Biblio

Transport Layer Security (TLS), has become the de-facto standard for secure Internet communication. When used correctly, it provides secure data transfer, but used incorrectly, it can leave users vulnerable to attacks while giving them a false sense of security. Numerous efforts have studied the adoption of TLS (and its predecessor, SSL) and its use in the desktop ecosystem, attacks, and vulnerabilities in both desktop clients and servers. However, there is a dearth of knowledge of how TLS is used in mobile platforms. In this paper we use data collected by Lumen, a mobile measurement platform, to analyze how 7,258 Android apps use TLS in the wild. We analyze and fingerprint handshake messages to characterize the TLS APIs and libraries that apps use, and also evaluate weaknesses. We see that about 84% of apps use default OS APIs for TLS. Many apps use third-party TLS libraries; in some cases they are forced to do so because of restricted Android capabilities. Our analysis shows that both approaches have limitations, and that improving TLS security in mobile is not straightforward. Apps that use their own TLS configurations may have vulnerabilities due to developer inexperience, but apps that use OS defaults are vulnerable to certain attacks if the OS is out of date, even if the apps themselves are up to date. We also study certificate verification, and see low prevalence of security measures such as certificate pinning, even among high-risk apps such as those providing financial services, though we did observe major third-party tracking and advertisement services deploying certificate pinning.

Path prediction on the Internet has been a topic of research in the networking community for close to a decade. Applications of path prediction solutions have ranged from optimizing selection of peers in peer- to-peer networks to improving and debugging CDN predictions. Recently, revelations of traffic correlation and surveillance on the Internet have raised the topic of path prediction in the context of network security. Specifically, predicting network paths can allow us to identify and avoid given organizations on network paths (e.g., to avoid traffic correlation attacks in Tor) or to infer the impact of hijacks and interceptions when direct measurements are not available. In this poster we propose the design and implementation of PathCache which aims to reuse measurement data to estimate AS level paths on the Internet. Unlike similar systems, PathCache does not assume that routing on the Internet is destination based. Instead, we develop an algorithm to compute confidence in paths between ASes. These multiple paths ranked by their confidence values are returned to the user.