_CR39","doi-asserted-by":"publisher","first-page":"2106","DOI":"10.1109\/OJCOMS.2022.3215676","volume":"3","author":"SK Jagatheesaperumal","year":"2022","unstructured":"Jagatheesaperumal SK, Pham QV, Ruby R, Yang Z, Xu C, Zhang Z (2022) Explainable AI over the Internet of Things (IoT): overview, state-of-the-art and future directions. IEEE Open J Commun Soc 3:2106\u20132136. https:\/\/doi.org\/10.1109\/OJCOMS.2022.3215676","journal-title":"IEEE Open J Commun Soc"},{"key":"1269_CR40","doi-asserted-by":"publisher","first-page":"93104","DOI":"10.22214\/ijraset.2022.42976","volume":"10","author":"Z Zhang","year":"2022","unstructured":"Zhang Z, Al Hamadi H, Damiani E, Yeun CY, Taher F (2022) Explainable artificial intelligence applications in cyber security: state-of-the-art in research. IEEE Access 10:93104\u201393139. https:\/\/doi.org\/10.1109\/ACCESS.2022.3204051","journal-title":"IEEE Access"},{"key":"1269_CR41","doi-asserted-by":"publisher","DOI":"10.22214\/ijraset.2022.42976","author":"P Divya","year":"2022","unstructured":"Divya P, Sherin Sithara A (2022) A zero trust framework security to prevent data breaches and mitigate the cloud network attacks. Ijraset J Res Appl Sci Eng Technol. https:\/\/doi.org\/10.22214\/ijraset.2022.42976","journal-title":"Ijraset J Res Appl Sci Eng Technol"},{"key":"1269_CR42","doi-asserted-by":"publisher","first-page":"220121","DOI":"10.1109\/ACCESS.2020.3042874","volume":"8","author":"RS Peres","year":"2020","unstructured":"Peres RS, Jia X, Lee J, Sun K, Colombo AW, Barata J (2020) Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access 8:220121\u2013220139","journal-title":"IEEE Access"},{"key":"1269_CR43","doi-asserted-by":"publisher","first-page":"100554","DOI":"10.1016\/j.iot.2022.100554","volume":"19","author":"R Sharma","year":"2022","unstructured":"Sharma R, Vill\u00e1nyi B (2022) Evaluation of corporate requirements for smart manufacturing systems using predictive analytics. Internet Things 19:100554","journal-title":"Internet Things"},{"key":"1269_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.teler.2023.100049","volume":"10","author":"R Das","year":"2021","unstructured":"Das R, Inuwa MM (2023) A review on fog computing: issues, characteristics, challenges, and potential applications. Telemat Inform Rep 10:100049. https:\/\/doi.org\/10.1016\/j.teler.2023.100049","journal-title":"Telemat Inform Rep"},{"key":"1269_CR45","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.matpr.2020.07.170","volume":"46","author":"H Singh","year":"2021","unstructured":"Singh H (2021) Big data, industry 4.0 and cyber-physical systems integration: a smart industry context. Mater Today Proc 46:157\u2013162","journal-title":"Mater Today Proc"},{"key":"1269_CR46","doi-asserted-by":"publisher","first-page":"35365","DOI":"10.1109\/ACCESS.2018.2836950","volume":"6","author":"Y Xin","year":"2018","unstructured":"Xin Y, Kong L, Liu Z, Chen Y, Li Y, Zhu H, Wang C (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 6:35365\u201335381","journal-title":"IEEE Access"},{"issue":"1","key":"1269_CR47","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.gltp.2021.01.004","volume":"2","author":"N Sharma","year":"2021","unstructured":"Sharma N, Sharma R, Jindal N (2021) Machine learning and deep learning applications-a vision. Global Transit Proc 2(1):24\u201328","journal-title":"Global Transit Proc"},{"key":"1269_CR48","doi-asserted-by":"publisher","first-page":"102436","DOI":"10.1016\/j.cose.2021.102436","volume":"110","author":"C Buck","year":"2021","unstructured":"Buck C, Olenberger C, Schweizer A, V\u00f6lter F, Eymann T (2021) Never trust, always verify: a multivocal literature review on current knowledge and research gaps of zero-trust. Comput Secur 110:102436","journal-title":"Comput Secur"},{"issue":"9","key":"1269_CR49","doi-asserted-by":"publisher","first-page":"7183","DOI":"10.1016\/j.aej.2021.12.061","volume":"61","author":"B Wang","year":"2022","unstructured":"Wang B, Hua Q, Zhang H, Tan X, Nan Y, Chen R, Shu X (2022) Research on anomaly detection and real-time reliability evaluation with the log of cloud platform. Alex Eng J 61(9):7183\u20137193","journal-title":"Alex Eng J"},{"issue":"2","key":"1269_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv (CSUR) 54(2):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"1269_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00496-w","volume":"8","author":"N AlDahoul","year":"2021","unstructured":"AlDahoul N, Abdul Karim H, Ba Wazir AS (2021) Model fusion of deep neural networks for anomaly detection. J Big Data 8:1\u201318","journal-title":"J Big Data"},{"issue":"9","key":"1269_CR52","doi-asserted-by":"publisher","first-page":"1973","DOI":"10.3390\/rs14091973","volume":"14","author":"X Hu","year":"2022","unstructured":"Hu X, Xie C, Fan Z, Duan Q, Zhang D, Jiang L, Chanussot J (2022) Hyperspectral anomaly detection using deep learning: a review. Remote Sens 14(9):1973","journal-title":"Remote Sens"},{"issue":"2","key":"1269_CR53","doi-asserted-by":"publisher","first-page":"201","DOI":"10.13052\/jicts2245-800X.1026","volume":"10","author":"BE Elbaghazaoui","year":"2022","unstructured":"Elbaghazaoui BE, Amnai M, Fakhri Y (2022) Data profiling and machine learning to identify influencers from social media platforms. J ICT Stand 10(2):201\u2013218. https:\/\/doi.org\/10.13052\/jicts2245-800X.1026","journal-title":"J ICT Stand"},{"key":"1269_CR54","doi-asserted-by":"crossref","unstructured":"Safi M, Kaur B, Dadkhah S, Shoeleh F, Lashkari AH, Molyneaux H, Ghorbani AA (2021) Behavioural monitoring and security profiling in the internet of things (IoT). In: 2021 IEEE 23rd Int Conf on high performance computing & communications; 7th Int Conf on data science & systems; 19th Int Conf on smart city; 7th Int Conf on dependability in sensor, cloud & big data systems & application (HPCC\/DSS\/SmartCity\/DependSys). IEEE, pp 1203\u20131210","DOI":"10.1109\/HPCC-DSS-SmartCity-DependSys53884.2021.00185"},{"key":"1269_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12859-018-2264-5","volume":"19","author":"R Couronn\u00e9","year":"2018","unstructured":"Couronn\u00e9 R, Probst P, Boulesteix AL (2018) Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinform 19:1\u201314","journal-title":"BMC Bioinform"},{"issue":"5","key":"1269_CR56","doi-asserted-by":"publisher","first-page":"3962","DOI":"10.1109\/JIOT.2021.3102056","volume":"9","author":"M Al-Hawawreh","year":"2021","unstructured":"Al-Hawawreh M, Sitnikova E, Aboutorab N (2021) X-IIoTID: a connectivity-agnostic and device-agnostic intrusion data set for industrial Internet of Things. IEEE Internet Things J 9(5):3962\u20133977","journal-title":"IEEE Internet Things J"},{"key":"1269_CR57","unstructured":"CIC IoT Dataset 2022. https:\/\/www.unb.ca\/cic\/datasets\/iotdataset-2022.html"},{"key":"1269_CR58","unstructured":"dpkt tool. https:\/\/dpkt.readthedocs.io\/en\/latest\/"}],"container-title":["Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01269-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00607-024-01269-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00607-024-01269-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T18:11:04Z","timestamp":1717092664000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00607-024-01269-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,23]]},"references-count":58,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["1269"],"URL":"https:\/\/doi.org\/10.1007\/s00607-024-01269-y","relation":{},"ISSN":["0010-485X","1436-5057"],"issn-type":[{"value":"0010-485X","type":"print"},{"value":"1436-5057","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,23]]},"assertion":[{"value":"30 June 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"All authors have mutually consented to participate.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All the authors have consented the Journal to publish this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"No Human subject or animals are involved in the research.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}