[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118232-en":3,"doc-seo-118232-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118232,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Predicting IDS and IPS attacks using Machine Learning - Poster","Poster presents an undergraduate research effort on predicting IDS (Intrusion Detection System) and IPS (Intrusion Prevention System) attacks using machine learning models. The study tests multiple attack categories including DoS, Probe, R2L, and U2R, comparing binary vs multi-class classification setups and evaluating models across different datasets. Results highlight high accuracy for deep learning approaches, especially RNN and LSTM variants, while several alternative ML models perform better on specific attacks. The work focuses on identifying the most effective modeling choices to improve prediction efficiency and reliability.","University of Arkansas, Fayetteville  \nScholarWorks@UARK  \n\n| Electrical Engineering Research Experience for Undergraduates | Electrical Engineering |\n| --- | --- |\n| 2024\u003Cbr>Predicting IDS and IPS attacks using Machine Learning\u003Cbr>Bishop Butler\u003Cbr>University of Arkansas, Fayetteville\u003Cbr>Follow this and additional works at: [https://scholarworks.uark.edu/elegreu](https://scholarworks.uark.edu/elegreu)\u003Cbr> Part of the Electrical and Computer Engineering Commons |  |\n\nCitation  \nButler, B. (2024) . Predicting IDS and IPS attacks using Machine Learning. Electrical Engineering Research Experience for Undergraduates. Retrieved from [https://scholarworks.uark.edu/elegreu/1](https://scholarworks.uark.edu/elegreu/1)  \n[This Poster is brought to you for free and open access by the Electrical Engineering at ScholarWorks@UARK. It has](This Poster is brought to you for free and open access by the Electrical Engineering at ScholarWorks@UARK. It has)[ ](This Poster is brought to you for free and open access by the Electrical Engineering at ScholarWorks@UARK. It has)been accepted for inclusion in Electrical Engineering Research Experience for Undergraduates by an authorized administrator of ScholarWorks@UARK. For more information, please contact [scholar@uark.edu](scholar@uark.edu),  \n[uarepos@uark.edu](uarepos@uark.edu).  \nBishop Butler University of Arkansas  \nPredicting IDS and IPS attacks using Machine Learning  \n| Problem | Initial Experimentation |  | Results |  |  |  |  |\n| --- | --- | --- | --- | --- | --- | --- | --- |\n|  |  |  | 1 |  |  | Accuracy Over Epochs |  |\n| • IDS and IPS systems don’t predict attacks\u003Cbr>• Could make them much more efficient\u003Cbr>• Test what models can best predict the attacks |  |  | Accuracy | 0.95\u003Cbr>0.9\u003Cbr>0.85\u003Cbr>0.8\u003Cbr>0.75\u003Cbr>0.7 | \u003Cbr> RNN(Test_21)\u003Cbr> RNN(Test+)\u003Cbr>~~ ~~~~ LSTM(Test+) ~~\u003Cbr>\u003Cbr>1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20\u003Cbr>Epochs |  |  |\n| Types of Attacks |  |  |  |  |  |  |  |\n| 4 different types of attacks tested\u003Cbr>• DoS (Denial of Service)\u003Cbr>• Probe\u003Cbr>• R2L (Remote-to-Local)\u003Cbr>• U2R (User-to-Root) |  |  |  |  |  |  |  |\n|  |  |  | Model |  |  | Dataset | Final Accuracy |\n|  |  | Architecture |  |  |  |  |  |\n|  |  |  | RNN |  |  | Test21 | 96.09% |\n|  |  | RNN Model |  |  |  |  |  |\n|  |  |  | RNN |  |  | Test+ | 99.66% |\n|  |  |  | LSTM |  |  | Test+ | 99.88% |\n|  |  |  | MLP |  |  | Test21 | 65.22% |\n|  |  |  | RF |  |  | Test21 | 65.13% |\n| Approach |  |  |  |  |  |  |  |\n|  |  |  | DT |  |  | Test21 | 63.13% |\n| • Different ways of classifying attacks (binary vs multi)\u003Cbr>• Different test datasets\u003Cbr>• Different models (RNN vs alternative ML models) | Input layer\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr>\u003Cbr> | LSTM layer\u003Cbr>\u003Cbr>Dense layer\u003Cbr>\u003Cbr> | SVM |  |  | Test21 | 54.82% |\n|  |  |  | GNB |  |  | Test21 | 59.44% |\n|  |  |  | \u003Cbr>Summary |  |  |  |  |\n|  |  |  | • DL models had great accuracy\u003Cbr>• Alternative ML models had good accuracy for specific attacks\u003Cbr>• RNN model looks to be the best choice |  |  |  |  |","cbCaiuf9irwudto1","https://ap.wps.com/l/cbCaiuf9irwudto1","pdf",593893,1,2,"English","en",105,"# Problem\n## Initial experimentation\n## Types of attacks tested\n# Approach\n## Classification setup\n## Model comparisons\n# Results\n## Accuracy over epochs\n## Final accuracy by model and dataset\n# Summary","[{\"question\":\"What problem does the poster address?\",\"answer\":\"It investigates whether IDS and IPS attacks can be predicted using machine learning and which models perform best.\"},{\"question\":\"Which attack types are tested in the experiments?\",\"answer\":\"The experiments test four attack types: DoS, Probe, R2L, and U2R.\"},{\"question\":\"How do deep learning models compare with alternative ML models?\",\"answer\":\"The poster reports that deep learning models achieve great accuracy overall, while alternative ML models show good accuracy for specific attacks, depending on the dataset and attack type.\"}]","Predicting IDS and IPS attacks using Machine Learning - Poster | PDF",1785682472,5,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":28},"predicting-ids-and-ips-attacks-using-machine-learning-poster","",{"@graph":36,"@context":84},[37,53,67],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,47,50],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":21},"https://docshare.wps.com/document/","Document",{"item":48,"name":12,"@type":43,"position":49},"https://docshare.wps.com/document/technology/",3,{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/predicting-ids-and-ips-attacks-using-machine-learning-poster/118232/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"What problem does the poster address?","Question",{"text":74,"@type":75},"It investigates whether IDS and IPS attacks can be predicted using machine learning and which models perform best.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"Which attack types are tested in the experiments?",{"text":79,"@type":75},"The experiments test four attack types: DoS, Probe, R2L, and U2R.",{"name":81,"@type":72,"acceptedAnswer":82},"How do deep learning models compare with alternative ML models?",{"text":83,"@type":75},"The poster reports that deep learning models achieve great accuracy overall, while alternative ML models show good accuracy for specific attacks, depending on the dataset and attack type.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":91},[92,96,100,104,108,111,116,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":46,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":105,"show_sort_weight":106,"slug":107},"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":109,"slug":110},50,"technology",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},7,"Healthcare",40,"healthcare",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},8,"Research & Report",30,"research-report",{"id":122,"doc_module":4,"doc_module_name":46,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":29,"slug":136},19,"General","general"]