[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118220-en":3,"doc-seo-118220-105":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},118220,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Web Attack Intrusion Detection System Using Machine Learning Techniques - Research paper","Web attacks commonly target network-accessible web applications and exploit vulnerabilities such as SQL injection, XSS, and brute-force attempts. Detecting these threats effectively depends on traffic classification in intrusion detection systems (IDS). This study evaluates machine-learning approaches using the CIC-IDS2017 dataset, which includes 80 attributes of recent assaults. Three algorithms—random forests, k-nearest neighbors, and naive Bayes—are compared by accuracy, precision, and recall.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 20 No. 3 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i03.45249](https://doi.org/10.3991/ijoe.v20i03.45249)  \nPAPER  \nWeb Attack Intrusion Detection System Using Machine Learning Techniques  \nMahmoud Khalid Baklizi1, Issa Atoum2, Mohammad Alkhazaleh1, Hasan Kanaker3, Nibras Abdullah4,5(􀀍), OlaA. Al-Wesabi5, Ahmed Ali Otoom6  \n1Department of Computer Sciences, Faculty of Information Technology, Isra University, Amman, Jordan  \n2Software Engineering Department, Faculty of Information Technology, The World Islamic Sciences and Education, Amman, Jordan  \n3Department of Cybersecurity, Faculty of Information Technology, Isra University, Amman, Jordan  \n4School of Computer Sciences, Universiti Sains Malaysia (USM), Gelugor, Malaysia  \n5Faculty of Computer Science and Engineering, Hodeidah University, Hodeidah, Yemen  \n6Cybersecurity and Cloud Computing Department, Faculty of Information Technology, Applied Science Private University, Amman, Jordan  \n[nibras@usm.my](nibras@usm.my)  \nABSTRACT  \nWeb attacks often target web applications because they can be accessed over a network and often have vulnerabilities. The success of an intrusion detection system (IDS) in detecting web attacks depends on an effective traffic classification system. Several previous studies have utilized machine learning classification methods to create an efficient IDS with various datasets for different types of attacks. This paper utilizes the Canadian Institute for Cyber Security’s (CIC-IDS2017) IDS dataset to assess web attacks. Importantly, the dataset contains 80 attributes of recent assaults, as reported in the 2016 McAfee report. Three machine learning algorithms have been evaluated in this research, namely random forests (RF), k-nearest neighbor (KNN), and naive bayes (NB) . The primary goal of this research is to propose an effective machine learning algorithm for the IDS web attacks model. The evaluation compares the performance of three algorithms (RF, KNN, and NB) based on their accuracy and precision in detecting anomalous traffic. The results indicate that the RF outperformed the NB and KNNin terms of average accuracy achieved during the training phase. During the testing phase, the KNN algorithm outperformed others, achieving an average accuracy of 99.4916% . However, RF and KNN achieved 100% average precision and recall rates compared to other algorithms. Finally, the RF and KNN algorithms have been identified as the most effective for detecting IDS web attacks.  \nKEYWORDS  \nintrusion detection systems, CIC-IDS2017, machine learning, false alarms, naive bayes (NB), k-nearest neighbors (KNN), random forest (RF)  \n1 INTRODUCTION  \nDespite the tremendous development of the Internet, it is still vulnerable to security issues in web-based applications. These vulnerabilities give hackers the ability to carry out various web attacks, such as SQL injection, XSS, and brute force [1] . In web attacks, hackers exploit vulnerabilities to gain unauthorized access to web  \nBaklizi, M. K., Atoum, I., Alkhazaleh, M., Kanaker, H., Abdullah, N., Al-Wesabi, O.A., Otoom, A.A. (2024) . Web Attack Intrusion Detection System Using Machine Learning Techniques. International Journal of Online and Biomedical Engineering (iJOE), 20(3), pp. 24–38. [https://doi.org/10.3991/ijoe](https://doi.org/10.3991/ijoe). v20i03.45249  \nArticle submitted 2023-09-24. Revision uploaded 2023-11-15. Final acceptance 2023-11-23.  \n© 2024 by the authors of this article. Published under CC-BY.  \n24 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 20 No. 3 (2024)  \nWeb Attack Intrusion Detection System Using Machine Learning Techniques  \nservers, network devices, or the network itself [2] . Hence the importance of intrusion detection systems, which play a cr","cbCaitZ1aY4ECEnE","https://ap.wps.com/l/cbCaitZ1aY4ECEnE","pdf",645903,1,15,"English","en",105,"# Introduction\n## Related work and datasets\n# Methodology\n## Dataset selection (CIC-IDS2017)\n## Algorithms evaluated (RF, KNN, NB)\n# Experimental results\n## Accuracy comparison\n## Precision and recall analysis\n# Conclusion","[{\"question\":\"What dataset is used to evaluate web attack detection in this study?\",\"answer\":\"The paper uses the Canadian Institute for Cyber Security’s CIC-IDS2017 IDS dataset, which contains 80 attributes of recent assaults.\"},{\"question\":\"Which machine learning algorithms are compared for intrusion detection?\",\"answer\":\"Three algorithms are evaluated: random forests (RF), k-nearest neighbors (KNN), and naive Bayes (NB).\"},{\"question\":\"What performance results are reported for the three algorithms?\",\"answer\":\"RF achieves the best average accuracy during training, while KNN reaches the highest average accuracy during testing (99.4916%). RF and KNN also show 100% average precision and recall rates.\"}]","Web Attack Intrusion Detection System Using Machine Learning Techniques - Research paper | PDF",1785682350,38,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"web-attack-intrusion-detection-system-using-machine-learning-techniques-research-paper","",{"@graph":36,"@context":86},[37,54,69],{"@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/web-attack-intrusion-detection-system-using-machine-learning-techniques-research-paper/118220/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What dataset is used to evaluate web attack detection in this study?","Question",{"text":76,"@type":77},"The paper uses the Canadian Institute for Cyber Security’s CIC-IDS2017 IDS dataset, which contains 80 attributes of recent assaults.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning algorithms are compared for intrusion detection?",{"text":81,"@type":77},"Three algorithms are evaluated: random forests (RF), k-nearest neighbors (KNN), and naive Bayes (NB).",{"name":83,"@type":74,"acceptedAnswer":84},"What performance results are reported for the three algorithms?",{"text":85,"@type":77},"RF achieves the best average accuracy during training, while KNN reaches the highest average accuracy during testing (99.4916%). 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