{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T03:40:53Z","timestamp":1773805253669,"version":"3.50.1"},"reference-count":114,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T00:00:00Z","timestamp":1759708800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013410","name":"INCIBE","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013410","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["MAKE"],"abstract":"<jats:p>The Internet of Things (IoT) revolutionizes connectivity, enabling innovative applications across healthcare, industry, and smart cities but also introducing significant cybersecurity challenges due to its expanded attack surface. Intrusion Detection Systems (IDSs) play a pivotal role in addressing these challenges, offering tailored solutions to detect and mitigate threats in dynamic and resource-constrained IoT environments. Through a rigorous analysis, this study classifies IDS research based on methodologies, performance metrics, and application domains, providing a comprehensive synthesis of the field. Key findings reveal a paradigm shift towards integrating artificial intelligence (AI) and hybrid approaches, surpassing the limitations of traditional, static methods. These advancements highlight the potential for IDSs to enhance scalability, adaptability, and detection accuracy. However, unresolved challenges, such as resource efficiency and real-world applicability, underline the need for further research. By contextualizing these findings within the broader landscape of IoT security, this work emphasizes the critical importance of developing IDS solutions that ensure the reliability, privacy, and security of interconnected systems, contributing to the sustainable evolution of IoT ecosystems.<\/jats:p>","DOI":"10.3390\/make7040115","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T15:05:06Z","timestamp":1759763106000},"page":"115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["AI-Enabled IoT Intrusion Detection: Unified Conceptual Framework and Research Roadmap"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8922-192X","authenticated-orcid":false,"given":"Antonio","family":"Villafranca","sequence":"first","affiliation":[{"name":"Department of Information and Communication Technologies, Universidad Polit\u00e9cnica de Cartagena, 30202 Cartagena, Spain"}]},{"given":"Kyaw Min","family":"Thant","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, Universidad Polit\u00e9cnica de Cartagena, 30202 Cartagena, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4059-8134","authenticated-orcid":false,"given":"Igor","family":"Tasic","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Business, UCAM Universidad Cat\u00f3lica San Antonio de Murcia, 30107 Murcia, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4952-0325","authenticated-orcid":false,"given":"Maria-Dolores","family":"Cano","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Technologies, Universidad Polit\u00e9cnica de Cartagena, 30202 Cartagena, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,6]]},"reference":[{"key":"ref_1","unstructured":"(2025, October 03). IHS Markit. Available online: https:\/\/euristiq.com\/future-of-iot\/."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.comcom.2022.06.015","article-title":"FEDGAN-IDS: Privacy-preserving IDS using GAN and Federated Learning","volume":"192","author":"Tabassum","year":"2022","journal-title":"Comput. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103097","DOI":"10.1016\/j.cose.2023.103097","article-title":"2DF-IDS: Decentralized and differentially private federated learning-based intrusion detection system for industrial IoT","volume":"127","author":"Friha","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109486","DOI":"10.1016\/j.asoc.2022.109486","article-title":"An edge computing based anomaly detection method in IoT industrial sustainability","volume":"128","author":"Yu","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6882","DOI":"10.1109\/JIOT.2020.2970501","article-title":"Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices","volume":"7","author":"Eskandari","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"52215","DOI":"10.1109\/ACCESS.2024.3386631","article-title":"FL-IDS: Federated Learning-Based Intrusion Detection System Using Edge Devices for Transportation IoT","volume":"12","author":"Bhavsar","year":"2024","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108379","DOI":"10.1016\/j.compeleceng.2022.108379","article-title":"HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection","volume":"103","author":"Sarhan","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"108884","DOI":"10.1016\/j.compeleceng.2023.108884","article-title":"A blockchain-assisted security management framework for collaborative intrusion detection in smart cities","volume":"111","author":"Li","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.aej.2024.03.041","article-title":"Robust network anomaly detection using ensemble learning approach and explainable artificial intelligence (XAI)","volume":"94","author":"Hooshmand","year":"2024","journal-title":"Alex. Eng. J."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"121751","DOI":"10.1016\/j.eswa.2023.121751","article-title":"Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach","volume":"238","author":"Sharma","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"131661","DOI":"10.1109\/ACCESS.2023.3336678","article-title":"Toward Enhanced Attack Detection and Explanation in Intrusion Detection System-Based IoT Environment Data","volume":"11","author":"Le","year":"2023","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100068","DOI":"10.1016\/j.csa.2024.100068","article-title":"Federated learning-based intrusion detection system for the internet of things using unsupervised and supervised deep learning models","volume":"3","author":"Pranggono","year":"2025","journal-title":"Cyber Secur. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"50078","DOI":"10.1109\/ACCESS.2021.3068961","article-title":"Anomaly-based intrusion detection by machine learning: A case study on probing attacks to an institutional network","volume":"9","author":"Tufan","year":"2021","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7164","DOI":"10.1109\/JIOT.2022.3229005","article-title":"Efficient and Lightweight Convolutional Networks for IoT Malware Detection: A Federated Learning Approach","volume":"10","author":"Hawash","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"143","DOI":"10.51936\/ayrt6204","article-title":"A secure edge computing model using machine learning and IDS to detect and isolate intruders","volume":"1","author":"Pohar","year":"2004","journal-title":"Adv. Methodol. Stat."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.aej.2024.10.032","article-title":"Sandpiper optimization with hybrid deep learning model for blockchain-assisted intrusion detection in iot environment","volume":"112","author":"Alkhonaini","year":"2025","journal-title":"Alex. Eng. J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103171","DOI":"10.1016\/j.rineng.2024.103171","article-title":"XAIEnsembleTL-IoV: A new eXplainable Artificial Intelligence ensemble transfer learning for zero-day botnet attack detection in the Internet of Vehicles","volume":"24","author":"Saheed","year":"2024","journal-title":"Results Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"115939","DOI":"10.1016\/j.chaos.2024.115939","article-title":"CPS-IoT-PPDNN: A new explainable privacy preserving DNN for resilient anomaly detection in Cyber-Physical Systems-enabled IoT networks","volume":"191","author":"Saheed","year":"2025","journal-title":"Chaos Solitons Fractals"},{"key":"ref_19","unstructured":"(2025, October 03). IEEE Xplore. Available online: https:\/\/ieeexplore.ieee.org\/."},{"key":"ref_20","unstructured":"(2025, October 03). Science Direct. Available online: https:\/\/www.sciencedirect.com\/."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jnca.2015.11.016","article-title":"A survey of network anomaly detection techniques","volume":"60","author":"Ahmed","year":"2016","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"100227","DOI":"10.1016\/j.iot.2020.100227","article-title":"Survey on IoT security: Challenges and solution using machine learning, artificial intelligence and blockchain technology","volume":"11","author":"Mohanta","year":"2020","journal-title":"Internet Things"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"86","DOI":"10.4018\/IJISP.2019010107","article-title":"A survey: Intrusion detection techniques for internet of things","volume":"13","author":"Choudhary","year":"2019","journal-title":"Int. J. Inf. Secur. Priv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.jnca.2017.02.009","article-title":"A survey of intrusion detection in Internet of Things","volume":"84","author":"Miani","year":"2017","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1186\/s42400-021-00077-7","article-title":"A critical review of intrusion detection systems in the internet of things: Techniques, deployment strategy, validation strategy, attacks, public datasets and challenges","volume":"4","author":"Khraisat","year":"2021","journal-title":"Cybersecurity"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13677-018-0123-6","article-title":"Intrusion detection systems for IoT-based smart environments: A survey","volume":"7","author":"Elrawy","year":"2018","journal-title":"J. Cloud Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103320","DOI":"10.1016\/j.adhoc.2023.103320","article-title":"Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: A survey","volume":"152","author":"Ali","year":"2024","journal-title":"Ad Hoc Netw."},{"key":"ref_28","first-page":"455","article-title":"A comprehensive survey on IoT attacks: Taxonomy, detection mechanisms and challenges","volume":"2","author":"Sasi","year":"2023","journal-title":"J. Inf. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"101306","DOI":"10.1016\/j.iot.2024.101306","article-title":"Energy-based approach for attack detection in IoT devices: A survey","volume":"27","author":"Merlino","year":"2024","journal-title":"Internet Things"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"11792","DOI":"10.1109\/ACCESS.2025.3526711","article-title":"Deep Learning-Based Intrusion Detection System For Detecting IoT Botnet Attacks: A Review","volume":"13","author":"Anbar","year":"2025","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100082","DOI":"10.1016\/j.csa.2024.100082","article-title":"A survey on intrusion detection system in IoT networks","volume":"3","author":"Rahman","year":"2025","journal-title":"Cyber Secur. Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"108055","DOI":"10.1016\/j.comcom.2025.108055","article-title":"AI-based malware detection in IoT networks within smart cities: A survey","volume":"233","author":"Alhamdi","year":"2025","journal-title":"Comput. Commun."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"119118","DOI":"10.1109\/ACCESS.2023.3327061","article-title":"IoT Network-Based Intrusion Detection Framework: A Solution to Process Ping Floods Originating from Embedded Devices","volume":"11","author":"Almorabea","year":"2023","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"27237","DOI":"10.1109\/ACCESS.2024.3367004","article-title":"A Systematic Literature Review on Host-Based Intrusion Detection Systems","volume":"12","author":"Akleylek","year":"2024","journal-title":"IEEE Access"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1109\/TNSM.2023.3242320","article-title":"Anomaly Detection in Social-Aware IoT Networks","volume":"20","author":"Tang","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"6639714","DOI":"10.1155\/2021\/6639714","article-title":"The Anomaly- And Signature-Based IDS for Network Security Using Hybrid Inference Systems","volume":"2021","author":"Einy","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_37","unstructured":"(2024, December 03). NS-3 Simulator. Available online: https:\/\/www.nsnam.org\/."},{"key":"ref_38","unstructured":"(2025, October 03). IoT Simulator. Available online: https:\/\/aws.amazon.com\/es\/solutions\/implementations\/iot-device-simulator\/."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"110005","DOI":"10.1016\/j.comnet.2023.110005","article-title":"SIDS: A federated learning approach for intrusion detection in IoT using Social Internet of Things","volume":"236","author":"Dara","year":"2023","journal-title":"Comput. Netw."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1109\/ACCESS.2023.3347778","article-title":"Artificial Intelligence-Based Intrusion Detection and Prevention in Edge-Assisted SDWSN With Modified Honeycomb Structure","volume":"12","author":"Kipongo","year":"2023","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"27433","DOI":"10.1109\/ACCESS.2023.3256277","article-title":"Hybrid Chain: Blockchain Enabled Framework for Bi-Level Intrusion Detection and Graph-Based Mitigation for Security Provisioning in Edge Assisted IoT Environment","volume":"11","author":"Sharadqh","year":"2023","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2287","DOI":"10.1007\/s11277-019-06986-8","article-title":"Machine Learning based Intrusion Detection Systems for IoT Applications","volume":"111","author":"Verma","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103331","DOI":"10.1016\/j.adhoc.2023.103331","article-title":"An ensemble learning framework for the detection of RPL attacks in IoT networks based on the genetic feature selection approach","volume":"152","author":"Osman","year":"2024","journal-title":"Ad Hoc Netw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.procs.2024.05.048","article-title":"A Blockchain-based Intrusion Detection\/Prevention Systems in IoT Network: A systematic review","volume":"236","author":"Shalabi","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"122198","DOI":"10.1016\/j.eswa.2023.122198","article-title":"HDA-IDS: A Hybrid DoS Attacks Intrusion Detection System for IoT by using semi-supervised CL-GAN","volume":"238","author":"Li","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"102588","DOI":"10.1016\/j.cose.2021.102588","article-title":"A comprehensive deep learning benchmark for IoT IDS","volume":"114","author":"Ahmad","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"101102","DOI":"10.1016\/j.iot.2024.101102","article-title":"M2VT-IDS: A multi-task multi-view learning architecture for designing IoT intrusion detection system","volume":"25","author":"Nie","year":"2024","journal-title":"Internet Things"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"63584","DOI":"10.1109\/ACCESS.2024.3396461","article-title":"Deep-IDS: A Real-Time Intrusion Detector for IoT Nodes Using Deep Learning","volume":"12","author":"Racherla","year":"2024","journal-title":"IEEE Access"},{"key":"ref_49","unstructured":"(2025, October 03). NSL-KDD. Available online: https:\/\/www.kaggle.com\/datasets\/hassan06\/nslkdd."},{"key":"ref_50","unstructured":"(2025, October 03). UNSW-NB15 Dataset. Available online: https:\/\/research.unsw.edu.au\/projects\/unsw-nb15-dataset."},{"key":"ref_51","unstructured":"(2025, October 03). CICIDS2017 Dataset. Available online: https:\/\/www.unb.ca\/cic\/datasets\/ids-2017.html."},{"key":"ref_52","unstructured":"(2025, October 03). BoT-IoT Dataset. Available online: https:\/\/research.unsw.edu.au\/projects\/bot-iot-dataset."},{"key":"ref_53","unstructured":"(2025, October 03). DS2OS Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/libamariyam\/ds2os-dataset."},{"key":"ref_54","unstructured":"(2025, October 03). IoTID20. Available online: https:\/\/www.kaggle.com\/datasets\/rohulaminlabid\/iotid20-dataset."},{"key":"ref_55","unstructured":"(2025, October 03). NB-IoT Dataset. Available online: https:\/\/ieee-dataport.org\/keywords\/nb-iot."},{"key":"ref_56","unstructured":"(2025, October 03). IoT-23 Dataset. Available online: https:\/\/www.stratosphereips.org\/datasets-iot23."},{"key":"ref_57","unstructured":"(2025, October 03). Ton_IoT Dataset. Available online: https:\/\/research.unsw.edu.au\/projects\/toniot-datasets."},{"key":"ref_58","unstructured":"(2025, October 03). MQTT-IoT 2020. Available online: https:\/\/ieee-dataport.org\/open-access\/mqtt-iot-ids2020-mqtt-internet-things-intrusion-detection-dataset."},{"key":"ref_59","unstructured":"(2025, October 03). IoT_malware Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/anaselmasry\/iot-malware."},{"key":"ref_60","unstructured":"(2025, October 03). Darpa Dataset. Available online: https:\/\/www.ll.mit.edu\/r-d\/datasets\/1999-darpa-intrusion-detection-evaluation-dataset."},{"key":"ref_61","unstructured":"(2025, October 03). N-BaIoT. Available online: https:\/\/archive.ics.uci.edu\/dataset\/442\/detection+of+iot+botnet+attacks+n+baiot."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"8357","DOI":"10.1109\/JIOT.2023.3234530","article-title":"OPTIMIST: Lightweight and Transparent IDS with Optimum Placement Strategy to Mitigate Mixed-Rate DDoS Attacks in IoT Networks","volume":"10","author":"Bhale","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.iotcps.2024.01.003","article-title":"Multi-objective optimization algorithms for intrusion detection in IoT networks: A systematic review","volume":"4","author":"Sharma","year":"2024","journal-title":"Internet Things Cyber Phys. Syst."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"100306","DOI":"10.1016\/j.iot.2020.100306","article-title":"A ZigBee Intrusion Detection System for IoT using Secure and Efficient Data Collection","volume":"12","author":"Sadikin","year":"2020","journal-title":"Internet Things"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"102943","DOI":"10.1016\/j.asej.2024.102943","article-title":"Tasmanian devil optimization with deep autoencoder for intrusion detection in IoT assisted unmanned aerial vehicle networks","volume":"15","author":"Negm","year":"2024","journal-title":"Ain Shams Eng. J."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"100656","DOI":"10.1016\/j.iot.2022.100656","article-title":"Deep learning-enabled anomaly detection for IoT systems","volume":"21","author":"Abusitta","year":"2023","journal-title":"Internet Things"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"104034","DOI":"10.1016\/j.cose.2024.104034","article-title":"IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networks","volume":"146","author":"Zohourian","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"100936","DOI":"10.1016\/j.iot.2023.100936","article-title":"Enhancing IoT network security through deep learning-powered Intrusion Detection System","volume":"24","author":"Bakhsh","year":"2023","journal-title":"Internet Things"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"110966","DOI":"10.1016\/j.knosys.2023.110966","article-title":"TS-IDS: Traffic-aware self-supervised learning for IoT Network Intrusion Detection","volume":"279","author":"Nguyen","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"103778","DOI":"10.1016\/j.cose.2024.103778","article-title":"M-RL: A mobility and impersonation-aware IDS for DDoS UDP flooding attacks in IoT-Fog networks","volume":"140","author":"Javanmardi","year":"2024","journal-title":"Comput. Secur."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"100297","DOI":"10.1016\/j.sintl.2024.100297","article-title":"GA-mADAM-IIoT: A new lightweight threats detection in the industrial IoT via genetic algorithm with attention mechanism and LSTM on multivariate time series sensor data","volume":"6","author":"Saheed","year":"2025","journal-title":"Sens. Int."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"104976","DOI":"10.1016\/j.jpdc.2024.104976","article-title":"Survey of federated learning in intrusion detection","volume":"195","author":"Zhang","year":"2025","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"102807","DOI":"10.1016\/j.inffus.2024.102807","article-title":"FedKD-IDS: A robust intrusion detection system using knowledge distillation-based semi-supervised federated learning and anti-poisoning attack mechanism","volume":"117","author":"Quyen","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"108693","DOI":"10.1016\/j.comnet.2021.108693","article-title":"Federated learning for malware detection in IoT devices","volume":"204","author":"Rey","year":"2022","journal-title":"Comput. Netw."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"12521","DOI":"10.1109\/JIOT.2023.3248259","article-title":"ADCL: Toward an Adaptive Network Intrusion Detection System Using Collaborative Learning in IoT Networks","volume":"10","author":"Ma","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"181560","DOI":"10.1109\/ACCESS.2020.3026260","article-title":"An intrusion detection system for internet of medical things","volume":"8","author":"Thamilarasu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2487","DOI":"10.1109\/TIFS.2024.3350379","article-title":"Static Multi Feature-Based Malware Detection Using Multi SPP-net in Smart IoT Environments","volume":"19","author":"Jeon","year":"2024","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"110724","DOI":"10.1016\/j.comnet.2024.110724","article-title":"An intrusion detection method combining variational auto-encoder and generative adversarial networks","volume":"253","author":"Li","year":"2024","journal-title":"Comput. Netw."},{"key":"ref_79","first-page":"221","article-title":"XA-GANomaly: An Explainable Adaptive Semi-Supervised Learning Method for Intrusion Detection Using GANomaly","volume":"76","author":"Han","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.aej.2023.09.023","article-title":"Deep learning-based intrusion detection approach for securing industrial Internet of Things","volume":"81","author":"Soliman","year":"2023","journal-title":"Alex. Eng. J."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"103385","DOI":"10.1016\/j.cose.2023.103385","article-title":"A new deep boosted CNN and ensemble learning based IoT malware detection","volume":"133","author":"Khan","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"108410","DOI":"10.1016\/j.compeleceng.2022.108410","article-title":"Deep malware detection framework for IoT-based smart agriculture","volume":"104","author":"Smmarwar","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.patrec.2021.11.023","article-title":"Design of Blockchain enabled intrusion detection model for detecting security attacks using deep learning","volume":"153","author":"Saveetha","year":"2022","journal-title":"Pattern Recognit. Lett."},{"key":"ref_84","unstructured":"Sunil, R.Y., Parimala, E.H., Bosco, V.S.J.P., Samuel Raj, A., Kumar, P.J.R.V., and Kolluru, V. (2024, January 18\u201320). Real-Time Adaptive Intrusion Detection System [RTPIDS] for Internet of Things Using Federated Learning and Blockchain. Proceedings of the 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Hafid, B., Ezzouhairi, A., and Haddouch, K. (2024, January 19\u201320). Strengthening Security in the Internet of Things (IoT): Integrated Approach of Intrusion Detection Systems (IDS) and Edge Computing. Proceedings of the 2024 3rd International Conference on Embedded Systems and Artificial Intelligence (ESAI), Fez, Morocco.","DOI":"10.1109\/ESAI62891.2024.10913549"},{"key":"ref_86","unstructured":"Loari, Y.Y.S., Bassole, D., Sawadogo, L.M., Koala, G., and Si\u00e9, O. (2024, January 17\u201319). IoT Devices Security Improvement Based on Collaborative Intrusion Detection System and Blockchain Technology. Proceedings of the 2024 International Conference on Computer and Applications (ICCA), Cairo, Egypt."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"25481","DOI":"10.1109\/JIOT.2023.3347492","article-title":"A Blockchain-Based Collaborative Intrusion Detection Systems Framework","volume":"11","author":"Alharbi","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"104925","DOI":"10.1016\/j.tifs.2025.104925","article-title":"Integrating AI with detection methods, IoT, and blockchain to achieve food authenticity and traceability from farm-to-table","volume":"158","author":"Liu","year":"2025","journal-title":"Trends Food Sci. Technol."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"103014","DOI":"10.1016\/j.cose.2022.103014","article-title":"A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks","volume":"125","author":"Alghamdi","year":"2023","journal-title":"Comput. Secur."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"102693","DOI":"10.1016\/j.cose.2022.102693","article-title":"Machine learning-based early detection of IoT botnets using network-edge traffic","volume":"117","author":"Kumar","year":"2022","journal-title":"Comput. Secur."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jpdc.2022.01.030","article-title":"A distributed intrusion detection system to detect DDoS attacks in blockchain-enabled IoT network","volume":"164","author":"Kumar","year":"2022","journal-title":"J. Parallel Distrib. Comput."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"119330","DOI":"10.1016\/j.eswa.2022.119330","article-title":"Implementation of intrusion detection model for DDoS attacks in Lightweight IoT Networks","volume":"215","author":"Khanday","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.icte.2025.02.012","article-title":"Distributed optimization for IoT attack detection using federated learning and Siberian Tiger optimizer","volume":"11","author":"Gupta","year":"2025","journal-title":"ICT Express"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"104446","DOI":"10.1016\/j.cose.2025.104446","article-title":"FLADEN: Federated Learning for Anomaly DEtection in IoT Networks","volume":"155","author":"Hendaoui","year":"2025","journal-title":"Comput. Secur."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"170308","DOI":"10.1007\/s11432-024-4463-0","article-title":"Let RFF do the talking: Large language model enabled lightweight RFFI for 6G edge intelligence","volume":"68","author":"Gao","year":"2025","journal-title":"Sci. China Inf. Sci."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Zheng, H., Gao, N., Cai, D., Jin, S., and Matthaiou, M. (2025). UAV Individual Identification via Distilled RF Fingerprints-Based LLM in ISAC Networks. arXiv.","DOI":"10.1109\/LWC.2025.3603423"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"101212","DOI":"10.1016\/j.iot.2024.101212","article-title":"SYN-GAN: A robust intrusion detection system using GAN-based synthetic data for IoT security","volume":"26","author":"Rahman","year":"2024","journal-title":"Internet Things"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"107751","DOI":"10.1016\/j.future.2025.107751","article-title":"RGAnomaly: Data reconstruction-based generative adversarial networks for multivariate time series anomaly detection in the Internet of Things","volume":"167","author":"Qian","year":"2025","journal-title":"Futur. Gener. Comput. Syst."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"101149","DOI":"10.1016\/j.iot.2024.101149","article-title":"Revolutionizing intrusion detection in industrial IoT with distributed learning and deep generative techniques","volume":"26","author":"Hamouda","year":"2024","journal-title":"Internet Things"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"110299","DOI":"10.1016\/j.comnet.2024.110299","article-title":"HSGAN-IoT: A hierarchical semi-supervised generative adversarial networks for IoT device classification","volume":"243","author":"Jin","year":"2024","journal-title":"Comput. Netw."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"112455","DOI":"10.1016\/j.asoc.2024.112455","article-title":"Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks","volume":"168","author":"Arafah","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"101589","DOI":"10.1016\/j.iot.2025.101589","article-title":"Explainable AI-based intrusion detection in IoT systems","volume":"31","author":"Li","year":"2025","journal-title":"Internet Things"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"101505","DOI":"10.1016\/j.iot.2025.101505","article-title":"A systematic evaluation of white-box explainable AI methods for anomaly detection in IoT systems","volume":"30","author":"Gummadi","year":"2025","journal-title":"Internet Things"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Saheed, Y.K., Misra, S., and Chockalingam, S. (2023, January 15). Autoencoder via DCNN and LSTM Models for Intrusion Detection in Industrial Control Systems of Critical Infrastructures. Proceedings of the 2023 IEEE\/ACM 4th International Workshop on Engineering and Cybersecurity of Critical Systems, EnCyCriS 2023, Melbourne, Australia.","DOI":"10.1109\/EnCyCriS59249.2023.00006"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.comcom.2022.10.001","article-title":"WOGRU-IDS\u2014An intelligent intrusion detection system for IoT assisted Wireless Sensor Networks","volume":"196","author":"Ramana","year":"2022","journal-title":"Comput. Commun."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"100186","DOI":"10.1016\/j.prime.2023.100186","article-title":"MUD enabled deep learning framework for anomaly detection in IoT integrated smart building","volume":"5","author":"Mirdula","year":"2023","journal-title":"E-Prime\u2014Adv. Electr. Eng. Electron. Energy"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"82199","DOI":"10.1109\/ACCESS.2023.3299589","article-title":"Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning","volume":"11","author":"Alamro","year":"2023","journal-title":"IEEE Access"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"9042","DOI":"10.1109\/JIOT.2019.2926365","article-title":"A Supervised Intrusion Detection System for Smart Home IoT Devices","volume":"6","author":"Anthi","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"119462","DOI":"10.1109\/ACCESS.2023.3325929","article-title":"IoT Network Anomaly Detection in Smart Homes Using Machine Learning","volume":"11","author":"Sarwar","year":"2023","journal-title":"IEEE Access"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Jaramillo-Alcazar, A., Govea, J., and Villegas-Ch, W. (2023). Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning. Sensors, 23.","DOI":"10.3390\/s23198286"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Patel, S.K. (2023). Improving intrusion detection in cloud-based healthcare using neural network. Biomed. Signal Process. Control, 83.","DOI":"10.1016\/j.bspc.2023.104680"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"102324","DOI":"10.1016\/j.scs.2020.102324","article-title":"Scalable machine learning-based intrusion detection system for IoT-enabled smart cities","volume":"61","author":"Rahman","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1109\/ACCESS.2022.3233775","article-title":"Transfer Learning Approach to IDS on Cloud IoT","volume":"11","author":"Okey","year":"2023","journal-title":"IEEE Access"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.iotcps.2023.12.003","article-title":"Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models","volume":"4","author":"Alwahedi","year":"2024","journal-title":"Internet Things Cyber-Phys. Syst."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/115\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T04:27:35Z","timestamp":1759897655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/4\/115"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,6]]},"references-count":114,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["make7040115"],"URL":"https:\/\/doi.org\/10.3390\/make7040115","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,6]]}}}