{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T13:28:08Z","timestamp":1770557288525,"version":"3.49.0"},"reference-count":46,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The comparison of low-rank-based learning models for multi-label categorization of attacks for intrusion detection datasets is presented in this work. In particular, we investigate the performance of three low-rank-based machine learning (LR-SVM) and deep learning models (LR-CNN), (LR-CNN-MLP) for classifying intrusion detection data: Low Rank Representation (LRR) and Non-negative Low Rank Representation (NLR). We also look into how these models\u2019 performance is affected by hyperparameter tweaking by using Guassian Bayes Optimization. The tests has been run on merging two intrusion detection datasets that are available to the public such as BoT-IoT and UNSW- NB15 and assess the models\u2019 performance in terms of key evaluation criteria, including precision, recall, F1 score, and accuracy. Nevertheless, all three models perform noticeably better after hyperparameter modification. The selection of low-rank-based learning models and the significance of the hyperparameter tuning log for multi-label classification of intrusion detection data have been discussed in this work. A hybrid security dataset is used with low rank factorization in addition to SVM, CNN and CNN-MLP. The desired multilabel results have been obtained by considering binary and multi-class attack classification as well. Low rank CNN-MLP achieved suitable results in multilabel classification of attacks. Also, a Gaussian-based Bayesian optimization algorithm is used with CNN-MLP for hyperparametric tuning and the desired results have been achieved using c and \u03b3 for SVM and \u03b1 and \u03b2 for CNN and CNN-MLP on a hybrid dataset. The results show the label UDP is shared among analysis, DoS and shellcode. The accuracy of classifying UDP among three classes is 98.54%.<\/jats:p>","DOI":"10.3390\/s23198333","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T07:37:50Z","timestamp":1696837070000},"page":"8333","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["HOMLC-Hyperparameter Optimization for Multi-Label Classification of Intrusion Detection Data for Internet of Things Network"],"prefix":"10.3390","volume":"23","author":[{"given":"Ankita","family":"Sharma","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 Applications, GLA University, Mathura 281406, Uttar Pradesh, India"}]},{"given":"Zahid","family":"Khan","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-0757","authenticated-orcid":false,"given":"Wadii","family":"Boulila","sequence":"additional","affiliation":[{"name":"Robotics and Internet-of-Things Laboratory, Prince Sultan University, Riyadh 12435, Saudi Arabia"},{"name":"RIADI Laboratory, National School of Computer Sciences, University of Manouba, Manouba 2010, Tunisia"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Iwendi, C., Khan, S., Anajemba, J., Mittal, M., Alenezi, M., and Alazab, M. 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