[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124191-en":3,"doc-seo-124191-105":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},124191,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","A Deep And Machine Learning Comparative Approach for Networks Intrusion Detection","Intrusion detection is an essential complement to firewalls, designed to prevent and identify attacks alongside existing defenses. The work focuses on building an attack detection system that works across multiple datasets and maintains comparable performance on different attack scenarios. Several machine learning methods are evaluated, including multilayer perceptron (MLP), convolutional neural networks (CNN), random forest (RF), and boosting approaches such as XGBoost, AdaBoost, and CatBoost. Results on the UNSW-NB15 dataset show that machine learning models can effectively classify normal and malicious network traffic, with MLP achieving strong intrusion detection performance.","A Deep And Machine Learning Comparative Approach for Networks Intrusion Detection  \nAli Raad Sameer Institute of Informatics and Communication University of Delhi [a.ra.ad.s0777@gmail.com](a.ra.ad.s0777@gmail.com)  \nOsamah Mohammed Jasmim Institute of Informatics and Communication, University of Delhi [osamahmohammed@south.du.ac.in](osamahmohammed@south.du.ac.in)  \nMohamed Omar Mohamed Institute of Informatics and Communication, University of Delhi [Mohedkhadar60@gmail.com](Mohedkhadar60@gmail.com)  \nAbstract : Intrusion detection is intergral section of firewallsand other attacks prevention applications that works side by side with the attack pouncing section. The strongest attack prevention application is that of wide range of attack pouncing capability. Recently, data driven models are used for this task which offers the required capability of multiple type of attack detection. In this paper, foucse given to establish an attack detection system that compatible with various datasets and able to draw similar perfromacne in attack flection. Multilayer perception (MLP), Convolutional neural network (CNN). Machine learning algorithms are also deployed such as Random Forest (RF) and Boosting algorithms such as XGBoost, AdaBoost and CatBoost. The MLP algorithm was realized with best intrusion detection performance, it yielded a higher accuracy in both dataset cases. Overall, the classification resultson the UNSW-NB15 dataset suggest that machine learning algorithms can be successfully applied to network intrusion detection tasks, with various algorithms demonstrating high levels of accuracy in distinguishing between normal and malicious network traffic.  \nKeywords: MLP, CNN, Intrusion, Boosting, UNSW-NB15, RF.  \nI. INTRODUCTION  \nThere are a lot more traffic jams, crashes, and smog in cities where more people drive cars. There should be more cutting edge choices. Smart transportation systems (ITS) help places handle daily traffic, people, and big events better. For these features to work well, there needs to be a strong network that lets cars, sensors, and motors share data easily [1] .  \nThis is quick and easy to do with VANETs because they use technologies that let cars talk to each other without any help. There could be a lot of hubs but not many WiFi devices [2] . Things go badly, though, because the cars are going fast. It's tough to get the fastest speed on most home networks. Also, the way we talk to each other now is bad [3] . To do things like move cars from one piece of infrastructure to another, you need a lot of Road Side Units (RSUs) . It costs alot of money and doesn't help with anything. People worry about their safety and privacy when cars talk to each other (V2V) [4] . People can get information and do work at edge nodes that are close to them. You can do more with it and get answers faster [5] . As a stand-alone system, an Intrusion Detection System (IDS) can be set up in a number of different ways. Some people have said that cars could be used as edge nodes. Though there are many ideas for IDS-based VANET systems, there are still some problems [6] . For example, the network has more noise and low detection rates. Also, the False Positive Rates (FPR) are high. IDS that is based on oddities is better than IDS that is based on rules because it can find new threats whose paths haven't been found yet [7] . But safety tips could slow down your network. This research  \nshows a way to find attacks on V2V transmission in ad hoc networks in cars that uses AI. It's meant to help with these problems. There is a fast false positive rate, a better false positive rate, and a low false negative rate in this smart IDS for the Internet of Vehicles. It keeps your information safe. You can pick more than one edge node ifyou don't want one to be too busy. TOPSIS means for Technique for Order Preference by Similarity to Ideal Solution. This is the name of the method. This makes sure that the network works well even when there are a l","cbCailjcac1LMVTj","https://ap.wps.com/l/cbCailjcac1LMVTj","pdf",1713202,1,6,"English","en",105,"# Abstract\n# Keywords\n# I. Introduction","[{\"question\":\"What is the main goal of the proposed intrusion detection approach?\",\"answer\":\"To establish an attack detection system compatible with various datasets and able to achieve similar performance across attack scenarios.\"},{\"question\":\"Which machine learning and deep learning models are compared in the paper?\",\"answer\":\"The paper evaluates MLP and CNN, along with ML algorithms such as Random Forest and boosting methods including XGBoost, AdaBoost, and CatBoost.\"},{\"question\":\"How do the models perform on the UNSW-NB15 dataset?\",\"answer\":\"Classification results on UNSW-NB15 indicate that machine learning algorithms can distinguish normal from malicious network traffic with high accuracy, with MLP showing the best intrusion detection performance.\"}]","A Deep And Machine Learning Comparative Approach for Networks Intrusion Detection | PDF",1785820954,15,{"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},"a-deep-and-machine-learning-comparative-approach-for-networks-intrusion-detection","",{"@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/a-deep-and-machine-learning-comparative-approach-for-networks-intrusion-detection/124191/",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},"What is the main goal of the proposed intrusion detection approach?","Question",{"text":75,"@type":76},"To establish an attack detection system compatible with various datasets and able to achieve similar performance across attack scenarios.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and deep learning models are compared in the paper?",{"text":80,"@type":76},"The paper evaluates MLP and CNN, along with ML algorithms such as Random Forest and boosting methods including XGBoost, AdaBoost, and CatBoost.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the models perform on the UNSW-NB15 dataset?",{"text":84,"@type":76},"Classification results on UNSW-NB15 indicate that machine learning algorithms can distinguish normal from malicious network traffic with high accuracy, with MLP showing the best intrusion detection performance.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]