{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:36:03Z","timestamp":1772793363669,"version":"3.50.1"},"reference-count":48,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>The quick spread of Internet of Things (IoT) devices in smart homes has raised cybersecurity concerns, calling for smart, flexible, and active ways to reduce threats. Standard Intrusion Detection Systems (IDS) and basic anomaly detection have trouble spotting new attacks and changing cyber threats. To fix these problems, this article puts forward a Reinforcement Learning-Based Adaptive Threat Mitigation (RL-ATM) model. It uses methods like reinforcement learning, deep learning, and data mining to make smart home cybersecurity better by reducing risks and finding network issues. Tests show that RL-ATM does much better than current cybersecurity options, such as signature-based IDS, anomaly-based machine learning models, and deep reinforcement learning (DRL) setups. The model got an accuracy of 98.87%, a precision of 97.49%, a recall of 98.36%, and a low false positive rate (FPR) of 1.8%. This makes it a dependable cybersecurity choice for actual smart home use. A comparison shows that standard IDS models are only 87.42% accurate with an FPR of 6.3%. Anomaly-based ML methods improve accuracy to 91.15% but still have an FPR of 4.9%. Hybrid Convolutional Neural Network (CNN) + Reinforcement Learning models reach 92.84% accuracy but can\u2019t adapt to changing attacks in real time. This makes RL-ATM a better choice for dependable detection and response. This work adds to smart home cybersecurity by giving a scalable, adaptive, and independent artificial intelligence (AI)-driven security system. It can lower cyber threats in real time.<\/jats:p>","DOI":"10.7717\/peerj-cs.3612","type":"journal-article","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T08:19:07Z","timestamp":1772785147000},"page":"e3612","source":"Crossref","is-referenced-by-count":0,"title":["Cybersecurity risk mitigation and network anomaly detection in smart homes using machine learning and data mining"],"prefix":"10.7717","volume":"12","author":[{"given":"Mahmood Hijran","family":"Abdulrazaq","sequence":"first","affiliation":[{"name":"Computer Engineering, Gazi University Institute of Science, Ankara, Turkey"}]},{"given":"Cemal","family":"Ko\u00e7ak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Gazi University Ankara, Ankara, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3880-3039","authenticated-orcid":true,"given":"Saadin","family":"Oyucu","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Technology, Gazi University Ankara, Ankara, Turkey"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3729-8170","authenticated-orcid":true,"given":"Bur\u00e7ak","family":"Asal","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Adana Alparslan T\u00fcrke\u015f Science and Technology University, Adana, Turkey"}]}],"member":"4443","published-online":{"date-parts":[[2026,3,6]]},"reference":[{"key":"10.7717\/peerj-cs.3612\/ref-1","doi-asserted-by":"crossref","DOI":"10.1109\/PAIS62114.2024.10541295","article-title":"Reinforcement learning approach for IoT security using CyberBattleSim: a simulation-based study","author":"Abid","year":"2024"},{"issue":"2","key":"10.7717\/peerj-cs.3612\/ref-2","doi-asserted-by":"publisher","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 of Things"},{"key":"10.7717\/peerj-cs.3612\/ref-3","doi-asserted-by":"publisher","first-page":"133","DOI":"10.5220\/0012437900003705","article-title":"HOMEFUS: a privacy and security-aware model for IoT data fusion in smart connected homes","author":"Adewole","year":"2024"},{"key":"10.7717\/peerj-cs.3612\/ref-4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/inmic64792.2024.11004309","article-title":"Enhancing security in smart home IoT networks: vulnerability analysis and mitigation strategies","author":"Ahmed","year":"2024"},{"issue":"23","key":"10.7717\/peerj-cs.3612\/ref-5","doi-asserted-by":"publisher","first-page":"7741","DOI":"10.1109\/jsen.2017.2713645","article-title":"Sustainable homecare monitoring system by sensing electricity data","volume":"17","author":"Alcala","year":"2017","journal-title":"IEEE Sensors Journal"},{"issue":"1","key":"10.7717\/peerj-cs.3612\/ref-6","doi-asserted-by":"publisher","first-page":"1121","DOI":"10.1186\/s42400-022-00133-w","article-title":"An ensemble deep learning based IDS for IoT using Lambda architecture","volume":"6","author":"Alghamdi","year":"2023","journal-title":"Cybersecurity"},{"key":"10.7717\/peerj-cs.3612\/ref-7","article-title":"A hybrid intrusion detection system for smart home security based on machine learning and user behavior. 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