Improving IoT IDS with Harmony Search Feature Selection and ADASYN on UNSW-NB15 and CIC- IoT23

Improving IoT IDS with Harmony Search Feature Selection and ADASYN on UNSW-NB15 and CIC- IoT23 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Improving IoT IDS with Harmony Search Feature Selection and ADASYN on UNSW-NB15 and CIC- IoT23 Tibebu Bekele Shana, Mayank Agarwal, Ebenezer Esenogho, Samrat Mondal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8077281/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The proliferation of Internet of Things (IoT) devices and increasing frequency of cyber-attacks have resulted in significant security challenges. The Intrusion Detection System (IDS) is a crucial network component, as it helps to protect the system from different threats and vulnerabilities. Machine learning (ML) models have demonstrated great potential in detecting attacks on IoT systems, enhancing security through intelligent threat detection. However, the perfor- mance of ML classification models can be negatively influenced by the unbalanced class distributions found in real-world datasets. To identify various attack types and to enhance IDS, we propose ensembling the Harmony Search Algorithm for feature selection (RF-HSA) together with the Adaptive Synthetic Sampling (ADASYN) to overcome class imbalance. The ADASYN enhances class balancing by generating synthetic samples, focusing more on hard-to-learn instances near decision boundaries and this approach reduces bias, improves model generaliza- tion, and mitigates overfitting for imbalanced datasets. We employed prominent ML models such as Bagging, Gradient Boosting, LightGBM, and Random For- est to classify genuine traffic from intrusion traffic. Using the UNSW-NB15 and CIC-IoT23 datasets, the proposed classifier is evaluated against other distinct classifiers to offer a detailed IDS. Performance analysis is demonstrated using evaluation metrics such as recall, F1-score, accuracy, and precision, and the out- comes are compared with the existing baselines. Apart from evaluation metrics, the training and prediction times (sec) are computed with and without ADASYN for both datasets. The proposed approach achieves accuracy values of 96.92% for the UNSW-NB15 dataset and 99.94% for the CIC-IoT23 dataset and outperforms all evaluation measures compared to baseline models for multi-classification of the IDS. Based on empirical evidence, the proposed HSA-ADASYN approach effectively outperforms existing IDS in identifying and categorizing attacks. Intrusion detection Computational modeling Machine learning Feature Selection Class Imbalance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Dec, 2025 Reviews received at journal 27 Dec, 2025 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 27 Nov, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 20 Nov, 2025 Submission checks completed at journal 20 Nov, 2025 First submitted to journal 20 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8077281","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":551959522,"identity":"2a6cab17-da6f-4d4e-99cf-7224ddbf992b","order_by":0,"name":"Tibebu Bekele Shana","email":"","orcid":"","institution":"Indian Institute of Technology Patna","correspondingAuthor":false,"prefix":"","firstName":"Tibebu","middleName":"Bekele","lastName":"Shana","suffix":""},{"id":551959523,"identity":"2af021e9-0160-4e2a-8b27-ce141ca2ffda","order_by":1,"name":"Mayank Agarwal","email":"","orcid":"","institution":"Indian Institute of Technology 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IoT23","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover 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The Intrusion Detection System (IDS) is a crucial network component, as it helps to protect the system from different threats and vulnerabilities. Machine learning (ML) models have demonstrated great potential in detecting attacks on IoT systems, enhancing security through intelligent threat detection. However, the perfor- mance of ML classification models can be negatively influenced by the unbalanced class distributions found in real-world datasets. To identify various attack types and to enhance IDS, we propose ensembling the Harmony Search Algorithm for feature selection (RF-HSA) together with the Adaptive Synthetic Sampling (ADASYN) to overcome class imbalance. The ADASYN enhances class balancing by generating synthetic samples, focusing more on hard-to-learn instances near decision boundaries and this approach reduces bias, improves model generaliza- tion, and mitigates overfitting for imbalanced datasets. We employed prominent ML models such as Bagging, Gradient Boosting, LightGBM, and Random For- est to classify genuine traffic from intrusion traffic. Using the UNSW-NB15 and CIC-IoT23 datasets, the proposed classifier is evaluated against other distinct classifiers to offer a detailed IDS. Performance analysis is demonstrated using evaluation metrics such as recall, F1-score, accuracy, and precision, and the out- comes are compared with the existing baselines. Apart from evaluation metrics, the training and prediction times (sec) are computed with and without ADASYN for both datasets. The proposed approach achieves accuracy values of 96.92% for the UNSW-NB15 dataset and 99.94% for the CIC-IoT23 dataset and outperforms all evaluation measures compared to baseline models for multi-classification of the IDS. Based on empirical evidence, the proposed HSA-ADASYN approach effectively outperforms existing IDS in identifying and categorizing attacks.\u003c/p\u003e","manuscriptTitle":"Improving IoT IDS with Harmony Search Feature Selection and ADASYN on UNSW-NB15 and CIC- IoT23","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-29 01:21:57","doi":"10.21203/rs.3.rs-8077281/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-30T04:58:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-27T20:26:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-17T01:13:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234282175029735772140974302794730781259","date":"2025-12-01T07:54:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257575081499750428086432797215074276289","date":"2025-11-27T23:17:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20061329514291953463372419580191913000","date":"2025-11-27T15:21:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128679381133583134628171643718914173957","date":"2025-11-25T12:18:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T10:36:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-21T04:20:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-20T15:01:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Computing","date":"2025-11-20T14:56:50+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9215eea6-cc89-465d-89b3-326a0f8349f4","owner":[],"postedDate":"November 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T12:10:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-29 01:21:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8077281","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8077281","identity":"rs-8077281","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}