[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125036-en":3,"doc-seo-125036-105":30,"detail-sidebar-cat-0-en-105":91},{"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},125036,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches","Intrusion Detection Systems (IDS) are critical for network security, with performance typically measured by precision, recall, F1 score, and AUC. Experiments on established Wi‑Fi intrusion datasets such as AWID and AWID3 show strong results from individual IDS models, with F1 values around 0.98–1 and AUC near 0.97–0.99. The work focuses on the harder question of feature transferability to unseen datasets and different network environments. Evaluations using MLP and CNN reveal that CNN significantly outperforms MLP for transferability assessment.","Received 20 November 2024, accepted 30 December 2024, date of publication 10 January 2025, date of current version 21 January 2025. Digital Object Identifier 10.1109/ACCESS.2025.3528214  \nTransferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches  \nSAUD YONBAWI1, ADIL AFZAL2, MUHAMMAD YASIR3, MUHAMMAD RIZWAN4, AND NATALIA KRYVINSKA5  \n1Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah 23218, Saudi Arabia  \n2XeroAI, Lahore, Punjab 54890, Pakistan  \n3Department of Computer Science, University of Engineering and Technology Lahore, Lahore, Punjab 39161, Pakistan  \n4College of Science and Engineering, University of Derby, DE22 1GB Derby, U.K.  \n5Department of Information Management and Business Systems, Faculty of Management, Comenius University Bratislava, 820 05 Bratislava, Slovakia Corresponding author: Adil Afzal ([adilafzalansari@gmail.com](adilafzalansari@gmail.com))  \nThis work was supported by the University of Jeddah, Jeddah, Saudi Arabia, under Grant UJ-22-DR-41 .  \nABSTRACT Intrusion Detection System (IDS) plays a pivotal role in safeguarding network security. The efficacy of these systems is rigorously assessed through established metrics including precision, recall, F1 score, and AUC score. When subjected to rigorous testing on well-known datasets like AWID and AWID3, individual IDS models consistently deliver exceptional performances, boasting F1 scores ranging from 0.98 to 1 and AUC scores spanning 0.97 to 0.99 . However, the true challenge surfaces when the objective is to extend the transferability of these high-performing models to entirely novel, unseen datasets. This endeavor unravels a diverse performance landscape, demonstrating that the outstanding performance observed on a particular dataset doesn’t guarantee the transferability of features across dissimilar datasets nestled within different network environments. In order to evaluate the feature transferability, we turn to AWID and AWID3 datasets as the main distinction between AWID (potentially referring to AWID2) and AWID3 lies in their specific focuses and contexts within the field of Wi-Fi intrusion detection. Although both datasets are centered on the general goal of detecting Wi-Fi intrusions, AWID3 has been carefully designed to meet the specific needs of corporate Wi-Fi applications. A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.  \nINDEX TERMS Transferability assessment, performance evaluation, intrusion detection system (IDS), deep learning, wireless security.  \nI. INTRODUCTION  \nThe worldwide cyber security environment has faced rising threats in recent years. Cyber fraudsters took advantage of misaligned networks as firms migrated to remote work settings during the epidemic. Malware assaults climbed 358 percent in 2020 compared to 2019 [1] . In 2020, cyber-attacks were anticipated to be the sixth most serious concern, and are now considered to be the new norm in the private as well as the public arenas. In 2023, this  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Li Zhang.  \nrisky industry will continue to grow, with IoT cyberattacks alone expected to triple by 2025 . Cyber-attack is a hostile attempt by one or more attackers to exploit vulnerabilities in a network in order to obtain unauthorized access, steal sensitive information, or disrupt usual network operations. Globally, cyberattacks surged by 38 percent in 2022 versus 2021 [2] . In an era marked by the pervasive integration of wireless communication technologies into our daily lives, the security of Wi-Fi networks has become paramount. With the exponential growth of connected devices and the continuous evolution of network threats, IDS plays a pivotal role in safeguarding the ","cbCaihvvcFJKn3cY","https://ap.wps.com/l/cbCaihvvcFJKn3cY","pdf",2279834,1,17,"English","en",105,"# Abstract\n# Introduction\n## Cybersecurity context and IDS role\n## Transferability and feature transferability\n# Evaluation setup (datasets and models)","[{\"question\":\"Why is transferability important in Wi‑Fi intrusion detection?\",\"answer\":\"High performance on a dataset does not guarantee that learned features work on unseen datasets with different network environments. Transferability evaluates how well features generalize across domains.\"},{\"question\":\"Which datasets are used to study feature transferability?\",\"answer\":\"The evaluation uses AWID and AWID3. Their focus and context differ, with AWID3 designed for corporate Wi‑Fi application needs.\"},{\"question\":\"How do MLP and CNN compare in the transferability evaluation?\",\"answer\":\"The results show that CNN clearly outshines the MLP model when assessing feature transferability.\"}]","Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches | PDF",1785896294,43,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"transferability-evaluation-in-wi-fi-intrusion-detection-systems-through-machine-learning-and-deep-learning-approaches","",{"@graph":36,"@context":85},[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/transferability-evaluation-in-wi-fi-intrusion-detection-systems-through-machine-learning-and-deep-learning-approaches/125036/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is transferability important in Wi‑Fi intrusion detection?","Question",{"text":75,"@type":76},"High performance on a dataset does not guarantee that learned features work on unseen datasets with different network environments. Transferability evaluates how well features generalize across domains.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets are used to study feature transferability?",{"text":80,"@type":76},"The evaluation uses AWID and AWID3. Their focus and context differ, with AWID3 designed for corporate Wi‑Fi application needs.",{"name":82,"@type":73,"acceptedAnswer":83},"How do MLP and CNN compare in the transferability evaluation?",{"text":84,"@type":76},"The results show that CNN clearly outshines the MLP model when assessing feature transferability.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]