[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123731-en":3,"doc-seo-123731-105":30,"detail-sidebar-cat-0-en-105":83},{"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},123731,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks","The study addresses the growing security challenges of the expanding internet and the increasing sophistication of cyberattacks, aiming to improve detection of network intrusions. It analyzes remote-to-local (R2L) intrusion detection using an ensemble classifier formed by combining three machine learning algorithms, and evaluates performance against single-model approaches. Results report that the ensemble classifier achieves an overall efficiency of 99.8% while single models show lower accuracy on the tested dataset. The work frames intrusion detection as a practical defense measure for sensitive data transmission.","Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks  \nAdemola Abdulkareem1, Tobiloba Emmanuel Somefun1, Adesina Lambe Mutalub2,  \nAdewale Adeyinka1  \n1Department of Electrical and Information Engineering, Covenant University, Ota, Nigeria 2Department of Electrical and Computer Engineering, Kwara State University, Kwara State, Nigeria  \nArticle history:  \nReceived Oct 3, 2022 Revised Mar 15, 2023 Accepted Apr 3, 2023  \nKeywords:  \nArtificial neural Ensemble classifier Intrusion detection system Machine learning Networks attack  \nCorresponding Author:  \nSince the invention of the internet for military and academic research purposes, it has evolved to meet the demands of the increasing number of users on the network, who have their scope beyond military and academics. As the scope of the network expanded maintaining its security became a matter of increasing importance. With various users and interconnections of more diversified networks, the internet needs to be maintained as securely as possible for the transmission of sensitive information to be one hundred percent safe; several anomalies may intrude on private networks. Several research works have been released around network security and this research seeks to add to the already existing body of knowledge by expounding on these attacks, proffering efficient measures to detect network intrusions, and introducing an ensemble classifier: a combination of 3 different machine learning algorithms. An ensemble classifier is used for detecting remote to local (R2L) attacks, which showed the lowest level of accuracy when the network dataset is tested using single machine learning models but the ensemble classifier gives an overall efficiency of 99.8% .  \nThis is an open access article under the CC BY-SA license.  \nTobiloba Emmanuel Somefun  \nDepartment of Electrical and Information Engineering, Covenant University Canaan Land, KM 10, Idiroko Rood, P. M. B. 1023, Ota, Ogun State, Nigeria Email: [tobi.shomefun@covenantuniversity.edu.ng](tobi.shomefun@covenantuniversity.edu.ng)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nAccess to the internet is very crucial to every business and individual in the 21 st century [1], [2] . It is nearly impossible to compete in today’s business world without staying connected to the world and customers. Staying connected to the internet is advantageous in the business world, but these advantages are not equipped to eliminate the accompanying threats, and it would be a disaster in this 21 st century cyber-age and cyberspace if the power of a single click on the internet is ever underestimated [3], [4] . The possibility of these threats gave rise to the need for protective measures on the internet [5], [6] . Many confidential transactions occur every second. These exchanges on the web give an approach to unfrosted gatherings outside to obtain entrance into an organization’s private organization and mess with the inside climate, data, assets, and structure. Network security helps us maintain the authorized access of data from hackers and authenticated data transfers, and we achieve the security of the network when a firewall is installed and turned ON.  \nWith the rise in internet and network use [7], the need for security has become tantamount to user’s convictions and interest to perform sensitive functions and activities on the internet or any cloud-based network system [8]–[10] . As the internet evolves, likewise the various malicious software hosted on the network and the attacks have become increasingly sophisticated [11] . In a 2017 report released by Symantec,  \non internet security threat, it recorded over three billion zero-day assaults in 2016, this implied that the assaults were gaining popularity and becoming increasingly common unlike before [12] . The 2017 data breach statistics recorded around nine billion lost or hijacked information records since 2013. A Symantec report track","cbCaidoHnx7L4Ysw","https://ap.wps.com/l/cbCaidoHnx7L4Ysw","pdf",482377,1,10,"English","en",105,"# Introduction\n## Network security challenges and evolving threats\n## Need for protective measures and intrusion detection\n## Malware, data breaches, and detection limitations","[{\"question\":\"What performance level is reported for the ensemble approach?\",\"answer\":\"When evaluated on the network dataset, the ensemble classifier is reported to provide an overall efficiency of 99.8%, outperforming single machine learning models for R2L attacks.\"}]","Experimental analysis of intrusion detection systems using machine learning algorithms and artificial neural networks | PDF",1785818235,25,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"experimental-analysis-of-intrusion-detection-systems-using-machine-learning-algorithms-and-artificial-neural-networks","",{"@graph":36,"@context":77},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/experimental-analysis-of-intrusion-detection-systems-using-machine-learning-algorithms-and-artificial-neural-networks/123731/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What performance level is reported for the ensemble approach?","Question",{"text":75,"@type":76},"When evaluated on the network dataset, the ensemble classifier is reported to provide an overall efficiency of 99.8%, outperforming single machine learning models for R2L attacks.","Answer","https://schema.org",{"og:url":52,"og:type":79,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":81,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":84},[85,89,93,97,102,107,112,115,120,123,126],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":86,"show_sort_weight":87,"slug":88},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":90,"show_sort_weight":91,"slug":92},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Exam",70,"exam",{"id":98,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},5,"Comic",60,"comic",{"id":103,"doc_module":4,"doc_module_name":46,"category_name":104,"show_sort_weight":105,"slug":106},6,"Technology",50,"technology",{"id":108,"doc_module":4,"doc_module_name":46,"category_name":109,"show_sort_weight":110,"slug":111},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":113,"slug":114},30,"research-report",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":98,"slug":129},19,"General","general"]