[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117909-en":3,"doc-seo-117909-105":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},117909,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Intrusion Detection: Machine Learning Techniques for Software Defined Networks","Software defined networking (SDN) has become a flexible, programmable networking paradigm for modern network management, yet it also introduces exposure to compromise and security threats. This thesis describes and evaluates a network intrusion detection system (NIDS) deployed within an SDN environment using machine learning to distinguish normal from malicious traffic. Training and testing rely on the UNR-IDD and NSL-KDD datasets, supported by feature selection during experimentation to identify effective predictive features and assess accuracy and effectiveness of the intrusion detection approach.","Intrusion Detection: Machine Learning Techniques for Software Defined Networks  \nJacob Rodriguez  \nA Thesis Submitted to the Graduate Faculty of  \nGRAND VALLEY STATE UNIVERSITY  \nIn  \nPartial Fulfillment of the Requirements For the Degree of  \nMaster of Science in Cybersecurity  \nSchool of Computing  \nThesis Approval Form  \nThe signatories of the committee below indicate that they have read and approved the thesis of Jacob Rodriguez in partial fulfillment of the requirements for the degree of Master of Science in Cybersecurity.  \n8/4/2023  \n______________________________________________________  \nAndrew Kalafut, Thesis committee chair Date  \n   08/04/2023_  \nXinli Wang, Committee member Date  \n  08/04/2023_  \nByron Devries, Committee member Date  \nAccepted and approved on behalf of the Padnos College of Engineering and Computing  \nDean of the College August 7, 2023  \nDate  \nAccepted and approved on behalf of the  \nGraduate Faculty  \nDean of The Graduate School  \n8/24/2023  \nDate  \nTable of Contents  \nTitle Page …………………………………………………………………………………………1  \nApproval Page …………………………………………………………………………………….2  \nTable of Contents ………………………………………………………………………………....3  \nAbstract ...………………………………………………………………………………………....4  \nChapter 1: Introduction .....………………………………………………………………………..5  \nChapter 2: Background Information and Related Work .…………………………………………9  \nSDN Overview …….…………………………………………………………………….10  \nSDN Architecture ………………………………………………………………………..12  \nChapter 3: Methods & Procedures …….………………………………………………………...15  \nFeature Selection ………………………………………………………………………...17  \nClassification Algorithms ……………………………………………………………….18  \nTesting the Model ……………………………………………………………………….20  \nChapter 4: Results …………………………………………………………………….…………24  \nChapter 5: Discussion …………………………………………………………………………...28  \nChapter 6: Conclusion …………………………………………………………………………...30  \nReferences ……………………………………………………………………………………….32  \nAbstract  \nIn recent years, software defined networking (SDN) has gained popularity as a novel approach towards network management and architecture. Compared to traditional network architectures, this software-based approach offers greater flexibility, programmability, and automation. However, despite the advantages ofthis system, there still remains the possibility that it could be compromised. As we continue to explore new approaches to network management, we must also develop new ways of protecting those systems from threats. Throughout this paper, I will describe and test a network intrusion detection system (NIDS), and how it can be implemented within a software defined network. This system will utilize machine learning techniques to discern between normal and malicious network traffic. The datasets that will be used for training and testing these machine learning methods include the UNR-IDD dataset, and the NSL-KDD dataset. The UNR-IDD dataset was created by researchers at the University of Nevada, Reno, and is intended to provide a wide range of samples and scenarios for machine learning-based intrusion detection systems. The NSL-KDD dataset is a newer version of the KDD '99 dataset, and is used as an effective benchmark for helping researchers compare various intrusion detection methods. Feature selection techniques will be performed during the testing phase to ensure the best features are used when performing analysis. In doing so, we’ll be able to extract the best results possible from the experiments to determine the accuracy and effectiveness of the IDS.  \nChapter 1: Introduction  \nComputer security is an essential part of ensuring the smooth operation of today’s computer systems. It is also essential for protecting sensitive information, maintaining privacy, and preventing cyberattacks. With various forms of cybercrime on the rise, failure to implement proper security measures could lead to significant financial losses, organizational damage, or even legal consequences. Due to the complexity and variety of modern computer systems, there exists a nu","cbCaimeyj7sNvdL0","https://ap.wps.com/l/cbCaimeyj7sNvdL0","pdf",449198,1,33,"English","en",105,"# Chapter 1: Introduction\n# Chapter 2: Background Information and Related Work\n## SDN Overview\n## SDN Architecture\n# Chapter 3: Methods & Procedures\n## Feature Selection\n## Classification Algorithms\n## Testing the Model\n# Chapter 4: Results\n# Chapter 5: Discussion\n# Chapter 6: Conclusion\n# References","[{\"question\":\"What problem does this thesis address in software defined networks?\",\"answer\":\"It addresses the risk that SDN systems can be compromised despite their flexibility and programmability, and it focuses on detecting network attacks in a timely manner.\"},{\"question\":\"How is intrusion detection implemented in this work?\",\"answer\":\"A network intrusion detection system (NIDS) is implemented within an SDN, using machine learning to differentiate normal traffic from malicious traffic.\"},{\"question\":\"Which datasets are used for training and testing the machine learning models?\",\"answer\":\"The thesis uses the UNR-IDD dataset and the NSL-KDD dataset, with feature selection applied during testing to choose effective features for analysis.\"}]","Intrusion Detection: Machine Learning Techniques for Software Defined Networks | 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