Michele Spagnuolo (Google), David Dworken (Google), Artur Janc (Google), Santiago Díaz (Google), Lukas Weichselbaum (Google)

The area of security measurability is gaining increased attention, with a wide range of organizations calling for the development of scalable approaches for assessing the security of software systems and infrastructure. In this paper, we present our experience developing Security Signals, a comprehensive system providing security measurability for web services, deployed in a complex application ecosystem of thousands of web services handling traffic from billions of users. The system collects security-relevant information from production HTTP traffic at the reverse proxy layer, utilizing novel concepts such as synthetic signals augmented with additional risk information to provide a holistic view of the security posture of individual services and the broader application ecosystem. This approach to measurability has enabled large-scale security improvements to our services, including prioritized rollouts of security enhancements and the implementation of automated regression monitoring. Furthermore, it has proven valuable for security research and prioritization of defensive work. Security Signals addresses shortcomings of prior web measurability proposals by tracking a comprehensive set of security properties relevant to web applications, and by extracting insights from collected data for use by both security experts and non-experts. We believe the lessons learned from the implementation and use of Security Signals offer valuable insights for practitioners responsible for web service security, potentially inspiring new approaches to web security measurability.

View More Papers

Alba: The Dawn of Scalable Bridges for Blockchains

Giulia Scaffino (TU Wien), Lukas Aumayr (TU Wien), Mahsa Bastankhah (Princeton University), Zeta Avarikioti (TU Wien), Matteo Maffei (TU Wien)

Read More

A Key-Driven Framework for Identity-Preserving Face Anonymization

Miaomiao Wang (Shanghai University), Guang Hua (Singapore Institute of Technology), Sheng Li (Fudan University), Guorui Feng (Shanghai University)

Read More

Sheep's Clothing, Wolf's Data: Detecting Server-Induced Client Vulnerabilities in...

Fangming Gu (Institute of Information Engineering, Chinese Academy of Sciences), Qingli Guo (Institute of Information Engineering, Chinese Academy of Sciences), Jie Lu (Institute of Computing Technology, Chinese Academy of Sciences), Qinghe Xie (Institute of Information Engineering, Chinese Academy of Sciences), Beibei Zhao (Institute of Information Engineering, Chinese Academy of Sciences), Kangjie Lu (University of Minnesota),…

Read More

Incorporating Gradients to Rules: Towards Lightweight, Adaptive Provenance-based Intrusion...

Lingzhi Wang (Northwestern University), Xiangmin Shen (Northwestern University), Weijian Li (Northwestern University), Zhenyuan LI (Zhejiang University), R. Sekar (Stony Brook University), Han Liu (Northwestern University), Yan Chen (Northwestern University)

Read More