[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125797-en":3,"doc-seo-125797-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},125797,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",6,"Technology","Reconnaissance Attack Detection via Boosting Machine Learning Classifiers","Network security challenges increase with rapid growth in Internet technologies, making timely and accurate threat identification essential. Intrusion Detection Systems (IDS) are widely used to mitigate attacks by distinguishing normal from abnormal traffic using machine learning classifiers. This study evaluates reconnaissance attack detection with AdaBoost, Gradient Boosting, CatBoost, and XGBoost, selecting the most effective model using accuracy, precision, and F-measure. Experiments on the UNSW-NB15 dataset show CatBoost achieves the strongest performance among the compared methods.","See discussions, stats, and author profiles for this publication at: [https://www. researchgate. net/publication/365438346](https://www. researchgate. net/publication/365438346)  \nReconnaissance Attack Detection via Boosting Machine Learning Classifiers  \nConference Paper · October 2023 DOI: 10. 1063/5 .0174730  \nCITATIONS 0  \nREADS 160  \n6 authors, including:  \nOmar Almomani  \nThe World Islamic Science and Education University 82 PUBLICATIONS 1,383 CITATIONS  \nDrmohammed Almaayah  \nKing Faisal University  \n136 PUBLICATIONS 5,108 CITATIONS  \nMohammed Madi  \nHasan Kalyoncu University 12 PUBLICATIONS 27 CITATIONS  \nAdeeb Saaidah  \nThe World Islamic Science and Education University 28 PUBLICATIONS 390 CITATIONS  \nAll content following this page was uploaded by Omar Almomani on 24 October 2023.  \nThe user has requested enhancement of the downloaded file.  \nRESEARCH ARTICLE | OCTOBER 20 2023  \nReconnaissance attack detection via boosting machine learning classifiers 􀀅  \nOmar Almomani 􀀧 ; Mohammed Amin Almaiah; Mohammed MADI; Adeeb Alsaaidah; Malek A. Almomani;  \nSami Smadi  \nAIP Conf. Proc. 2979, 060002 (2023)  \n[https://doi.org/10.1063/5.0174730](https://doi.org/10.1063/5.0174730)  \n􀀭  \nView Online  \n􀀱  \nExport Citation  \nCrossMark  \nArticles You May Be Interested In  \nComparison between machine learning and deep learning for intrusion detection  \nAIP Conference Proceedings (March 2023)  \nA comparative study of machine learning based anomaly detection for IoT data using SPARK AIP Conference Proceedings (November 2022)  \nResilience evaluation for UAV swarm performing joint reconnaissance mission  \nChaos (May 2019)  \n23 October 2023 20:23:49  \nReconnaissance Attack Detection via Boosting Machine  \nLearning Classifiers  \nOmar Almomani 1,a), Mohammed Amin Almaiah2,b), Mohammed MADI3,c),  \nAdeeb Alsaaidah 1,d), Malek A. Almomani4,e) and Sami Smadi 1,f)  \n1Department of Information System and Networks, The World Islamic Sciences and Education University, Amman, Jordan  \n2College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia 3  \n.Department of Computer Engineering, Hasan Kalyoncu University,Gaziantep, Turkey  \n.4Department of Software Engineering, The World Islamic Sciences and Education University University, Amman, Jordan  \na) [Corresponding author:Omar.almomani@wise.edu.jo](Corresponding author:Omar.almomani@wise.edu.jo)  \nb) [malmaiah@kfu.edu.sa](malmaiah@kfu.edu.sa)  \nc) [mohammed.madi@hku.edu.tr](mohammed.madi@hku.edu.tr)  \nd) [Adeeb.saaidah@wise.edu.jo](Adeeb.saaidah@wise.edu.jo)  \ne) [malek.almomani@wise.edu.jo](malek.almomani@wise.edu.jo)  \nf) [sami.smadi@wise.edu.jo](sami.smadi@wise.edu.jo)  \nAbstract. With the advancement of Internet technologies, network security concerns are growing exponentially. One of the most difficult issues of network security is keeping it safe. To detect and identify any malicious behavior the network, many security techniques were deployed. Intrusion Detection Systems (IDS) is one of the most frequent strategies for mitigating the effects of these attacks. Reconnaissance is a common attack in computer networks in which the attacker gathers as much information as possible about the target before conducting an attack. Machine Learning (ML) classifiers are commonly used to distinguish between normal and abnormal network traffic. In this paper, econnaissance attacks detection is an exam with the following ML classifiers: Adaptive Boosting (AdaBoost), Gradient Boosting, cat Boosting, and eXtreme Gradient Boosting (XGBoost) to determine the most effective classifier in identifying Reconnaissance attacks. Evaluation metrics used are accuracy, precision, F-measure True Positive. The experiment on the UNSW-NB15 dataset shows that the cat Boosting classifier is superior to the XGBoost, AdaBoost and Gradiant Boosting.  \nINTRODUCTION  \nThe significance of computer networks has risen as central information systems in modern life during the last few years [1][2","cbCaimEQ4StMqAVo","https://ap.wps.com/l/cbCaimEQ4StMqAVo","pdf",980907,1,9,"English","en",105,"# Abstract\n# Introduction\n## Reconnaissance attacks in network security\n# Attack detection using boosting ML classifiers\n## AdaBoost, Gradient Boosting, CatBoost, and XGBoost\n## Evaluation metrics and UNSW-NB15 results","[{\"question\":\"What is the main goal of this paper?\",\"answer\":\"The paper aims to detect reconnaissance attacks in computer networks and determine which boosting machine learning classifier performs best for identifying such behavior.\"},{\"question\":\"Which machine learning models are compared for reconnaissance attack detection?\",\"answer\":\"AdaBoost, Gradient Boosting, CatBoost, and XGBoost are evaluated to find the most effective classifier.\"},{\"question\":\"What dataset and metrics are used to evaluate performance?\",\"answer\":\"Results are tested on the UNSW-NB15 dataset using accuracy, precision, and F-measure (including true positive-based assessment).\"}]","Reconnaissance Attack Detection via Boosting Machine Learning Classifiers | 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is the main goal of this paper?","Question",{"text":76,"@type":77},"The paper aims to detect reconnaissance attacks in computer networks and determine which boosting machine learning classifier performs best for identifying such behavior.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning models are compared for reconnaissance attack detection?",{"text":81,"@type":77},"AdaBoost, Gradient Boosting, CatBoost, and XGBoost are evaluated to find the most effective classifier.",{"name":83,"@type":74,"acceptedAnswer":84},"What dataset and metrics are used to evaluate performance?",{"text":85,"@type":77},"Results are tested on the UNSW-NB15 dataset using accuracy, precision, and F-measure (including true positive-based 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