All Your iFRAMEs Point to Us

By Niels Provos, Panayiotis Mavrommatis, Moheeb Abu Rajab, Fabian Monrose. USENIX Security Symposium, 2008

593 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/87de247714bf0e4a9349918322fb820ee6904728

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Citations in the corpus

CAMP: Content-Agnostic Malware Protection (2013, background): “Some of the client features such as the content hash are used directly in subsequent aggregate lookups.”
Adversarial Web Search (2011, background): “Further, some spam pages exist to promote or even install malware [68, 192, 193].”
Shady paths: leveraging surfing crowds to detect malicious web pages (2013, background): “The most common way of infecting victims with malware is to present them with a web page that performs a drive-by download attack [18].”
Hey, You, Get Off of My Market: Detecting Malicious Apps in Official and Alternative Android Markets (2012, background): “Other systematic security studies The third area of related work includes prior efforts [46, 50, 51] to systematically study malicious web contents.”
Detection of Malicious PDF Files Based on Hierarchical Document Structure (2013, methodology): “Both of these methods are focused on detection of malicious JavaScript content in PDF documents, which relates them to a large body of work on detection of drive-by-downloads; e.g., [27, 31, 10, 6].”
Static detection of malicious JavaScript-bearing PDF documents (2011, background): “[17]; however, the lack of a technical presentation in this reference prevents us from a detailed comparison.”
Detecting and characterizing social spam campaigns (2010, methodology): “Wepawet is a specialized tool that uses machine learning to identify web pages with characteristics associated with drive-by download attacks [34].”
Finding the Linchpins of the Dark Web: a Study on Topologically Dedicated Hosts on Malicious Web Infrastructures (2013, background): “• Twitter feed: We run Twitter Search APIs [29] to pick top 10 trending terms every day.”
Detection and analysis of drive-by-download attacks and malicious JavaScript code (2010, background): “Categories and Subject Descriptors K.6.5 [Computing Milieux]: Management of Computing and Information Systems Security and Protection General Terms Security Keywords Drive-by-download attacks, web client exploits, anomaly detection 1.”
Graphical passwords: Learning from the first twelve years (2012, methodology): “Malicious software includes any unauthorized software installed or downloaded without a user’s informed consent, for example, computer viruses and worms, Trojan horse software including login spoofing, code silently installed upon visiting websites [Provos et al. 2008], and mobile code (e.”
Effective and Efficient Malware Detection at the End Host (2009, background): “There is a large number of previous work that studies the behavior [34, 37, 42] or the prevalence [31, 35 ] of different types of malware.”
FlashDetect: ActionScript 3 Malware Detection (2012, unknown): “[20], and Monkey-Spider [7].”
EvilSeed: A Guided Approach to Finding Malicious Web Pages (2012, methodology): “low-interaction honeyclient) and the Google Safe Browsing blacklist [2] (a blacklist produced using high-interaction honeyclients) as our oracle.”
ZOZZLE: Fast and Precise In-Browser JavaScript Malware Detection (2011, background): “When captured at the network layer or through a static crawler, the content of malicious web pages is usually highly obfuscated opening the door to static feature based exploit detection [10, 20, 24, 27, 28].”
Reviewer Integration and Performance Measurement for Malware Detection (2015, background): “which moderate access to expensive analysis in a virtual machine [5, 19].”
Evil Searching: Compromise and Recompromise of Internet Hosts for Phishing (2009, background): “[22] tracked malicious URLs advertising malware, finding that 1.”