[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119524-en":3,"doc-seo-119524-105":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},119524,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","NETWORK ANOMALY DETECTION USING ADVANCED MACHINE LEARNING - Master of Science Thesis","Distributed denial of service (DDoS) attacks pose a major and evolving network security challenge due to their broad attacker base and complex, time-varying behavior, making many traditional detection techniques less effective. The work uses recurrent neural network approaches, specifically RNNs and LSTM networks, to capture timing patterns in network traffic. PCA is applied to reduce features while preserving key information, and SMOTE addresses imbalanced anomaly data by generating synthetic samples of rare threats. Experiments on a benchmark dataset evaluate detection performance, false positive rates, and comparative results against standard machine learning methods.","Intisam Ahmed  \nNETWORK ANOMALY DETECTION USING ADVANCED MACHINE LEARNING  \nMaster of Science Thesis  \nFaculty of Information Technology and Communication Science Examiners: Marko Helenius  \nAri Visa April 2025  \nABSTRACT  \nIntisam Ahmed: Network Anomaly Detection using Advanced Machine Learning Master of Science Thesis  \nTampere University  \nMaster’s Programme in Information Technology April 2025  \nA distributed denial of service (DDoS) attack is one of the biggest challenges in network security today, as it has a large spread of attackers, a complicated attack structure and is always changing over time, so standard detection methods don’t work anymore. Nowadays, using techniques such as RNNs and LSTM networks with machine learning helps have good results against such threats. They perform superbly in finding timing patterns in the network traffic which is vital for finding both typical and unusual kinds of traffic. The authors combine PCA and SMOTE with RNN and LSTM to create an earlydetection financial fraud system through this research. PCA allows you to narrow down the number of features to process without losing key information which is beneficial foryour system. The common issue of imbalanced data in anomaly detection is solved by SMOTE which creates new samples of rare threats.  \nAfter proposing the method, it is tested on a benchmark dataset that anyone can use, showing top detection success, low false positives and better results than standard machine learning approaches. They reveal that box structures supported by better preprocessing may make the network more efficient at defending against complex DDoS attacks.  \nKeywords: DDoS Detection, RNN, LSTM, PCA, SMOTE, Network Security  \nThe originality of this thesis has been verified using the Turnitin Originality Check service.  \nPREFACE  \nThis document template conforms to the Guide to Writing a Thesis in Technical Fields at Tampere University (2019) .  \nAcknowledgements to those who contributed to the thesis are generally presented in the preface. It is not appropriate to criticize anyone in the preface, even though the preface will not affect your grade. The preface must fit on one page. Add the date, after which you have not made any revisions to the text, at the end of the preface.  \nTampere, 16 January 2019  \nAuthor  \nCONTENTS  \n1. INTRODUCTION ................................................................................................... 7  \n1.1 Motivation .............................................................................................. 7  \n1.2 Objectives.............................................................................................. 8  \n2. BACKGROUND................................................................................................... 10  \n2.1 DDoS Attacks ...................................................................................... 10  \n2.2 Machine Learning in DDoS Detection .................................................. 10  \n2.3 Recurrent Neural Networks (RNN) ....................................................... 11  \n2.4 Long Short-Term Memory (LSTM) ....................................................... 11  \n2.5 Evaluation Metrics ............................................................................... 12  \n3. FOUNDATIONAL AND EMERGING APPROACHES IN NETWORK TRAFFIC ANOMALY DETECTION ............................................................................................ 13  \n3.1 Anomaly Detection in Network Security ............................................... 13  \n3.2 Machine Learning for Network Security ............................................... 14  \n3.3 Deep Learning for Anomaly Detection ................................................. 14  \n3.4 Use of RNN and LSTM in DDoS Detection .......................................... 15  \n3.5 DDoS Detection Techniques................................................................ 16  \n3.6 Network Traffic Feature Engineering ........","cbCailu12l4eUvF2","https://ap.wps.com/l/cbCailu12l4eUvF2","pdf",1569549,1,44,"English","en",105,"# 1. INTRODUCTION\n## 1.1 Motivation\n## 1.2 Objectives\n# 2. BACKGROUND\n## 2.1 DDoS Attacks\n## 2.2 Machine Learning in DDoS Detection\n# 3. FOUNDATIONAL AND EMERGING APPROACHES IN NETWORK TRAFFIC ANOMALY DETECTION\n## 3.1 Anomaly Detection in Network Security\n## 3.2 Machine Learning for Network Security\n# 4. METHODOLOGY\n## 4.1 Dataset Description\n## 4.2 Data Preprocessing\n# 5. RESULTS AND DISCUSSION\n## 5.1 Performance Metrics\n## 5.2 Impact of PCA and SMOTE\n# 6. CONCLUSION\n# 7. REFERENCES","[{\"question\":\"Why are standard DDoS detection methods less effective today?\",\"answer\":\"DDoS attacks are highly distributed, have complex structures, and change over time, so conventional detection approaches can no longer keep up effectively.\"},{\"question\":\"How do PCA and SMOTE contribute to the proposed anomaly detection approach?\",\"answer\":\"PCA reduces the number of features processed while retaining key information, and SMOTE mitigates imbalanced anomaly data by generating new samples for rare threats.\"},{\"question\":\"What evaluation approach is used to validate the method?\",\"answer\":\"The proposed system is tested on a benchmark dataset, reporting strong detection success, low false positives, and improved results compared with standard machine learning approaches.\"}]","NETWORK ANOMALY DETECTION USING ADVANCED MACHINE LEARNING - Master of Science Thesis | PDF",1785724770,111,{"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},"network-anomaly-detection-using-advanced-machine-learning-master-of-science-thesis","",{"@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/network-anomaly-detection-using-advanced-machine-learning-master-of-science-thesis/119524/",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","2026-08-03",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 are standard DDoS detection methods less effective today?","Question",{"text":75,"@type":76},"DDoS attacks are highly distributed, have complex structures, and change over time, so conventional detection approaches can no longer keep up effectively.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do PCA and SMOTE contribute to the proposed anomaly detection approach?",{"text":80,"@type":76},"PCA reduces the number of features processed while retaining key information, and SMOTE mitigates imbalanced anomaly data by generating new samples for rare threats.",{"name":82,"@type":73,"acceptedAnswer":83},"What evaluation approach is used to validate the method?",{"text":84,"@type":76},"The proposed system is tested on a benchmark dataset, reporting strong detection success, low false positives, and improved results compared with standard machine learning approaches.","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"]