利用深度学习检测恶意浏览器扩展和链接的综述

Rama Abirami K, Tiago Zonta, Mithileysh Sathiyanarayanan
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引用次数: 0

摘要

互联网的发展引起了人们对网络安全的关注。一个安全的网络环境是互联网快速、无懈可击发展的基础。大多数基于互联网的任务都可以借助网络浏览器来完成。尽管许多网络应用程序都添加了浏览器扩展程序来改善其功能,但其中一些扩展程序是恶意的,可以在用户不知情的情况下访问敏感数据。具有恶意意图的浏览器扩展带来了日益严重的安全问题,并迅速成为危害互联网安全的最普遍方法之一。这主要是由于它们的广泛使用及其拥有的广泛权限。安装后,这些恶意扩展程序会被执行,并试图入侵受害者的浏览器。这使得它们特别难以捉摸,打击起来也极具挑战性。及时制定有效策略来应对这些扩展程序带来的威胁至关重要。本文全面回顾了有关浏览器扩展漏洞的研究。研究了网络浏览器扩展中的恶意链接在几种攻击中的作用。恶意浏览器扩展的检测涉及多个方面,即使用入侵检测、基于机器学习的检测方法和基于深度学习的技术来检测恶意网页浏览器扩展,以减轻恶意网页浏览器扩展的危害。本研究探讨了恶意检测在保护网络浏览器方面的关键作用,研究了不断变化的威胁和降低风险的策略。通过认识到主动检测的重要性,可以创建一个强大的网络安全框架,不仅能应对已知的威胁,还能预测和挫败未来网络对手的策略。因此,本调查详细比较了针对恶意浏览器扩展的各种解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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