Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Detecting semantic cloaking on the web
Proceedings of the 15th international conference on World Wide Web
Tracking Web spam with HTML style similarities
ACM Transactions on the Web (TWEB)
Spamming botnets: signatures and characteristics
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
SS'08 Proceedings of the 17th conference on Security symposium
Beyond blacklists: learning to detect malicious web sites from suspicious URLs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Detection and analysis of drive-by-download attacks and malicious JavaScript code
Proceedings of the 19th international conference on World wide web
PhoneyC: a virtual client honeypot
LEET'09 Proceedings of the 2nd USENIX conference on Large-scale exploits and emergent threats: botnets, spyware, worms, and more
WebCop: locating neighborhoods of malware on the web
LEET'10 Proceedings of the 3rd USENIX conference on Large-scale exploits and emergent threats: botnets, spyware, worms, and more
NOZZLE: a defense against heap-spraying code injection attacks
SSYM'09 Proceedings of the 18th conference on USENIX security symposium
ARROW: GenerAting SignatuRes to Detect DRive-By DOWnloads
Proceedings of the 20th international conference on World wide web
On the effects of registrar-level intervention
LEET'11 Proceedings of the 4th USENIX conference on Large-scale exploits and emergent threats
Design and Evaluation of a Real-Time URL Spam Filtering Service
SP '11 Proceedings of the 2011 IEEE Symposium on Security and Privacy
Fear the EAR: discovering and mitigating execution after redirect vulnerabilities
Proceedings of the 18th ACM conference on Computer and communications security
SURF: detecting and measuring search poisoning
Proceedings of the 18th ACM conference on Computer and communications security
Cloak and dagger: dynamics of web search cloaking
Proceedings of the 18th ACM conference on Computer and communications security
Understanding fraudulent activities in online ad exchanges
Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference
Rozzle: De-cloaking Internet Malware
SP '12 Proceedings of the 2012 IEEE Symposium on Security and Privacy
Knowing your enemy: understanding and detecting malicious web advertising
Proceedings of the 2012 ACM conference on Computer and communications security
Manufacturing compromise: the emergence of exploit-as-a-service
Proceedings of the 2012 ACM conference on Computer and communications security
Revolver: an automated approach to the detection of evasiveweb-based malware
SEC'13 Proceedings of the 22nd USENIX conference on Security
Stranger danger: exploring the ecosystem of ad-based URL shortening services
Proceedings of the 23rd international conference on World wide web
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The web is one of the most popular vectors to spread malware. Attackers lure victims to visit compromised web pages or entice them to click on malicious links. These victims are redirected to sites that exploit their browsers or trick them into installing malicious software using social engineering. In this paper, we tackle the problem of detecting malicious web pages from a novel angle. Instead of looking at particular features of a (malicious) web page, we analyze how a large and diverse set of web browsers reach these pages. That is, we use the browsers of a collection of web users to record their interactions with websites, as well as the redirections they go through to reach their final destinations. We then aggregate the different redirection chains that lead to a specific web page and analyze the characteristics of the resulting redirection graph. As we will show, these characteristics can be used to detect malicious pages. We argue that our approach is less prone to evasion than previous systems, allows us to also detect scam pages that rely on social engineering rather than only those that exploit browser vulnerabilities, and can be implemented efficiently. We developed a system, called SpiderWeb, which implements our proposed approach. We show that this system works well in detecting web pages that deliver malware.