The DOI matches its Crossref record.
Detection and analysis of drive-by-download attacks and malicious JavaScript code
Abstract
JavaScript is a browser scripting language that allows developers to create sophisticated client-side interfaces for web applications. However, JavaScript code is also used to carry out attacks against the user's browser and its extensions. These attacks usually result in the download of additional malware that takes complete control of the victim's platform, and are, therefore, called "drive-by downloads." Unfortunately, the dynamic nature of the JavaScript language and its tight integration with the browser make it difficult to detect and block malicious JavaScript code.
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Drive-by download attacks are prevalent in programmatic interface manipulation, where the attacker injects malicious scripts (e.g. Javascript) into vulnerable websites to redirect users to a malicious download/installation.
“This is prevalent in drive-by download attacks where the attacker injects malicious scripts (e.g. Javascript) into vulnerable websites to redirect users to a malicious download/installation [Egele et al. 2008; Cova et al. 2010].”
From A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks (2016) · cites this paper as Cova et al. 2010
The rise of drive-by malware, where the user's system is infected with a single click, can be attributed in part to the lower risk of detection from less user interaction.
“This is one of the reasons that can be attributed to the rise of driveby malware, where the user's system is infected with a single click [Cova et al. 2010].”
From A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks (2016) · cites this paper as Cova et al. 2010
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Malicious PDF detection using metadata and structural features
Smutz, C., & Stavrou, A. (2012) · Proceedings of the 28th Annual Computer Security Applications Conference · doi:10.1145/2420950.2420987 · Cited by 271
A Taxonomy of Attacks and a Survey of Defence Mechanisms for Semantic Social Engineering Attacks
Heartfield, R., & Loukas, G. (2016) · ACM Computing Surveys · doi:10.1145/2835375 · Cited by 202
Exploiting Trust: Stealthy Attacks Through Socioware and Insider Threats
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Metadata
- Authors
- Cova, M., Kruegel, C., & Vigna, G.
- Affiliations
- University of California, Santa Barbara (US)
- Venue
- Proceedings of the 19th international conference on World wide web
- Publisher
- ACM
- Published
- 2010-04-26
- Pages
- 281-290
- OpenAlex
- W1970867218
- Cited by
- 580
- References
- 0
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 14 · 2023: 22 · 2022: 21 · 2021: 28 · 2020: 34 · 2019: 36 · 2018: 33 · 2017: 48 · 2016: 40 · 2015: 51 · 2014: 71 · 2013: 53 · 2012: 47
- FWCI
- 35.71
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Web Application Security Vulnerabilities
- Physical Sciences › Computer Science › Software › Advanced Malware Detection Techniques
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Keywords
- drive-by downloads, malicious JavaScript, malware detection, browser security, web application security, browser extension, client-side attacks
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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