Detection of Attacks with An Adversarial Machine Learning Approach

Detection of Attacks with An Adversarial Machine Learning Approach | 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 Detection of Attacks with An Adversarial Machine Learning Approach Taha Akhlaghpasandi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4096674/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Machine learning methods are widely used in various domains, and the analysis of attacks is no exception. Various types of attacks occur daily. Therefore, examining each of them by human experts is becoming increasingly difficult due to the limited number of experts compared to the increasing number of attacks and the possibility of human error in detecting attacks, making it a tedious and almost impossible task. In recent years, significant efforts have been made to design a machine learning model or deep learning for intrusion detection. These models have been built with different accuracies using machine learning algorithms such as RF , SVM , Decision tree, Logistic Regression, Naive Bayes, DNN , ANN , CNN , RNN , LSTM , and GRU . Groups have created various models with different accuracies using machine learning or deep learning. In all cases, a good level of accuracy has been achieved, but none of them have exposed their model to attacks to evaluate their model's ability. In other words, none of them have subjected their designed model to attacks to assess their model's own capabilities. The aim of this research is to propose a method to improve the intrusion detection results using machine learning methods. Machine learning methods are continuously evolving and are constantly being replaced by methods that have better performance, processing power, efficiency, and accuracy. In our proposed method, in addition to building an acceptable model with good accuracy, we attack our model using adversarial attack methods. GAN neural networks, as one of the frameworks suitable for applying adversarial attacks, consist of generative models that produce new data similar to the training data. Network attacks machine learning deep learning intrusion detection adversarial attacks Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4096674","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":293008476,"identity":"b1504efb-403b-461f-902a-c660a268972a","order_by":0,"name":"Taha Akhlaghpasandi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACNv7+7z8YGA4wMLA3ALkGFoS18EkcMJA4ANLCcwCkRYKwFjmGBKgWiQQQnwgtbAwHEow/1NyR55/5/OqGHwUSDPzt3Qn4tTA3HEg4cOyZ4YzbOWU3e4AOkzhzdgMBWw42HDjAdpix4XZO2g0eoBYDiVxCWpIZGw78O2w//+aZtJt/iNOSxsxwsO1w4oYb7MduE2eLxBk2hrN9h5M3nslhuy1jIMFD0C/y/T1sDBXfDtvOO3782c03f2zk+Nt78WtBAjwGYJJY5SDA/oAU1aNgFIyCUTCCAADh5k8qCePO1gAAAABJRU5ErkJggg==","orcid":"","institution":"K.N.Toosi University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Taha","middleName":"","lastName":"Akhlaghpasandi","suffix":""}],"badges":[],"createdAt":"2024-03-14 02:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4096674/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4096674/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63587557,"identity":"d6112771-c532-4ede-81cb-e8f072221a7b","added_by":"auto","created_at":"2024-08-30 02:00:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1300653,"visible":true,"origin":"","legend":"","description":"","filename":"DetectionofAttacks.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4096674/v1_covered_bc7878e7-be0c-4142-974b-ad4b7dc6d0bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Detection of Attacks with An Adversarial Machine Learning Approach","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Network attacks, machine learning, deep learning, intrusion detection, adversarial attacks","lastPublishedDoi":"10.21203/rs.3.rs-4096674/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4096674/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachine learning methods are widely used in various domains, and the analysis of attacks is no exception. Various types of attacks occur daily. Therefore, examining each of them by human experts is becoming increasingly difficult due to the limited number of experts compared to the increasing number of attacks and the possibility of human error in detecting attacks, making it a tedious and almost impossible task. In recent years, significant efforts have been made to design a machine learning model or deep learning for intrusion detection. These models have been built with different accuracies using machine learning algorithms such as RF\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e, SVM\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e, Decision tree, Logistic Regression, Naive Bayes, DNN\u003ca class=\"FNLink\" href=\"#Fn3\" id=\"#FNLinkFn3\"\u003e\u003c/a\u003e, ANN\u003ca class=\"FNLink\" href=\"#Fn4\" id=\"#FNLinkFn4\"\u003e\u003c/a\u003e, CNN\u003ca class=\"FNLink\" href=\"#Fn5\" id=\"#FNLinkFn5\"\u003e\u003c/a\u003e, RNN\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e, LSTM\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e, and GRU\u003ca class=\"FNLink\" href=\"#Fn8\" id=\"#FNLinkFn8\"\u003e\u003c/a\u003e. Groups have created various models with different accuracies using machine learning or deep learning. In all cases, a good level of accuracy has been achieved, but none of them have exposed their model to attacks to evaluate their model's ability. In other words, none of them have subjected their designed model to attacks to assess their model's own capabilities.\u003c/p\u003e \u003cp\u003eThe aim of this research is to propose a method to improve the intrusion detection results using machine learning methods. Machine learning methods are continuously evolving and are constantly being replaced by methods that have better performance, processing power, efficiency, and accuracy. In our proposed method, in addition to building an acceptable model with good accuracy, we attack our model using adversarial attack methods. GAN \u003ca class=\"FNLink\" href=\"#Fn9\" id=\"#FNLinkFn9\"\u003e\u003c/a\u003eneural networks, as one of the frameworks suitable for applying adversarial attacks, consist of generative models that produce new data similar to the training data.\u003c/p\u003e","manuscriptTitle":"Detection of Attacks with An Adversarial Machine Learning Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-23 10:07:06","doi":"10.21203/rs.3.rs-4096674/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"987638b3-7731-4a32-b53a-b9ebf228c67e","owner":[],"postedDate":"April 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-08T09:23:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-23 10:07:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4096674","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4096674","identity":"rs-4096674","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}