{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T21:15:54Z","timestamp":1781126154077,"version":"3.54.1"},"reference-count":164,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T00:00:00Z","timestamp":1685404800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.<\/jats:p>","DOI":"10.3390\/fi15060200","type":"journal-article","created":{"date-parts":[[2023,5,31]],"date-time":"2023-05-31T02:27:30Z","timestamp":1685500050000},"page":"200","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8580-1648","authenticated-orcid":false,"given":"Shereen","family":"Ismail","sequence":"first","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4777-2134","authenticated-orcid":false,"given":"Diana W.","family":"Dawoud","sequence":"additional","affiliation":[{"name":"College of Engineering and Information Technology, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hassan","family":"Reza","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Abbas, G., Mehmood, A., Carsten, M., Epiphaniou, G., and Lloret, J. (2022). Safety, Security and Privacy in Machine Learning Based Internet of Things. J. Sens. Actuator Netw., 11.","DOI":"10.3390\/jsan11030038"},{"key":"ref_2","unstructured":"Bajaj, K., Sharma, B., and Singh, R. (2020). Integration of WSN and IoT for Smart Cities, Springer."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2089","DOI":"10.1007\/s10586-020-03059-5","article-title":"Towards building a blockchain framework for IoT","volume":"23","author":"Pavithran","year":"2020","journal-title":"Clust. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sinha, P., Jha, V.K., Rai, A.K., and Bhushan, B. (2017, January 28\u201329). Security vulnerabilities, attacks and countermeasures in wireless sensor networks at various layers of OSI reference model: A survey. Proceedings of the 2017 International Conference on Signal Processing and Communication (ICSPC), Coimbatore, India.","DOI":"10.1109\/CSPC.2017.8305855"},{"key":"ref_5","first-page":"50","article-title":"Security in wireless sensor networks using cryptographic techniques","volume":"3","author":"Panda","year":"2014","journal-title":"Am. J. Eng. Res. (AJER)"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1109\/COMST.2020.2986444","article-title":"Machine learning in IoT security: Current solutions and future challenges","volume":"22","author":"Hussain","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10452","DOI":"10.1109\/JIOT.2021.3060508","article-title":"Embedding Blockchain Technology Into IoT for Security: A Survey","volume":"8","author":"Xu","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inffus.2018.09.013","article-title":"Machine learning algorithms for wireless sensor networks: A survey","volume":"49","author":"Kumar","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1996","DOI":"10.1109\/COMST.2014.2320099","article-title":"Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications","volume":"16","author":"Alsheikh","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/COMST.2021.3127267","article-title":"How Machine Learning Changes the Nature of Cyberattacks on IoT Networks: A Survey","volume":"24","author":"Bout","year":"2022","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tahsien, S.M., Karimipour, H., and Spachos, P. (2020). Machine learning based solutions for security of Internet of Things (IoT): A survey. J. Netw. Comput. Appl., 161.","DOI":"10.1016\/j.jnca.2020.102630"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.comnet.2019.01.023","article-title":"Internet of Things: A survey on machine learning-based intrusion detection approaches","volume":"151","author":"Papa","year":"2019","journal-title":"Comput. Netw."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100365","DOI":"10.1016\/j.iot.2021.100365","article-title":"Machine learning approaches to IoT security: A systematic literature review","volume":"14","author":"Ahmad","year":"2021","journal-title":"Internet Things"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30","DOI":"10.9734\/ajrcos\/2021\/v9i230218","article-title":"Attack and Anomaly Detection in IoT Networks using Machine Learning Techniques: A Review","volume":"9","author":"Haji","year":"2021","journal-title":"Asian J. Res. Comput. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Faraj, O., Megias, D., Ahmad, A.M., and Garcia-Alfaro, J. (2020, January 25\u201328). Taxonomy and challenges in machine learning-based approaches to detect attacks in the internet of things. Proceedings of the 15th International Conference on Availability, Reliability and Security, Virtual.","DOI":"10.1145\/3407023.3407048"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mamdouh, M., Elrukhsi, M.A.I., and Khattab, A. (2018, January 25\u201326). Securing the internet of things and wireless sensor networks via machine learning: A survey. Proceedings of the 2018 International Conference on Computer and Applications (ICCA), Beirut, Lebanon.","DOI":"10.1109\/COMAPP.2018.8460440"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mehta, A., Sandhu, J.K., and Sapra, L. (2020, January 6\u20138). Machine Learning in Wireless Sensor Networks: A Retrospective. Proceedings of the 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Solan, India.","DOI":"10.1109\/PDGC50313.2020.9315767"},{"key":"ref_18","first-page":"161","article-title":"Role of machine learning algorithms intrusion detection in WSNs: A survey","volume":"2","author":"Baraneetharan","year":"2020","journal-title":"J. Inf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Gunduz, S., Arslan, B., and Demirci, M. (2015, January 9\u201311). A Review of Machine Learning Solutions to Denial-of- Services Attacks in Wireless Sensor Networks. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), Miami, FL, USA.","DOI":"10.1109\/ICMLA.2015.202"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"12379","DOI":"10.1109\/JSEN.2020.3035846","article-title":"Machine Learning for Advanced Wireless Sensor Networks: A Review","volume":"21","author":"Kim","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ramotsoela, D., Abu-Mahfouz, A., and Hancke, G. (2018). A survey of anomaly detection in industrial wireless sensor networks with critical water system infrastructure as a case study. Sensors, 18.","DOI":"10.3390\/s18082491"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ahmad, R., Wazirali, R., and Abu-Ain, T. (2022). Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues. Sensors, 22.","DOI":"10.3390\/s22134730"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"9675050","DOI":"10.1155\/2018\/9675050","article-title":"A Survey of How to Use Blockchain to Secure Internet of Things and the Stalker Attack","volume":"2018","author":"Jesus","year":"2018","journal-title":"Secur. Commun. Netw."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1159","DOI":"10.1109\/TNSM.2021.3122147","article-title":"Blockchain on Security and Forensics Management in Edge Computing for IoT: A Comprehensive Survey","volume":"19","author":"Liao","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"102481","DOI":"10.1016\/j.jnca.2019.102481","article-title":"A Comprehensive Survey on Attacks, Security Issues and Blockchain Solutions for IoT and IIoT","volume":"149","author":"Sengupta","year":"2020","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1016\/j.future.2017.11.022","article-title":"IoT security: Review, blockchain solutions, and open challenges","volume":"82","author":"Khan","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Darla, S., and Naveena, C. (2022, January 16\u201318). Survey on Securing Internet of Things through Block chain Technology. Proceedings of the 2022 International Conference on Electronics and Renewable Systems (ICEARS), Tuticorin, India.","DOI":"10.1109\/ICEARS53579.2022.9752316"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100006","DOI":"10.1016\/j.bcra.2021.100006","article-title":"A survey on the adoption of blockchain in IoT: Challenges and solutions","volume":"2","author":"Uddin","year":"2021","journal-title":"Blockchain Res. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e3741","DOI":"10.1002\/ett.3741","article-title":"Blockchain-based security aspects in heterogeneous Internet-of-Things networks: A survey","volume":"30","author":"Pohrmen","year":"2019","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.comcom.2021.07.009","article-title":"Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review","volume":"178","author":"Miglani","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_31","first-page":"3","article-title":"Overview of wireless sensor network","volume":"1","author":"Matin","year":"2012","journal-title":"Wirel. Sens.-Netw.-Technol. Protoc."},{"key":"ref_32","first-page":"105","article-title":"A study of machine learning in wireless sensor network","volume":"4","author":"Khan","year":"2017","journal-title":"Int. J. Comput. Netw. Appl."},{"key":"ref_33","unstructured":"Rehana, J. (2009, January 24\u201328). Security of wireless sensor network. Proceedings of the Seminar on Internetworking, Helsinki University of Technology, Glasgow, UK."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"26","DOI":"10.3991\/ijoe.v6i4.1466","article-title":"Security Issues in Wireless Sensor Networks","volume":"6","author":"Sora","year":"2010","journal-title":"Int. J. Online Biomed. Eng. (IJOE)"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Patel, N.R., and Kumar, S. (2018, January 23\u201324). Wireless Sensor Networks\u2019 Challenges and Future Prospects. Proceedings of the 2018 International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India.","DOI":"10.1109\/SYSMART.2018.8746937"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"de Farias, C.M., Pirmez, L., Delicato, F.C., Pires, P.F., Guerrieri, A., Fortino, G., Cauteruccio, F., and Terracina, G. (2017, January 16\u201318). A multisensor data fusion algorithm using the hidden correlations in Multiapplication Wireless Sensor data streams. Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC), Calabria, Italy.","DOI":"10.1109\/ICNSC.2017.8000074"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.comnet.2018.05.010","article-title":"A comprehensive survey on wireless sensor node hardware platforms","volume":"144","author":"Karray","year":"2018","journal-title":"Comput. Netw."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"2205","DOI":"10.1109\/JIOT.2018.2883403","article-title":"Data Collection for Security Measurement in Wireless Sensor Networks: A Survey","volume":"6","author":"Xie","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Alam, S., and De, D. (2014). Analysis of security threats in wireless sensor network. arXiv.","DOI":"10.5121\/ijwmn.2014.6204"},{"key":"ref_40","unstructured":"Walters, J.P., Liang, Z., Shi, W., and Chaudhary, V. (2006). Security in distributed, Grid, and Pervasive Computing, Auerbach Publications."},{"key":"ref_41","unstructured":"Vacca, J.R. (2013). Computer and Information Security Handbook, Morgan Kaufmann. [2nd ed.]."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Elhoseny, M., and Hassanien, A.E. (2019). Dynamic Wireless Sensor Networks, Springer.","DOI":"10.1007\/978-3-319-92807-4"},{"key":"ref_43","unstructured":"Shahzad, F., Pasha, M., and Ahmad, A. (2017). A survey of active attacks on wireless sensor networks and their countermeasures. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mathew, A., and Terence, J.S. (2017, January 6\u20138). A survey on various detection techniques of sinkhole attacks in WSN. Proceedings of the 2017 International Conference on Communication and Signal Processing (ICCSP), Chennai, India.","DOI":"10.1109\/ICCSP.2017.8286550"},{"key":"ref_45","unstructured":"Dewal, P., Narula, G.S., Jain, V., and Baliyan, A. (2018). Cyber Security, Springer."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kaur, R., and Kaur Sandhu, J. (2021, January 4\u20135). A Study on Security Attacks in Wireless Sensor Network. Proceedings of the 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), Greater Noida, India.","DOI":"10.1109\/ICACITE51222.2021.9404619"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Ismail, S., Khoei, T.T., Marsh, R., and Kaabouch, N. (2021, January 1\u20134). A Comparative Study of Machine Learning Models for Cyber-attacks Detection in Wireless Sensor Networks. Proceedings of the 2021 IEEE 12th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), New York, NY, USA.","DOI":"10.1109\/UEMCON53757.2021.9666581"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Pruthi, V., Mittal, K., Sharma, N., and Kaushik, I. (2019, January 18\u201319). Network layers threats & its countermeasures in WSNs. Proceedings of the 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India.","DOI":"10.1109\/ICCCIS48478.2019.8974523"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, G., Dai, L., and Wei, Z. (2018). Challenges, threats, security issues and new trends of underwater wireless sensor networks. Sensors, 18.","DOI":"10.3390\/s18113907"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"de Lima Pinto, E.M., Lachowski, R., Pellenz, M.E., Penna, M.C., and Souza, R.D. (2018, January 16\u201318). A machine learning approach for detecting spoofing attacks in wireless sensor networks. Proceedings of the 2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), Krakow, Poland.","DOI":"10.1109\/AINA.2018.00113"},{"key":"ref_51","unstructured":"Bhattasali, T., and Chaki, R. (2011). Advances in Network Security and Applications, Proceedings of the 4th International Conference, CNSA 2011, Chennai, India, 15\u201317 July 2011, Springer."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Tiberti, W., Carmenini, A., Pomante, L., and Cassioli, D. (2020, January 26\u201328). A Lightweight Blockchain-based Technique for Anti-Tampering in Wireless Sensor Networks. Proceedings of the 2020 23rd Euromicro Conference on Digital System Design (DSD), Kranj, Slovenia.","DOI":"10.1109\/DSD51259.2020.00095"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1165","DOI":"10.1007\/s00779-018-1162-1","article-title":"Swarm-based defense technique for tampering and cheating attack in WSN using CPHS","volume":"22","author":"Periyanayagi","year":"2018","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"65450","DOI":"10.1109\/ACCESS.2020.2983091","article-title":"A Systematic Review on Clone Node Detection in Static Wireless Sensor Networks","volume":"8","author":"Numan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_55","first-page":"558","article-title":"Security attacks & prerequisite for wireless sensor networks","volume":"2","author":"Gupta","year":"2013","journal-title":"Int. J. Eng. Adv. Technol. (IJEAT)"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"103278","DOI":"10.1016\/j.micpro.2020.103278","article-title":"DLDM: Deep learning-based defense mechanism for denial of service attacks in wireless sensor networks","volume":"79","author":"Premkumar","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1503","DOI":"10.30534\/ijeter\/2020\/05852020","article-title":"Handling of Man-In-The-Middle Attack in WSN Through Intrusion Detection System","volume":"8","author":"Mohapatra","year":"2020","journal-title":"Int. J. Emerg. Trends Eng. Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yahyaoui, A., Abdellatif, T., and Attia, R. (2019, January 24\u201328). Hierarchical anomaly based intrusion detection and localization in IoT. Proceedings of the 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), Tangier, Morocco.","DOI":"10.1109\/IWCMC.2019.8766574"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Somaya, H., and Tomader, M. (2021, January 1\u20132). Build a malware detection software for IOT network Using Machine learning. Proceedings of the 4th International Conference on Networking, Information Systems & Security, Kenitra, Morocco.","DOI":"10.1145\/3454127.3458773"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"92931","DOI":"10.1109\/ACCESS.2022.3202807","article-title":"STLGBM-DDS: An Efficient Data Balanced DoS Detection System for Wireless Sensor Networks on Big Data Environment","volume":"10","author":"Dener","year":"2022","journal-title":"IEEE Access"},{"key":"ref_61","unstructured":"Park, T., Cho, D., and Kim, H. (2018, January 3\u20136). An effective classification for DoS attacks in wireless sensor networks. Proceedings of the 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), Prague, Czech Republic."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Quincozes, S.E., and Kazienko, J.F. (2020, January 2\u201316). Machine learning methods assessment for denial of service detection in wireless sensor networks. Proceedings of the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA.","DOI":"10.1109\/WF-IoT48130.2020.9221146"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Alsubaie, F., Al-Akhras, M., and Alzahrani, H.A. (2020, January 3\u20135). Using machine learning for intrusion detection system in wireless body area network. Proceedings of the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMART-TECH49988.2020.00036"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Alsulaiman, L., and Al-Ahmadi, S. (2021). Performance evaluation of machine learning techniques for DOS detection in wireless sensor network. arXiv.","DOI":"10.5121\/ijnsa.2021.13202"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1742-6596\/1743\/1\/012021","article-title":"Anomaly detection using machine learning techniques in wireless sensor networks","volume":"1743","author":"Ifzarne","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e6152","DOI":"10.1002\/cpe.6152","article-title":"Design and analysis of efficient neural intrusion detection for wireless sensor networks","volume":"33","author":"Batiha","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Al-Akhras, M., Al-Issa, A.I., Alsahli, M.S., and Alawairdhi, M. (2020, January 3\u20135). POSTER: Feature Selection to Optimize DoS Detection in Wireless Sensor Networks. Proceedings of the 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH), Riyadh, Saudi Arabia.","DOI":"10.1109\/SMART-TECH49988.2020.00070"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Batiha, T., Prauzek, M., and Kr\u00f6mer, P. (2019). Intrusion Detection in Wireless Sensor Networks by an Ensemble of Artificial Neural Networks, Springer.","DOI":"10.1007\/978-981-13-8311-3_28"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Ismail, S., Dawoud, D., and Reza, H. (2022, January 26\u201329). A Lightweight Multilayer Machine Learning Detection System for Cyber-attacks in WSN. Proceedings of the 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Virtual.","DOI":"10.1109\/CCWC54503.2022.9720891"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Ismail, S., and Reza, H. (2022, January 6\u20139). Evaluation of Na\u00efve Bayesian Algorithms for Cyber-Attacks Detection in Wireless Sensor Networks. Proceedings of the 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA.","DOI":"10.1109\/AIIoT54504.2022.9817298"},{"key":"ref_71","first-page":"1","article-title":"Novel Wireless Sensor Network Intrusion Detection Method Based on LightGBM Model","volume":"52","author":"Meng","year":"2022","journal-title":"IAENG Int. J. Appl. Math."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Luo, T., and Nagarajan, S.G. (2018, January 20\u201324). Distributed anomaly detection using autoencoder neural networks in WSN for IoT. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422402"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Sherubha, P., Amudhavalli, P., and Sasirekha, S. (2019, January 4\u20136). Clone attack detection using random forest and multi objective cuckoo search classification. Proceedings of the 2019 International Conference on Communication and Signal Processing (ICCSP), Melmaruvathur, India.","DOI":"10.1109\/ICCSP.2019.8698077"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Otoum, S., Kantarci, B., and Mouftah, H. (2019, January 20\u201324). Empowering reinforcement learning on big sensed data for intrusion detection. Proceedings of the ICC 2019-2019 IEEE international conference on communications (ICC), Shanghai, China.","DOI":"10.1109\/ICC.2019.8761575"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"e4336","DOI":"10.1002\/ett.4336","article-title":"Developing an attack detection framework for wireless sensor network-based healthcare applications using hybrid convolutional neural network","volume":"32","author":"Subasini","year":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Salmi, S., and Oughdir, L. (2022). CNN-LSTM Based Approach for Dos Attacks Detection in Wireless Sensor Networks. Int. J. Adv. Comput. Sci. Appl., 13.","DOI":"10.14569\/IJACSA.2022.0130497"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-023-00692-w","article-title":"Performance evaluation of deep learning techniques for DoS attacks detection in wireless sensor network","volume":"10","author":"Salmi","year":"2023","journal-title":"J. Big Data"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LNET.2019.2901792","article-title":"On the feasibility of deep learning in sensor network intrusion detection","volume":"1","author":"Otoum","year":"2019","journal-title":"IEEE Netw. Lett."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hussain, K., Xia, Y., Onaizah, A.N., Manzoor, T., and Jalil, K. (2022). Hybrid of WOA-ABC and Proposed CNN for Intrusion Detection System in wireless sensor networks. Optik, 170145.","DOI":"10.1016\/j.ijleo.2022.170145"},{"key":"ref_80","unstructured":"Nguyen, T.T., and Reddi, V.J. Deep reinforcement learning for cyber security. IEEE Trans. Neural Netw. Learn. Syst., 2019."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Benaddi, H., Ibrahimi, K., Benslimane, A., and Qadir, J. (2019, January 26\u201327). A deep reinforcement learning based intrusion detection system (drl-ids) for securing wireless sensor networks and internet of things. Proceedings of the International Wireless Internet Conference, Taichung, Taiwan.","DOI":"10.1007\/978-3-030-52988-8_7"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1109\/MCOM.001.1900461","article-title":"Federated learning for wireless communications: Motivation, opportunities, and challenges","volume":"58","author":"Niknam","year":"2020","journal-title":"IEEE Commun. Mag."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Kamel, R.M., and El Mougy, A. (2020, January 16\u201319). Retrospective sensing based on federated learning in the IoT. Proceedings of the 2020 IEEE 45th LCN Symposium on Emerging Topics in Networking (LCN Symposium), Sydney, Australia.","DOI":"10.1109\/LCNSymposium50271.2020.9363271"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Kim, S., Cai, H., Hua, C., Gu, P., Xu, W., and Park, J. (2020, January 9\u201311). Collaborative anomaly detection for internet of things based on federated learning. Proceedings of the 2020 IEEE\/CIC International Conference on Communications in China (ICCC), Chongqing, China.","DOI":"10.1109\/ICCC49849.2020.9238913"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Mertens, J., Galluccio, L., and Morabito, G. (2021, January 24\u201328). Federated learning through model gossiping in wireless sensor networks. Proceedings of the 2021 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), Bucharest, Romania.","DOI":"10.1109\/BlackSeaCom52164.2021.9527886"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Banerjee, J., Maiti, S., Chakraborty, S., Dutta, S., Chakraborty, A., and Banerjee, J.S. (2019, January 27\u201329). Impact of Machine Learning in Various Network Security Applications. Proceedings of the 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC.2019.8819811"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1109\/JSAC.2019.2904348","article-title":"Adaptive federated learning in resource constrained edge computing systems","volume":"37","author":"Wang","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Zahariadis, T., Trakadas, P., Maniatis, S., Karkazis, P., Leligou, H.C., and Voliotis, S. (2009, January 18\u201320). Efficient detection of routing attacks in wireless sensor networks. Proceedings of the 2009 16th International Conference on Systems, Signals and Image Processing, Chalkida, Greece.","DOI":"10.1109\/IWSSIP.2009.5367775"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1080\/15501320600692044","article-title":"Intrusion detection for routing attacks in sensor networks","volume":"2","author":"Loo","year":"2006","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Amouri, A., Alaparthy, V.T., and Morgera, S.D. (2018, January 9\u201310). Cross layer-based intrusion detection based on network behavior for IoT. Proceedings of the 2018 IEEE 19th Wireless and Microwave Technology Conference (WAMICON), Sand Key, FL, USA.","DOI":"10.1109\/WAMICON.2018.8363921"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Pande, S., Khamparia, A., and Gupta, D. (2021). Feature selection and comparison of classification algorithms for wireless sensor networks. J. Ambient. Intell. Humaniz. Comput., 1\u201313.","DOI":"10.1007\/s12652-021-03411-6"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Almomani, I., Al-Kasasbeh, B., and Al-Akhras, M. (2016). WSN-DS: A Dataset for Intrusion Detection Systems in Wireless Sensor Networks. J. Sens., 2016.","DOI":"10.1155\/2016\/4731953"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Tavallaee, M., Bagheri, E., Lu, W., and Ghorbani, A.A. (2009, January 8\u201310). A detailed analysis of the KDD CUP 99 data set. Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications, Ottawa, ON, Canada.","DOI":"10.1109\/CISDA.2009.5356528"},{"key":"ref_94","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":"ref_95","doi-asserted-by":"crossref","unstructured":"Moustafa, N., and Slay, J. (2015, January 10\u201312). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), Canberra, Australia.","DOI":"10.1109\/MilCIS.2015.7348942"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.aci.2018.08.003","article-title":"Classification assessment methods","volume":"17","author":"Tharwat","year":"2018","journal-title":"Appl. Comput. Inform."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"91038","DOI":"10.1109\/ACCESS.2021.3092054","article-title":"Developing an Efficient Feature Engineering and Machine Learning Model for Detecting IoT-Botnet Cyber Attacks","volume":"9","author":"Panda","year":"2021","journal-title":"IEEE Access"},{"key":"ref_98","first-page":"617","article-title":"Evaluation of Machine Learning Algorithms for Intrusion Detection System in WSN","volume":"12","author":"Alsahli","year":"2021","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_99","unstructured":"Menezes, A.J., and Vanstone, S.A. How to Time-Stamp a Digital Document. Proceedings of the Advances in Cryptology-CRYPTO\u2019 90; Menezes."},{"key":"ref_100","unstructured":"Nakamoto, S. (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. Decentralized Bus. Rev., 21260. Available online: https:\/\/bitcoin.org\/bitcoin.pdf."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.comcom.2019.01.006","article-title":"Survey on blockchain for Internet of Things","volume":"136","author":"Wang","year":"2019","journal-title":"Comput. Commun."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Gao, W., Hatcher, W.G., and Yu, W. (August, January 30). A Survey of Blockchain: Techniques, Applications, and Challenges. Proceedings of the 2018 27th International Conference on Computer Communication and Networks (ICCCN), Hangzhou, China.","DOI":"10.1109\/ICCCN.2018.8487348"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.icte.2019.08.001","article-title":"Analysis of the main consensus protocols of blockchain","volume":"6","author":"Zhang","year":"2020","journal-title":"ICT Express"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Gagneja, K., Gagneja, K., and Kiefer, R. (2020, January 22\u201323). Security Protocol for Internet of Things (IoT): Blockchain-based Implementation and Analysis. Proceedings of the 2020 Sixth International Conference on Mobile And Secure Services (MobiSecServ), Miami, FL, USA.","DOI":"10.1109\/MobiSecServ48690.2020.9042948"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"117134","DOI":"10.1109\/ACCESS.2019.2936094","article-title":"A survey of blockchain from the perspectives of applications, challenges, and opportunities","volume":"7","author":"Monrat","year":"2019","journal-title":"IEEE Access"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"72326","DOI":"10.1109\/ACCESS.2021.3079708","article-title":"Employing Blockchain Technology to Strengthen Security of Wireless Sensor Networks","volume":"9","author":"Hsiao","year":"2021","journal-title":"IEEE Access"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Xu, R., Chen, Y., Blasch, E., and Chen, G. (August, January 30). Blendcac: A blockchain-enabled decentralized capability-based access control for iots. Proceedings of the IEEE 2018 International Congress on Cybermatics: 2018 IEEE Conferences on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, iThings\/Gree Halifax, Halifax, NS, Canada.","DOI":"10.1109\/Cybermatics_2018.2018.00191"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Khalil, A.A., Franco, J., Parvez, I., Uluagac, S., and Rahman, M.A. (July, January 27). A Literature Review on Blockchain-enabled Security and Operation of Cyber-Physical Systems. Proceedings of the 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC), Los Alamitos, CA, USA.","DOI":"10.1109\/COMPSAC54236.2022.00282"},{"key":"ref_109","first-page":"241","article-title":"A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN","volume":"13","author":"Cui","year":"2020","journal-title":"IEEE Trans. Serv. Comput."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"102491","DOI":"10.1016\/j.cose.2021.102491","article-title":"Authentication and Identity Management of IoHT Devices: Achievements, Challenges, and Future Directions","volume":"111","author":"Mamdouh","year":"2021","journal-title":"Comput. Secur."},{"key":"ref_111","unstructured":"Salimitari, M., and Chatterjee, M. (2018). A Survey on Consensus Protocols in Blockchain for IoT Networks. arXiv, 1\u201315."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Mohanta, B.K., Panda, S.S., and Jena, D. (2018, January 10\u201312). An overview of smart contract and use cases in blockchain technology. Proceedings of the 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Bengaluru, India.","DOI":"10.1109\/ICCCNT.2018.8494045"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Teslya, N., and Ryabchikov, I. (2018, January 15\u201318). Blockchain platforms overview for industrial IoT purposes. Proceedings of the Conference of Open Innovation Association, FRUCT, Jyvaskyla, Finland.","DOI":"10.23919\/FRUCT.2018.8468276"},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Ismail, S., Reza, H., Zadeh, H.K., and Vasefi, F. (2023, January 8\u201311). A Blockchain-based IoT Security Solution Using Multichain. Proceedings of the 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC57344.2023.10099128"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Alkurdi, F., Elgendi, I., Munasinghe, K.S., Sharma, D., and Jamalipour, A. (2018, January 21\u201323). Blockchain in IoT Security: A Survey. Proceedings of the 2018 28th International Telecommunication Networks and Applications Conference, ITNAC 2018, Sydney, Australia.","DOI":"10.1109\/ATNAC.2018.8615409"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1109\/COMST.2020.2975911","article-title":"Blockchain and Machine Learning for Communications and Networking Systems","volume":"22","author":"Liu","year":"2020","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Honar Pajooh, H., Rashid, M., Alam, F., and Demidenko, S. (2021). Hyperledger fabric blockchain for securing the edge internet of things. Sensors, 21.","DOI":"10.3390\/s21020359"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"6193","DOI":"10.1109\/TII.2020.2965975","article-title":"A Blockchain-Based Secure Key Management Scheme With Trustworthiness in DWSNs","volume":"16","author":"Tian","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.future.2021.06.039","article-title":"A secure localization scheme based on trust assessment for WSNs using blockchain technology","volume":"125","author":"Goyat","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Guerrero-Sanchez, A.E., Rivas-Araiza, E.A., Gonzalez-Cordoba, J.L., Toledano-Ayala, M., and Takacs, A. (2020). Blockchain mechanism and symmetric encryption in a wireless sensor network. Sensors, 20.","DOI":"10.3390\/s20102798"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1007\/s12652-020-02017-8","article-title":"A secure IoT sensors communication in industry 4.0 using blockchain technology","volume":"12","author":"Rathee","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Ismail, S., Dawoud, D., and Reza, H. (2022, January 6\u20139). Towards A Lightweight Identity Management and Secure Authentication for IoT Using Blockchain. Proceedings of the 2022 IEEE World AI IoT Congress (AIIoT), Seattle, WA, USA.","DOI":"10.1109\/AIIoT54504.2022.9817349"},{"key":"ref_123","unstructured":"Miraz, M.H. (2020). Advanced Applications of Blockchain Technology, Springer."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"4650","DOI":"10.1109\/JIOT.2018.2874095","article-title":"A scalable blockchain framework for secure transactions in IoT","volume":"6","author":"Biswas","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_125","first-page":"1","article-title":"A rolling blockchain for a dynamic WSNs in a smart city","volume":"1","author":"Kushch","year":"2018","journal-title":"arXiv"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3372136","article-title":"A survey of IoT applications in blockchain systems: Architecture, consensus, and traffic modeling","volume":"53","author":"Lao","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Zamani, M., Movahedi, M., and Raykova, M. (2018, January 15\u201319). RapidChain: Scaling blockchain via full sharding. Proceedings of the ACM Conference on Computer and Communications Security, Toronto, ON, Canada.","DOI":"10.1145\/3243734.3243853"},{"key":"ref_128","first-page":"267","article-title":"Lightweight and Scalable DAG based distributed ledger for verifying IoT data integrity","volume":"2021","author":"Cherupally","year":"2021","journal-title":"Int. Conf. Inf. Netw."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Buldin, I.D., Gorodnichev, M.G., Makhrov, S.S., and Denisova, E.N. (2018, January 3\u20137). Next Generation Industrial Blockchain-Based Wireless Sensor Networks. Proceedings of the 2018 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF), Saint Petersburg, Russia.","DOI":"10.1109\/WECONF.2018.8604408"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"9228982","DOI":"10.1155\/2022\/9228982","article-title":"A Study on the Adoption of Blockchain for IoT Devices in Supply Chain","volume":"2022","author":"Baig","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Goyal, H., and Saha, S. (November, January 31). Reli: Real-time lightweight byzantine consensus in low-power iot-systems. Proceedings of the 2022 18th International Conference on Network and Service Management (CNSM), Thessaloniki, Greece.","DOI":"10.23919\/CNSM55787.2022.9965123"},{"key":"ref_132","unstructured":"Gopalakrishnan, K. (2020). Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm, Springer."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3417987","article-title":"Security and Privacy in IoT Using Machine Learning and Blockchain: Threats and Countermeasures","volume":"53","author":"Waheed","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Marchang, J., Ibbotson, G., and Wheway, P. (2019, January 24\u201326). Will blockchain technology become a reality in sensor networks?. Proceedings of the 2019 Wireless Days (WD), Manchester, UK.","DOI":"10.1109\/WD.2019.8734268"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"156114","DOI":"10.1109\/ACCESS.2021.3129697","article-title":"A survey of IoT and blockchain integration: Security perspective","volume":"9","author":"Shammar","year":"2021","journal-title":"IEEE Access"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"38947","DOI":"10.1109\/ACCESS.2019.2902811","article-title":"Blockchain trust model for malicious node detection in wireless sensor networks","volume":"7","author":"She","year":"2019","journal-title":"IEEE Access"},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Kaiser, M.S., Bandyopadhyay, A., Mahmud, M., and Ray, K. (2021). Proceedings of International Conference on Trends in Computational and Cognitive Engineering, Springer.","DOI":"10.1007\/978-981-33-4673-4"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Chanana, R., Singh, A.K., Killa, R., Agarwal, S., and Mehra, P.S. (2020, January 5\u20137). Blockchain Based Secure Model for Sensor Data in Wireless Sensor Network. Proceedings of the 2020 6th International Conference on Signal Processing and Communication (ICSC), Noida, India.","DOI":"10.1109\/ICSC48311.2020.9182776"},{"key":"ref_139","first-page":"1","article-title":"An efficient authentication scheme using blockchain technology for wireless sensor networks","volume":"1","author":"Mubarakali","year":"2021","journal-title":"Wirel. Pers. Commun."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"59","DOI":"10.36548\/jtcsst.2021.1.006","article-title":"A data driven trust mechanism based on blockchain in IoT sensor networks for detection and mitigation of attacks","volume":"3","author":"Sivaganesan","year":"2021","journal-title":"J. Trends Comput. Sci. Smart Technol. (TCSST)"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1504\/IJAHUC.2021.115125","article-title":"Black hole attack prevention scheme using a blockchain-block approach in SDN-enabled WSN","volume":"37","year":"2021","journal-title":"Int. J. Hoc Ubiquitous Comput."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.procs.2022.07.126","article-title":"Protecting Routing Data in WSNs with use of IOTA Tangle","volume":"203","author":"Soltani","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Javed, S., Khan, M.A., Abdullah, A.M., Alsirhani, A., Alomari, A., Noor, F., and Ullah, I. (2022). An Efficient Authentication Scheme Using Blockchain as a Certificate Authority for the Internet of Drones. Drones, 6.","DOI":"10.3390\/drones6100264"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"e6144","DOI":"10.1002\/cpe.6144","article-title":"Efficient and secure routing protocol based on Blockchain approach for wireless sensor networks","volume":"33","author":"Lazrag","year":"2021","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_145","doi-asserted-by":"crossref","unstructured":"Awan, S., Javaid, N., Ullah, S., Khan, A.U., Qamar, A.M., and Choi, J.G. (2022). Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks. Sensors, 22.","DOI":"10.3390\/s22020411"},{"key":"ref_146","doi-asserted-by":"crossref","unstructured":"Chen, Y., Yang, X., Li, T., Ren, Y., and Long, Y. A blockchain-empowered authentication scheme for worm detection in wireless sensor network. Digit. Commun. Netw., 2022.","DOI":"10.1016\/j.dcan.2022.04.007"},{"key":"ref_147","doi-asserted-by":"crossref","unstructured":"Maleh, Y., Baddi, Y., Alazab, M., Tawalbeh, L., and Romdhani, I. (2021). Artificial Intelligence and Blockchain for Future Cybersecurity Applications, Springer International Publishing.","DOI":"10.1007\/978-3-030-74575-2"},{"key":"ref_148","unstructured":"Moinet, A., Darties, B., and Baril, J.L. (2017). Blockchain based trust & authentication for decentralized sensor networks. arXiv."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"184133","DOI":"10.1109\/ACCESS.2019.2960609","article-title":"A novel trust evaluation process for secure localization using a decentralized blockchain in wireless sensor networks","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1109\/ACCESS.2019.2962829","article-title":"Intrusion detection protocols in wireless sensor networks integrated to Internet of Things deployment: Survey and future challenges","volume":"8","author":"Pundir","year":"2019","journal-title":"IEEE Access"},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"e4259","DOI":"10.1002\/ett.4259","article-title":"A trusted distributed routing scheme for wireless sensor networks using blockchain and meta-heuristics-based deep learning technique","volume":"32","author":"Revanesh","year":"2021","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"6106","DOI":"10.1109\/ACCESS.2023.3236983","article-title":"Malicious Node Detection using Machine Learning and Distributed Data Storage using Blockchain in WSNs","volume":"11","author":"Nouman","year":"2023","journal-title":"IEEE Access"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"9430132","DOI":"10.1155\/2021\/9430132","article-title":"BCEAD: A blockchain-empowered ensemble anomaly detection for wireless sensor network via isolation forest","volume":"2021","author":"Yang","year":"2021","journal-title":"Secur. Commun. Netw."},{"key":"ref_154","first-page":"24695","article-title":"Exploiting Machine Learning to Detect Malicious Nodes in Intelligent Sensor-Based Systems Using Blockchain","volume":"9","author":"Sajid","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_155","doi-asserted-by":"crossref","unstructured":"Yang, J., He, S., Xu, Y., Chen, L., and Ren, J. (2019). A trusted routing scheme using blockchain and reinforcement learning for wireless sensor networks. Sensors, 19.","DOI":"10.3390\/s19040970"},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"103822","DOI":"10.1109\/ACCESS.2021.3098933","article-title":"Towards designing a trusted routing scheme in wireless sensor networks: A new deep blockchain approach","volume":"9","author":"Darwish","year":"2021","journal-title":"IEEE Access"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"4513","DOI":"10.1007\/s11276-021-02748-2","article-title":"Machine learning based volatile block chain construction for secure routing in decentralized military sensor networks","volume":"27","author":"Rajasoundaran","year":"2021","journal-title":"Wirel. Netw."},{"key":"ref_158","doi-asserted-by":"crossref","unstructured":"Puthal, D., Mohanty, S.P., Nanda, P., Kougianos, E., and Das, G. (2019, January 8\u201311). Proof-of-Authentication for Scalable Blockchain in Resource-Constrained Distributed Systems. Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Berlin, Germany.","DOI":"10.1109\/ICCE.2019.8662009"},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Mingxiao, D., Xiaofeng, M., Zhe, Z., Xiangwei, W., and Qijun, C. (2017, January 5\u20138). A review on consensus algorithm of blockchain. Proceedings of the 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, AB, Canada.","DOI":"10.1109\/SMC.2017.8123011"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"24695","DOI":"10.1109\/JIOT.2022.3196269","article-title":"Vulnerability Analysis of Smart Contract for Blockchain-Based IoT Applications: A Machine Learning Approach","volume":"9","author":"Zhou","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_161","doi-asserted-by":"crossref","unstructured":"Wang, S.Y., Hsu, Y.J., and Hsiao, S.J. (2018, January 6\u20138). Integrating blockchain technology for data collection and analysis in wireless sensor networks with an innovative implementation. Proceedings of the 2018 International Symposium on Computer, Consumer and Control (IS3C), Taichung, Taiwan.","DOI":"10.1109\/IS3C.2018.00045"},{"key":"ref_162","first-page":"33","article-title":"Machine learning techniques for anomaly detection: An overview","volume":"79","author":"Omar","year":"2013","journal-title":"Int. J. Comput. Appl."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13635-019-0084-4","article-title":"Transfer learning for detecting unknown network attacks","volume":"2019","author":"Zhao","year":"2019","journal-title":"Eurasip J. Inf. Secur."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Lu, Y., Tang, Q., and Wang, G. (2018, January 17\u201320). On enabling machine learning tasks atop public blockchains: A crowdsourcing approach. Proceedings of the 2018 IEEE International Conference on Data Mining Workshops (ICDMW), Singapore.","DOI":"10.1109\/ICDMW.2018.00019"}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/6\/200\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:45:40Z","timestamp":1760125540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/15\/6\/200"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,30]]},"references-count":164,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["fi15060200"],"URL":"https:\/\/doi.org\/10.3390\/fi15060200","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,30]]}}}