Karri Narendra Reddy, Kolapalli Naga, Venkatesh, Dr. T. Prem
{"title":"使用机器学习模型检测恶意URL网站","authors":"Karri Narendra Reddy, Kolapalli Naga, Venkatesh, Dr. T. Prem","doi":"10.1109/ICESC57686.2023.10193080","DOIUrl":null,"url":null,"abstract":"In recent years, web applications have become increasingly vulnerable to hacking, with an estimated occurrence of 32,590 attacks every day. However, many web developers and website owners are unprepared to identify and prevent these attacks. Hackers utilize various techniques, including phishing websites to gain unauthorized access or compromise authentic web programmers. This research study examines the web application security and the frequency of attacks on such systems. This study focuses on the most common types of attacks and suggests effective detection methods for preventing them. Recently, the secure coding methodologies and machine learning algorithms are used to detect and block unauthorized access and phishing attacks.In almost all the existing research works, a web application is developed to test several website URLs. The findings suggest that the model is capable of detecting malicious web application attacks. Furthermore, the model compares the performance of several machine learning algorithms for identifying phishing website links and detecting with the best model.","PeriodicalId":235381,"journal":{"name":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detection of Malicious URL Websites using Machine Learning Models\",\"authors\":\"Karri Narendra Reddy, Kolapalli Naga, Venkatesh, Dr. T. Prem\",\"doi\":\"10.1109/ICESC57686.2023.10193080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, web applications have become increasingly vulnerable to hacking, with an estimated occurrence of 32,590 attacks every day. However, many web developers and website owners are unprepared to identify and prevent these attacks. Hackers utilize various techniques, including phishing websites to gain unauthorized access or compromise authentic web programmers. This research study examines the web application security and the frequency of attacks on such systems. This study focuses on the most common types of attacks and suggests effective detection methods for preventing them. Recently, the secure coding methodologies and machine learning algorithms are used to detect and block unauthorized access and phishing attacks.In almost all the existing research works, a web application is developed to test several website URLs. The findings suggest that the model is capable of detecting malicious web application attacks. Furthermore, the model compares the performance of several machine learning algorithms for identifying phishing website links and detecting with the best model.\",\"PeriodicalId\":235381,\"journal\":{\"name\":\"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICESC57686.2023.10193080\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICESC57686.2023.10193080","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Detection of Malicious URL Websites using Machine Learning Models
In recent years, web applications have become increasingly vulnerable to hacking, with an estimated occurrence of 32,590 attacks every day. However, many web developers and website owners are unprepared to identify and prevent these attacks. Hackers utilize various techniques, including phishing websites to gain unauthorized access or compromise authentic web programmers. This research study examines the web application security and the frequency of attacks on such systems. This study focuses on the most common types of attacks and suggests effective detection methods for preventing them. Recently, the secure coding methodologies and machine learning algorithms are used to detect and block unauthorized access and phishing attacks.In almost all the existing research works, a web application is developed to test several website URLs. The findings suggest that the model is capable of detecting malicious web application attacks. Furthermore, the model compares the performance of several machine learning algorithms for identifying phishing website links and detecting with the best model.