[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127363-en":3,"doc-seo-127363-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127363,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Enhancing Intrusion Detection Systems in Cloud Computing Environments - A Hybrid Machine Learning Approach","Intrusion Detection Systems (IDS) are critical to protecting cloud environments increasingly targeted by sophisticated cyber-attacks. The paper proposes a hybrid machine learning approach that combines Random Forest feature selection, LSTM networks for temporal pattern recognition, and Transformer networks for contextual learning. Experiments on CICIDS2017 and CSE-CIC-IDS2018 show weighted F1-scores reaching 97% and 99%, with accuracy and F1 improvements over LSTM-only baselines. Results highlight strong performance on common attacks while noting remaining difficulty for rare threats such as SQL Injection.","Enhancing Intrusion Detection Systems in Cloud Computing Environments: A Hybrid Machine Learning Approach  \nSuliman Alharbi1, Majzoob K. Omer2  \n1,2Department of Computer Science, Al-Baha University, Saudi Arabia  \nArticle history:  \nReceived May 22, 2025 Revised Jul 13, 2025 Accepted Sep 13, 2025  \nKeywords:  \nCloud Computing  \nIntrusion Detection Systems (IDS)  \nHybrid Machine Learning LSTM Networks  \nTransformer Networks  \nCorresponding Author:  \nMajzoob K. Omer,  \nDepartment of Computer Science, Al-Baha University, [Email: mkomer@bu.edu.sa](Email: mkomer@bu.edu.sa)  \nIntrusion Detection Systems (IDS) are essential for maintaining the security of cloud computing environments, which are increasingly targeted by sophisticated cyber-attacks. This paper presents a novel hybrid approach for intrusion detection in cloud environments, combining Random Forest for feature selection, Long Short-Term Memory (LSTM) networks for temporal pattern recognition, and Transformer networks for contextual learning. Evaluated on CICIDS2017 and CSE-CIC-IDS2018 datasets, the proposed approach achieved weighted F1-scores of 97% and 99% respectively, significantly outperforming baseline models. The hybrid model improved accuracy from 95.1% to 98.0% and F1-score from 94.2% to 97.0% compared to LSTM-only approaches. While excelling at detecting common attack patterns such as Distributed Denial of Services (DDoS), challenges remain in identifying rare threats including SQL Injection. This research contributes to cloud security advancement by demonstrating the effectiveness of hybrid machine learning architectures in addressing the unique challenges of intrusion detection in distributed cloud infrastructures.  \nCopyright © 2025 Institute of Advanced Engineering and Science.  \nAll rights reserved.  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCloud computing has transformed the information technology landscape, offering unprecedented flexibility, scalability, and cost-efficiency [1] . However, this transition has introduced complex security challenges that traditional protection mechanisms struggle to address effectively [2],[3] . As organizations migrate critical data and applications to cloud environments, these systems become high-value targets for sophisticated cyber-attacks, necessitating robust security measures that can adapt to the dynamic nature of cloud computing [4] .  \nIDSs represent a critical defense in cloud security architecture. Traditional IDS approaches, including signature-based and rule-based systems, have demonstrated significant limitations when deployed in cloud environments [5],[6] . These conventional systems typically rely on predefined patterns of known attacks, rendering them ineffective against novel threats. Moreover, the distributed, multi-tenant nature of cloud computing introduces additional complexities, including virtualization layers, resource sharing, and dynamic scaling, which further challenge traditional security mechanisms [7] .  \nMachine learning (ML) has emerged as a promising solution, offering the potential to enhance IDS capabilities through automated pattern recognition, anomaly detection, and adaptive learning. ML techniques enable systems to analyze vast amounts of network traffic and system behavior data, identifying subtle patterns that may indicate malicious activity. Unlike traditional approaches, ML-based systems can continuously learn and adapt to new attack patterns without requiring manual rule updates [8],[9],[10] .  \nDespite these advances, existing ML-based IDS solutions face several challenges in cloud environments, including real-time detection requirements, scalability needs, and maintaining detection accuracy across diverse attack vectors. Additionally, the multi-tenant nature of cloud computing introduces unique privacy and isolation concerns that must be addressed in effective security solutions [11] .  \nThis research addresses the challenges by proposing a novel hybrid approach for intrusi","cbCaick0Kzh5OQtD","https://ap.wps.com/l/cbCaick0Kzh5OQtD","pdf",716469,1,11,"English","en",105,"# Introduction\n## Challenges in Cloud Security\n## Limitations of Traditional IDS\n## Role of Machine Learning in IDS\n# Related Work\n## Evolution of Intrusion Detection Systems\n# Research Methodology\n# Experimental Setup and Results\n# Findings and Discussion\n# Conclusion","[{\"question\":\"What hybrid techniques does the proposed intrusion detection model use?\",\"answer\":\"It integrates Random Forest for feature selection, LSTM networks for temporal dependency capture, and Transformer networks for contextual learning on network traffic data.\"},{\"question\":\"Which datasets were used to evaluate the approach?\",\"answer\":\"The model was evaluated on the CICIDS2017 and CSE-CIC-IDS2018 datasets.\"},{\"question\":\"How does the hybrid model compare with LSTM-only approaches?\",\"answer\":\"The hybrid approach improves accuracy from 95.1% to 98.0% and F1-score from 94.2% to 97.0%, outperforming LSTM-only baselines.\"}]","Enhancing Intrusion Detection Systems in Cloud Computing Environments - A Hybrid Machine Learning Approach | PDF",1785938513,28,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"enhancing-intrusion-detection-systems-in-cloud-computing-environments-a-hybrid-machine-learning-approach","",{"@graph":36,"@context":86},[37,54,69],{"@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/enhancing-intrusion-detection-systems-in-cloud-computing-environments-a-hybrid-machine-learning-approach/127363/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What hybrid techniques does the proposed intrusion detection model use?","Question",{"text":76,"@type":77},"It integrates Random Forest for feature selection, LSTM networks for temporal dependency capture, and Transformer networks for contextual learning on network traffic data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets were used to evaluate the approach?",{"text":81,"@type":77},"The model was evaluated on the CICIDS2017 and CSE-CIC-IDS2018 datasets.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the hybrid model compare with LSTM-only approaches?",{"text":85,"@type":77},"The hybrid approach improves accuracy from 95.1% to 98.0% and F1-score from 94.2% to 97.0%, outperforming LSTM-only baselines.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]