Vampire Squid Optimization Algorithm for Bioinspired Energy Efficient Swarm Intelligence in Cyber Threat Detection

Vampire Squid Optimization Algorithm for Bioinspired Energy Efficient Swarm Intelligence in Cyber Threat Detection | 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 Vampire Squid Optimization Algorithm for Bioinspired Energy Efficient Swarm Intelligence in Cyber Threat Detection Ali Mohammed Alqaraghuli, Abdullahi Ibrahim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7382015/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Discover Computing → Version 1 posted 15 You are reading this latest preprint version Abstract This paper presents the Vampire Squid Optimization Algorithm (VSOA), a novel energy-efficient, bio-inspired metaheuristic designed to optimize network-based intrusion detection systems (IDS) by jointly selecting features and tuning classifier hyperparameters. Inspired by the deep-sea vampire squid’s dual strategy of passive drifting and rapid striking, VSOA alternates between adaptive, diversity-driven exploration and shrinking-radius, Gaussian-based exploitation—executed under a strict evaluation budget to minimize computational overhead. VSOA is integrated with a Support Vector Machine (SVM) classifier using an RBF kernel, where each candidate solution encodes both a binary feature mask and real-valued hyperparameters (C, γ). Through iterative fitness evaluation, the algorithm searches for compact, high-performing IDS configurations that balance accuracy with efficiency. On the NSL-KDD dataset, VSOA achieves 95.0% accuracy, 93.0% recall, 94.0% precision, and a 5.0% false-positive rate, outperforming PSO and GA by 3.8% in accuracy and reducing false alarms by 22%. On UNSW-NB15, it reaches 93.2% accuracy and 92.5% F₁-score, again surpassing the best baseline by over 3.5%. VSOA converges rapidly reaching 90% accuracy in 60 ± 5 iterations (≈3,000 ± 200 evaluations), about half the time required by competing methods. It also reduces total evaluations and runtime by up to 25%, completing optimization in 120 seconds using 9,800 evaluations. These results confirm that VSOA, when coupled with SVM, delivers accurate, compact, and computationally efficient IDS solutions, making it ideal for real-time, resource-constrained environments such as IoT gateways and edge devices. Vampire Squid Optimization Algorithm (VSOA) intrusion detection feature selection swarm intelligence metaheuristic NSL-KDD UNSW-NB15 energy-efficient optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 24 Oct, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 14 Oct, 2025 Reviewers agreed at journal 12 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers agreed at journal 07 Oct, 2025 Reviews received at journal 06 Oct, 2025 Reviewers agreed at journal 06 Oct, 2025 Reviewers invited by journal 06 Oct, 2025 Editor assigned by journal 28 Sep, 2025 Submission checks completed at journal 07 Sep, 2025 First submitted to journal 07 Sep, 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-7382015","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529674387,"identity":"fa0451ce-d512-490b-bf69-910f030f79a7","order_by":0,"name":"Ali Mohammed Alqaraghuli","email":"data:image/png;base64,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","orcid":"","institution":"Altınbaş University","correspondingAuthor":true,"prefix":"","firstName":"Ali","middleName":"Mohammed","lastName":"Alqaraghuli","suffix":""},{"id":529674388,"identity":"7978e661-dc42-43e0-ad4e-3ea7253c4a7a","order_by":1,"name":"Abdullahi Ibrahim","email":"","orcid":"","institution":"Altınbaş University","correspondingAuthor":false,"prefix":"","firstName":"Abdullahi","middleName":"","lastName":"Ibrahim","suffix":""}],"badges":[],"createdAt":"2025-08-15 14:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7382015/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7382015/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10791-025-09824-7","type":"published","date":"2025-12-10T15:59:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93722112,"identity":"b90a3919-bbe7-404b-8872-01947f8f4b11","added_by":"auto","created_at":"2025-10-16 23:46:35","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":700496,"visible":true,"origin":"","legend":"","description":"","filename":"PaperAli11.docx","url":"https://assets-eu.researchsquare.com/files/rs-7382015/v1/2a18f7ba82355c2f99562cb0.docx"},{"id":93722113,"identity":"dd683a6c-c5b5-49c4-810c-ecdbf2927086","added_by":"auto","created_at":"2025-10-16 23:46:35","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5124,"visible":true,"origin":"","legend":"","description":"","filename":"1df294a05b20415282765eed90a6f2cf.json","url":"https://assets-eu.researchsquare.com/files/rs-7382015/v1/5c7b2445e5ee47820e7a7796.json"},{"id":98244144,"identity":"b4deaa0a-41e6-4875-a2ee-79dd83e5b8aa","added_by":"auto","created_at":"2025-12-15 16:13:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":906180,"visible":true,"origin":"","legend":"","description":"","filename":"PaperAli11.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7382015/v1_covered_35f2e88b-e541-4e0e-b519-0590aa187085.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vampire Squid Optimization Algorithm for Bioinspired Energy Efficient Swarm Intelligence in Cyber Threat Detection","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 Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vampire Squid Optimization Algorithm (VSOA), intrusion detection, feature selection, swarm intelligence, metaheuristic, NSL-KDD, UNSW-NB15, energy-efficient optimization","lastPublishedDoi":"10.21203/rs.3.rs-7382015/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7382015/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This paper presents the Vampire Squid Optimization Algorithm (VSOA), a novel energy-efficient, bio-inspired metaheuristic designed to optimize network-based intrusion detection systems (IDS) by jointly selecting features and tuning classifier hyperparameters. Inspired by the deep-sea vampire squid’s dual strategy of passive drifting and rapid striking, VSOA alternates between adaptive, diversity-driven exploration and shrinking-radius, Gaussian-based exploitation—executed under a strict evaluation budget to minimize computational overhead. VSOA is integrated with a Support Vector Machine (SVM) classifier using an RBF kernel, where each candidate solution encodes both a binary feature mask and real-valued hyperparameters (C, γ). Through iterative fitness evaluation, the algorithm searches for compact, high-performing IDS configurations that balance accuracy with efficiency. On the NSL-KDD dataset, VSOA achieves 95.0% accuracy, 93.0% recall, 94.0% precision, and a 5.0% false-positive rate, outperforming PSO and GA by 3.8% in accuracy and reducing false alarms by 22%. On UNSW-NB15, it reaches 93.2% accuracy and 92.5% F₁-score, again surpassing the best baseline by over 3.5%. VSOA converges rapidly reaching 90% accuracy in 60 ± 5 iterations (≈3,000 ± 200 evaluations), about half the time required by competing methods. It also reduces total evaluations and runtime by up to 25%, completing optimization in 120 seconds using 9,800 evaluations. These results confirm that VSOA, when coupled with SVM, delivers accurate, compact, and computationally efficient IDS solutions, making it ideal for real-time, resource-constrained environments such as IoT gateways and edge devices.","manuscriptTitle":"Vampire Squid Optimization Algorithm for Bioinspired Energy Efficient Swarm Intelligence in Cyber Threat Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-16 23:46:31","doi":"10.21203/rs.3.rs-7382015/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-24T07:50:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-22T13:38:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-14T20:27:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"11690878258892220395210714922901020347","date":"2025-10-12T06:16:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T05:31:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161178472632829839087868386455666517584","date":"2025-10-11T05:03:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-09T05:10:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333401956398878168389419763858236757290","date":"2025-10-08T05:52:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50460445333023551684552594842527508424","date":"2025-10-07T04:11:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-06T06:46:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96857165219644559700402828215784818424","date":"2025-10-06T04:08:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-06T04:06:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-29T03:35:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-07T15:35:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Computing","date":"2025-09-07T15:34:15+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":"d38e6180-d1dd-4ee9-9baa-4e3a6aacf250","owner":[],"postedDate":"October 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:06:12+00:00","versionOfRecord":{"articleIdentity":"rs-7382015","link":"https://doi.org/10.1007/s10791-025-09824-7","journal":{"identity":"discover-computing","isVorOnly":false,"title":"Discover Computing"},"publishedOn":"2025-12-10 15:59:36","publishedOnDateReadable":"December 10th, 2025"},"versionCreatedAt":"2025-10-16 23:46:31","video":"","vorDoi":"10.1007/s10791-025-09824-7","vorDoiUrl":"https://doi.org/10.1007/s10791-025-09824-7","workflowStages":[]},"version":"v1","identity":"rs-7382015","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7382015","identity":"rs-7382015","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}