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Selected Papers See all papers
Stranger Danger: Exploring the Ecosystem of Ad-based URL Shortening ServicesNick Nikiforakis, Federico Maggi, Gianluca Stringhini, M. Zubair Rafique, Wouter Joosen, Christopher Kruegel, Frank Piessens, Giovanni Vigna, Stefano Zanero
URL shortening services facilitate the need of exchanging long URLs using limited space, by creating compact URL aliases that redirect users to the original URLs when followed. Some of these services show advertisements (ads) to link-clicking users and pay a commission of their advertising earnings to link-shortening users. In this paper, we investigate the ecosystem of these increasingly popular ad-based URL shortening services. Even though traditional URL shortening services have been thoroughly investigated in previous research, we argue that, due to the monetary incentives and the presence of third-party advertising networks, ad-based URL shortening services and their users are exposed to more hazards than traditional shortening services. By analyzing the services themselves, the advertisers involved, and their users, we uncover a series of issues that are actively exploited by malicious advertisers and endanger the users. Moreover, next to documenting the ongoing abuse, we suggest a series of defense mechanisms that services and users can adopt to protect themselves.
Measurement Short URLs
Two years of short URLs internet measurement: security threats and countermeasuresFederico Maggi, Alessandro Frossi, Stefano Zanero, Gianluca Stringhini, Brett Stone-Gross, Christopher Kruegel, Giovanni Vigna
URL shortening services have become extremely popular. However, it is still unclear whether they are an effective and reliable tool that can be leveraged to hide malicious URLs, and to what extent these abuses can impact the end users. With these questions in mind, we first analyzed existing countermeasures adopted by popular shortening services. Surprisingly, we found such countermeasures to be ineffective and trivial to bypass. This first measurement motivated us to proceed further with a large-scale collection of the HTTP interactions that originate when web users access live pages that contain short URLs. To this end, we monitored 622 distinct URL shortening services between March 2010 and April 2012, and collected 24,953,881 distinct short URLs. With this large dataset, we studied the abuse of short URLs. Despite short URLs are a significant, new security risk, in accordance with the reports resulting from the observation of the overall phishing and spamming activity, we found that only a relatively small fraction of users ever encountered malicious short URLs. Interestingly, during the second year of measurement, we noticed an increased percentage of short URLs being abused for drive-by download campaigns and a decreased percentage of short URLs being abused for spam campaigns. In addition to these security-related findings, our unique monitoring infrastructure and large dataset allowed us to complement previous research on short URLs and analyze these web services from the user’s perspective.
Crowdsourcing Measurement Short URLs
Protecting a Moving Target: Addressing Web Application Concept DriftFederico Maggi, William Robertson, Christopher Kruegel, Giovanni Vigna
Because of the ad hoc nature of web applications, intrusion detection systems that leverage machine learning techniques are particularly well-suited for protecting websites. The reason is that these systems are able to characterize the applications' normal behavior in an automated fashion. However, anomaly-based detectors for web applications suffer from false positives that are generated whenever the applications being protected change. These false positives need to be analyzed by the security officer who then has to interact with the web application developers to confirm that the reported alerts were indeed erroneous detections. In this paper, we propose a novel technique for the automatic detection of changes in web applications, which allows for the selective retraining of the affected anomaly detection models. We demonstrate that, by correctly identifying legitimate changes in web applications, we can reduce false positives and allow for the automated retraining of the anomaly models. We have evaluated our approach by analyzing a number of real-world applications. Our analysis shows that web applications indeed change substantially over time, and that our technique is able to effectively detect changes and automatically adapt the anomaly detection models to the new structure of the changed web applications.
Anomaly detection Machine learning Web security