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In this paper we introduce Timeloops a novel technique for automaticallylearning system call filtering policies for containerized microservicesapplications. At run-time, Timeloops automatically learns which system calls aprogram should be allowed to invoke while rejecting attempts to call spurioussystem calls. Further, Timeloops addresses many of the shortcomings ofstate-of-the-art static analysis-based techniques, such as the ability togenerate tight filters for programs written in interpreted languages such asPHP, Python, and JavaScript. Timeloops has a simple and robust implementationbecause it is mainly built out of commodity, and proven, technologies such asseccomp-BPF, systemd, and Podman containers, with fewer than 500 lines of code.We demonstrate the utility of Timeloops by learning system calls for individualservices and two microservices benchmark applications, which utilize populartechnologies like Python Flask, Nginx (with PHP and Lua modules), ApacheThrift, Memcached, Redis, and MongoDB. Further, the amortized performance ofTimeloops is similar to that of an unhardened system while producing a smallersystem call filter than state-of-the-art static analysis-based techniques.

Timeloops: Automatic System Call Policy Learning for Containerized Microservices