{"title":"利用深度学习检测恶意浏览器扩展和链接的综述","authors":"Rama Abirami K, Tiago Zonta, Mithileysh Sathiyanarayanan","doi":"10.1109/ICAIC60265.2024.10433842","DOIUrl":null,"url":null,"abstract":"The growth of the Internet has aroused people’s attention toward network security. A secure network environment is fundamental for the expeditious and impeccable development of the Internet. The majority of internet-based tasks can be completed with the help of a web browser. Although many web applications add browser extensions to improve their functionality, some of these extensions are malicious and can access sensitive data without the user’s knowledge. Browser extensions with malicious intent present a growing security concern and have quickly become one of the most prevalent methods used to compromise Internet security. This is largely due to their widespread usage and the extensive privileges they possess. After being installed, these malicious extensions are executed and make an attempt to compromise the victim’s browser. This makes them particularly elusive and challenging to combat. It is crucial to promptly develop an effective strategy to address the threats posed by these extensions. A comprehensive review of the research on browser extension vulnerabilities is presented in this paper. The role of malicious links in web browser extensions are examined for several attacks. Detection of malicious browser extension on various aspects are represented namely Intrusion malicious web browser extensions detection using Intrusion detection, Machine learning based detection methods and Deep learning based techniques to mitigate malicious web browser extensions are examined. This study investigates the critical function of malicious detection in protecting web browsers, looking at the changing threats and risk-reduction tactics. A robust cybersecurity frameworks can be created that not only respond to known threats but also anticipate and thwart the strategies of future cyber adversaries by realizing the significance of proactive detection. Thus this survey provides a detailed comparison of various solutions for malicious browser extension.","PeriodicalId":517265,"journal":{"name":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","volume":"273 4","pages":"1-6"},"PeriodicalIF":0.0000,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Holistic Review on Detection of Malicious Browser Extensions and Links using Deep Learning\",\"authors\":\"Rama Abirami K, Tiago Zonta, Mithileysh Sathiyanarayanan\",\"doi\":\"10.1109/ICAIC60265.2024.10433842\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The growth of the Internet has aroused people’s attention toward network security. A secure network environment is fundamental for the expeditious and impeccable development of the Internet. The majority of internet-based tasks can be completed with the help of a web browser. Although many web applications add browser extensions to improve their functionality, some of these extensions are malicious and can access sensitive data without the user’s knowledge. Browser extensions with malicious intent present a growing security concern and have quickly become one of the most prevalent methods used to compromise Internet security. This is largely due to their widespread usage and the extensive privileges they possess. After being installed, these malicious extensions are executed and make an attempt to compromise the victim’s browser. This makes them particularly elusive and challenging to combat. It is crucial to promptly develop an effective strategy to address the threats posed by these extensions. A comprehensive review of the research on browser extension vulnerabilities is presented in this paper. The role of malicious links in web browser extensions are examined for several attacks. Detection of malicious browser extension on various aspects are represented namely Intrusion malicious web browser extensions detection using Intrusion detection, Machine learning based detection methods and Deep learning based techniques to mitigate malicious web browser extensions are examined. This study investigates the critical function of malicious detection in protecting web browsers, looking at the changing threats and risk-reduction tactics. A robust cybersecurity frameworks can be created that not only respond to known threats but also anticipate and thwart the strategies of future cyber adversaries by realizing the significance of proactive detection. Thus this survey provides a detailed comparison of various solutions for malicious browser extension.\",\"PeriodicalId\":517265,\"journal\":{\"name\":\"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)\",\"volume\":\"273 4\",\"pages\":\"1-6\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICAIC60265.2024.10433842\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAIC60265.2024.10433842","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Holistic Review on Detection of Malicious Browser Extensions and Links using Deep Learning
The growth of the Internet has aroused people’s attention toward network security. A secure network environment is fundamental for the expeditious and impeccable development of the Internet. The majority of internet-based tasks can be completed with the help of a web browser. Although many web applications add browser extensions to improve their functionality, some of these extensions are malicious and can access sensitive data without the user’s knowledge. Browser extensions with malicious intent present a growing security concern and have quickly become one of the most prevalent methods used to compromise Internet security. This is largely due to their widespread usage and the extensive privileges they possess. After being installed, these malicious extensions are executed and make an attempt to compromise the victim’s browser. This makes them particularly elusive and challenging to combat. It is crucial to promptly develop an effective strategy to address the threats posed by these extensions. A comprehensive review of the research on browser extension vulnerabilities is presented in this paper. The role of malicious links in web browser extensions are examined for several attacks. Detection of malicious browser extension on various aspects are represented namely Intrusion malicious web browser extensions detection using Intrusion detection, Machine learning based detection methods and Deep learning based techniques to mitigate malicious web browser extensions are examined. This study investigates the critical function of malicious detection in protecting web browsers, looking at the changing threats and risk-reduction tactics. A robust cybersecurity frameworks can be created that not only respond to known threats but also anticipate and thwart the strategies of future cyber adversaries by realizing the significance of proactive detection. Thus this survey provides a detailed comparison of various solutions for malicious browser extension.