{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T01:18:41Z","timestamp":1772587121842,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"King Saud University","award":["RSPD2023R585"],"award-info":[{"award-number":["RSPD2023R585"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system\u2019s security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.<\/jats:p>","DOI":"10.3390\/s23167256","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T10:28:48Z","timestamp":1692354528000},"page":"7256","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4580-6617","authenticated-orcid":false,"given":"Himanshi","family":"Babbar","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8474-9435","authenticated-orcid":false,"given":"Shalli","family":"Rani","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}]},{"given":"Dipak Kumar","family":"Sah","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering and Application, GLA University, Mathura 281406, Uttar Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1233-1774","authenticated-orcid":false,"given":"Salman A.","family":"AlQahtani","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2601-9327","authenticated-orcid":false,"given":"Ali","family":"Kashif Bashir","sequence":"additional","affiliation":[{"name":"Department of Computing and Mathematics, Manchaster Metropolitian University, Manchaster M15 6BH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fernandez, G.C., and Xu, S. (2019, January 12\u201314). A Case Study on using Deep Learning for Network Intrusion Detection. Proceedings of the IEEE Military Communications Conference (MILCOM 2019), Norfolk, VA, USA.","DOI":"10.1109\/MILCOM47813.2019.9020824"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dhiman, P., Kukreja, V., Manoharan, P., Kaur, A., Kamruzzaman, M.M., Dhaou, I., and Iwendi, C. (2022). A novel deep learning model for detection of severity level of the disease in citrus fruits. 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