Biblio

Recent research suggests that 88% of Android applications that use Java cryptographic APIs make at least one mistake, which results in an insecure implementation. It is unclear, however, if these mistakes originate from code written by application or third-party library developers. Understanding the responsible party for a misuse case is important for vulnerability disclosure. In this paper, we bridge this knowledge gap and introduce source attribution to the analysis of cryptographic API misuse. We developed BinSight, a static program analyzer that supports source attribution, and we analyzed 132K Android applications collected in years 2012, 2015, and 2016. Our results suggest that third-party libraries are the main source of cryptographic API misuse. In particular, 90% of the violating applications, which contain at least one call-site to Java cryptographic API, originate from libraries. When compared to 2012, we found the use of ECB mode for symmetric ciphers has significantly decreased in 2016, for both application and third-party library code. Unlike application code, however, third-party libraries have significantly increased their reliance on static encryption keys for symmetric ciphers and static IVs for CBC mode ciphers. Finally, we found that the insecure RC4 and DES ciphers were the second and the third most used ciphers in 2016.

In the face of large-scale automated cyber-attacks to large online services, fast detection and remediation of compromised accounts are crucial to limit the spread of new attacks and to mitigate the overall damage to users, companies, and the public at large. We advocate a fully automated approach based on machine learning to enable large-scale online service providers to quickly identify potentially compromised accounts. We develop an early warning system for the detection of suspicious account activity with the goal of quick identification and remediation of compromised accounts. We demonstrate the feasibility and applicability of our proposed system in a four month experiment at a large-scale online service provider using real-world production data encompassing hundreds of millions of users. We show that - even using only login data, features with low computational cost, and a basic model selection approach - around one out of five accounts later flagged as suspicious are correctly predicted a month in advance based on one week's worth of their login activity.

The orthodox paradigm to defend against automated social-engineering attacks in large-scale socio-technical systems is reactive and victim-agnostic. Defenses generally focus on identifying the attacks/attackers (e.g., phishing emails, social-bot infiltrations, malware offered for download). To change the status quo, we propose to identify, even if imperfectly, the vulnerable user population, that is, the users that are likely to fall victim to such attacks. Once identified, information about the vulnerable population can be used in two ways. First, the vulnerable population can be influenced by the defender through several means including: education, specialized user experience, extra protection layers and watchdogs. In the same vein, information about the vulnerable population can ultimately be used to fine-tune and reprioritize defense mechanisms to offer differentiated protection, possibly at the cost of additional friction generated by the defense mechanism. Secondly, information about the user population can be used to identify an attack (or compromised users) based on differences between the general and the vulnerable population. This paper considers the implications of the proposed paradigm on existing defenses in three areas (phishing of user credentials, malware distribution and socialbot infiltration) and discusses how using knowledge of the vulnerable population can enable more robust defenses.