[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122862-en":3,"doc-seo-122862-105":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},122862,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","Intrusion Detection System Using Machine Learning by RNN Method","As computer networks expand, network intrusions become more frequent, advanced, and unstable, making reliable detection difficult with existing security tools. Network Intrusion Detection Systems (NIDS) monitor traffic, identify unauthorized usage, and reveal evidence of malicious attacks. Research indicates machine learning, especially deep learning, improves detection of network attack variants over traditional rule-based approaches. The proposed model applies LSTM-RNN, classification methods, and a hybrid sparse autoencoder with DNN on NSL-KDD, evaluated with metrics for accuracy, detection rate, and false alarms.","Intrusion Detection System Using Machine Learning by RNN Method  \nMr.K. Azarudeen 1 * B.E., M.E. ,Ghulam Dasthageer2G, Rakesh2 A,Sathaiah Balaji2 Sand Vishal Raj2 R  \n1Assistant Professor, Department of Computer Science &Engineering,-Velammal College of Engineering &Technology, (Autonomous) Madurai,India  \n[Email: ](Email: kad@vcet.ac.in)[kad@vcet.ac.in](Email: kad@vcet.ac.in)  \n2UG Student Department of Computer Science &Engineering,-Velammal College of Engineering &Technology,(Autonomous) Madurai,India  \nEmail: codeurrakesh17@gmail.comsathaiahbalaji333@gmail.com [prvishal2000@gmail.com](prvishal2000@gmail.com)  \nAbstract.As computer networks continue to grow, network intrusions become more frequent, advanced, and volatile, making it challenging to detect them. This has led to an increase in illegal intrusions that current security tools cannot handle. NIDS is currently available and most reliable ways to monitor network traffic, identify unauthorized usage, and detect malicious attacks. NIDS can provide better visibility of network activity and detect any evidence of attacks and malicious traffic. Recent research has shown that machine learning-based NIDS, particularly with deep learning, is more effective in detecting variants of network attacks compared to traditional rule-based solutions. This proposed model that introduces novel deep learning methodologies for network intrusion detection. The model consists of three approaches: LSTM-RNN, various classifying methodology, and a hybrid Sparse autoencoder with DNN. The LSTM-RNN evaluated NSL-KDD dataset and classified as multi-attack classification. The model outperformed with Adamax optimizer in terms of  \naccuracy, detection rate, and low false alarm rate.  \n1 Introduction  \nIntrusion Detection Systems (IDSs) are effective security mechanisms that identify attacks by analyzing network traffic [1][2] . ML techniques have been used to design IDSs and improve their classification performance [3] . In those classification techniques Accuracy is calculated in order to get the best one. However, advancements in networking technologies have led to new types of attacks and research gaps in ML-based IDSs, including handling high-dimensional datasets and managing uneven distribution of data samples [4] . In this thesis, the author proposes approaches for enhancing attack classification capability by considering good generalization ability, enhanced feature engineering capability, improved  \n*[Corresponding Author :](Corresponding Author :ghualamdasthageer25@gmail.com)[ghualamdasthageer25@gmail.com](Corresponding Author :ghualamdasthageer25@gmail.com)  \n© The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nlearning capability, and handling class imbalance. Before classification pre-processing is carried out, different values are taken and It is processed and categorized [5] . The author presents empirical analysis of six ML classifiers and a novel sampling technique.[6] . The author also proposes two feature selection techniques and custom layers in DNN architecture for fine representation of data [7] . To handle high-dimensional data, the author proposes federated learning-inspired DNN architecture, ensemble learning approach, and a novel approach using adaptive learning rate. To address class imbalance, the author proposes a novel ensemble learning-based DNN. The proposed approaches are evaluated using various intrusion detection datasets, and statistically significant results are achieved for different evaluation metrics [8] . Choosing the type of dataset has essential play in classification through the ML. Wrong choice of Sample load data can damage the quality of Analysis. The Metrics is the scale or component which condition the value of algorithms in Machine Learning [9] . Coefficient of","cbCaioz8EHpOdmWi","https://ap.wps.com/l/cbCaioz8EHpOdmWi","pdf",2583148,1,10,"English","en",105,"# Abstract\n# 1 Introduction\n# 2 Related works","[{\"question\":\"Why are traditional security tools insufficient for intrusion detection?\",\"answer\":\"Network intrusions are more frequent, advanced, and volatile, and existing security tools cannot handle these evolving attack patterns effectively.\"},{\"question\":\"What model approaches are proposed for network intrusion detection?\",\"answer\":\"The proposed work includes LSTM-RNN, several classification methodologies, and a hybrid sparse autoencoder with DNN.\"},{\"question\":\"How is the proposed LSTM-RNN model evaluated and what datasets are used?\",\"answer\":\"The model is evaluated on the NSL-KDD dataset and uses multi-attack classification, reporting performance via metrics such as accuracy, detection rate, and low false alarm rate.\"}]","Intrusion Detection System Using Machine Learning by RNN Method | PDF",1785813408,25,{"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},"intrusion-detection-system-using-machine-learning-by-rnn-method","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/intrusion-detection-system-using-machine-learning-by-rnn-method/122862/",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","2026-08-04",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 traditional security tools insufficient for intrusion detection?","Question",{"text":75,"@type":76},"Network intrusions are more frequent, advanced, and volatile, and existing security tools cannot handle these evolving attack patterns effectively.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What model approaches are proposed for network intrusion detection?",{"text":80,"@type":76},"The proposed work includes LSTM-RNN, several classification methodologies, and a hybrid sparse autoencoder with DNN.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed LSTM-RNN model evaluated and what datasets are used?",{"text":84,"@type":76},"The model is evaluated on the NSL-KDD dataset and uses multi-attack classification, reporting performance via metrics such as accuracy, detection rate, and low false alarm rate.","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},[93,97,101,105,110,113,118,123,128,131,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]