Cross-site scripting (XSS) attacks and mitigation: A survey

被引:61
|
作者
Rodriguez, German E. [1 ,2 ]
Torres, Jenny G. [1 ]
Flores, Pamela [1 ]
Benavides, Diego E. [1 ,2 ]
机构
[1] Escuela Politec Nacl, Fac Ingn Sistemas Ladron Guevara & Roca, Quito, Ecuador
[2] Univ Fuerzas Armadas ESPE, Dept Ciencias Comp, Latacunga, Ecuador
关键词
XSS; Cookies; DOM-XSS; DEFENSE; VULNERABILITIES; FRAMEWORK; INJECTION;
D O I
10.1016/j.comnet.2019.106960
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
The results of the Cisco 2018 Annual Security Report show that all analyzed web applications have at least one vulnerability. It also shows that web attacks are becoming more frequent, specific and sophisticated. According to this report, 40% of all attack attempts lead to a method known as Cross-Site Scripting (XSS), which was the most widely used technique. According to the OWASP Top 10 - 2017 security risk, this type of attack is ranked No. 7, and it is noted that XSS is present in approximately two thirds of all web applications. This attack occurs when a malicious user uses a web application to execute or send malicious code on another user's computer. Also, Cross Site Scripting is a type of cyber attack by which vulnerabilities are searched in a web application to introduce a harmful script. This implies that user information can be affected by stealing cookies, phishing, or attacking a company's entire network. In this context, we have analyzed a total of 67 documents to collect information of the tools and methods that the scientific community has used to detect and mitigate these type of attack. It has been hypothesized that the trend in the proposal of traditional methods to mitigate XSS attacks is greater than the proposals that use some artificial intelligence technique. Our results show that the trend is increasing in the proposals that analyze the content of web pages (13.20%), as well as those that serve as a toolkit for web browsers (16.98%). Also, we have found that there is a low tendency in the use of artificial intelligence techniques to detect or mitigate this attack, using Web Classifiers (9.43%). (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页数:23
相关论文
共 50 条
  • [1] Detection of Web Cross-Site Scripting (XSS) Attacks
    Alsaffar, Mohammad
    Aljaloud, Saud
    Mohammed, Badiea Abdulkarem
    Al-Mekhlafi, Zeyad Ghaleb
    Almurayziq, Tariq S.
    Alshammari, Gharbi
    Alshammari, Abdullah
    [J]. ELECTRONICS, 2022, 11 (14)
  • [2] XSStudent: Proposal to Avoid Cross-Site Scripting (XSS) Attacks in Universities
    Escuela Politecnica Nacional, Facultad de Ingenieŕia de Sistemas, Quito, Ecuador
    不详
    [J]. Cyber Secur. Netw. Conf., CSNet, 1600, (142-149):
  • [3] XSStudent: Proposal to Avoid Cross-Site Scripting (XSS) Attacks in Universities
    Rodriguez, German
    Torres, Jenny
    Flores, Pamela
    Benavides, Eduardo
    Nunez-Agurto, Daniel
    [J]. 2019 3RD CYBER SECURITY IN NETWORKING CONFERENCE (CSNET), 2019,
  • [4] XSS-GUARD: Precise dynamic prevention of cross-site scripting attacks
    Bisht, Prithvi
    Venkatakrishnan, V. N.
    [J]. DETECTION OF INTRUSIONS AND MALWARE, AND VULNERABILITY ASSESSMENT, 2008, 5137 : 23 - 43
  • [5] XSS-Dec: A Hybrid Solution to Mitigate Cross-Site Scripting Attacks
    Sundareswaran, Smitha
    Squicciarini, Anna Cinzia
    [J]. DATA AND APPLICATIONS SECURITY AND PRIVACY XXVI, 2012, 7371 : 223 - 238
  • [6] Detection of cross-site scripting (XSS) attacks using machine learning techniques: a review
    Jasleen Kaur
    Urvashi Garg
    Gourav Bathla
    [J]. Artificial Intelligence Review, 2023, 56 : 12725 - 12769
  • [7] Cross-Site Scripting (XSS) attacks and defense mechanisms: classification and state-of-the-art
    Gupta S.
    Gupta B.B.
    [J]. International Journal of System Assurance Engineering and Management, 2017, 8 (Suppl 1) : 512 - 530
  • [8] Detection of cross-site scripting (XSS) attacks using machine learning techniques: a review
    Kaur, Jasleen
    Garg, Urvashi
    Bathla, Gourav
    [J]. ARTIFICIAL INTELLIGENCE REVIEW, 2023, 56 (11) : 12725 - 12769
  • [9] Defending against Cross-Site Scripting Attacks
    Shar, Lwin Khin
    Tan, Hee Beng Kuan
    [J]. COMPUTER, 2012, 45 (03) : 55 - 62
  • [10] XSS-SAFE: A Server-Side Approach to Detect and Mitigate Cross-Site Scripting (XSS) Attacks in JavaScript Code
    Shashank Gupta
    B. B. Gupta
    [J]. Arabian Journal for Science and Engineering, 2016, 41 : 897 - 920