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A combined IDS is proposed using K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) to classify network traffic as Allow, Deny, or Drop. Using the Internet Firewall dataset, feature scaling and feature selection improve model performance; MLP achieves 99.83% validation accuracy and KNN 95.29%. A real-time, user-friendly interface provides probability-based predictions, integrating machine learning with deep learning to enhance secure networks.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/attack-detection-and-prevention-using-machine-learning-and-deep-learning-techniques/291234/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/attack-detection-and-prevention-using-machine-learning-and-deep-learning-techniques/291234.png","ImageObject",300,407,{"name":92,"@type":93},"Ophelia","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What threats does the proposed IDS target?","Question",{"text":113,"@type":114},"It targets emerging threats such as sniffing and suspicious port scanning, aiming to protect sensitive data and service continuity.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which models are used to classify network traffic?",{"text":118,"@type":114},"The study uses K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) to classify traffic into Allow, Deny, or Drop.",{"name":120,"@type":111,"acceptedAnswer":121},"How does the system support real-time predictions?",{"text":122,"@type":114},"It includes a green, clean, user-friendly interface that enables real-time predictions with probability scores, supported by the trained models.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},291234,1789643823,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},7971461741311,"https://ap-avatar.wpscdn.com/avatar/74000253aff267980c6?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779345379180704826","Attack Detection and Prevention Using Machine Learning and Deep Learning  \nTechniques  \nMSc Research Project Master of Science in Cyber Security  \nAchu Abraham George  \nStudent ID: X23127872  \nSchool of Computing  \nNational College of Ireland  \nSupervisor: Diego Lugones  \nNational College of Ireland  \nMSc Project Submission Sheet  \nSchool of Computing  \nStudent   .Achu Abraham George    \nName:  \nStudent ID:   X23127872    \nProgramme: …MS in Cyber Security   Year: …2024-2025….  \nModule:   Research Project   .    \nSupervisor:   Diego Lugones   .   Submission  \nDue Date:   29/01/2025    \nProject Title:   Attack Detection and Prevention using Machine Learning and Deep Learning Techniques   .    \nWord Count:   6993   Page Count   24   .   . .  \nI hereby certify that the information contained in this (my submission) is information pertaining to research I conducted for this project. All information other than my own contribution will be fully referenced and listed in the relevant bibliography section at the rear of the project.  \nALL internet material must be referenced in the bibliography section. Students are required to use the Referencing Standard specified in the report template. To use other author's written or electronic work is illegal (plagiarism) and may result in disciplinary action.  \nSignature:   Achu Abraham George    \nDate:   28/01/2025    \nPLEASE READ THE FOLLOWING INSTRUCTIONS AND CHECKLIST  \n| Attach a completed copy of this sheet to each project (including multiple copies) | □ |\n| --- | --- |\n| Attach a Moodle submission receipt of the online project submission, to each project (including multiple copies) . | □ |\n| You must ensure that you retain a HARD COPY of the project, both for your own reference and in case a project is lost or mislaid. It is not sufficient to keep a copy on computer. | □ |\n\nAssignments that are submitted to the Programme Coordinator Office must be placed into the assignment box located outside the office.  \n\n| Office Use Only |  |\n| --- | --- |\n| Signature: |  |\n| Date: |  |\n| Penalty Applied (if applicable): |  |\n\nAttack Detection and Prevention Using Machine Learning and Deep Learning Techniques  \nAchu Abraham George  \nX23127872  \nAbstract  \nThis research seeks to offer a solution to the growing development of sound Intrusion Detection Systems( IDSs) to suit modern emerging threats such as sniffing and port scanning. A combined IDS is suggested using K-Nearest Neighbors (KNN) as well as Multilayer Perceptron (MLP) schemes in order to recognize network traffic as Allow, Deny, or Drop. When using the Internet Firewall dataset, feature scaling and feature selection transformations were performed for the sake of improving the models. With regards to theMLP model, the validation accuracy was 99.83%, and fortheKNN was only 95.29% implying the benefits of both models. Furthermore, this study focuses on the network probing tools and machine learning as key approaches to sniffing detection. Green, clean, and user-friendly interface capabilities allow users to make predictions in real-time complete with probability scores. This work demonstrates how a machine learning approach can be integrated with deep learning to improve the level of intrusion detection for networks and offers a practical way towards secure networks in constantly evolving threat landscapes. The contributions fill the gap between the theoretical developments and systems to improve the effectiveness of adaptive and intelligent cybersecurity systems.  \n1 Introduction  \nDigital systems are advancing rapidly; and, increasingly, the networked infrastructure that undergirds them is exposed to the risk of cyber attack. With increasing sophistication and variety of these attacks, especially in finance and healthcare sectors, IDS development is needed to help protect sensitive data and critical service continuity. Existing approaches of cybersecurity are not able to detect newer types of attack, and thus advanced machine learning (ML) and","cbCaij1garsxa7JP","https://ap.wps.com/l/cbCaij1garsxa7JP","pdf",981418,27,"English","# Abstract\n# Introduction\n## Real-Time Client-Server Cybersecurity Framework\n## Research Question and Objectives","[{\"question\":\"What threats does the proposed IDS target?\",\"answer\":\"It targets emerging threats such as sniffing and suspicious port scanning, aiming to protect sensitive data and service continuity.\"},{\"question\":\"Which models are used to classify network traffic?\",\"answer\":\"The study uses K-Nearest Neighbors (KNN) and Multilayer Perceptron (MLP) to classify traffic into Allow, Deny, or Drop.\"},{\"question\":\"How does the system support real-time predictions?\",\"answer\":\"It includes a green, clean, user-friendly interface that enables real-time predictions with probability scores, supported by the trained models.\"}]","Attack Detection and Prevention Using Machine Learning and Deep Learning Techniques | PDF",68]