Network traffic encryption is increasing. This increase is driven by demand for privacy protection and the availability of great services for deploying certificates for free. According to Google’s Transparency Report, 88% of web traffic performed on Chrome for Windows is encrypted, and that number is higher for macOS, Android, and ChromeOS. The encryption trend is even clearer when you look at the percentage of HTTPS browsing time in the Transparency Report. At the same time, malware is also following this trend, as the increased security allows attackers to evade some detection mechanisms.
Be it for macOS or my dog eating out of the trash, there is no such thing as a bullet-proof security policy. It’s all about creating a threshold of standards- something to work off of while simultaneously reducing overall risk (you know, like storing your trash can on the counter, for example).
In this blog post I’ll cover osquery’s ability to provide performant behavior and its capabilities to excel at enterprise grade requirements. Many observations covered in this blog will highlight various capabilities of osquery that should aid in your journey toward an enterprise-grade osquery deployment.
Osquery has become a popular source of instrumentation for a wide variety of use cases. On github security showcase, it is currently among the top most popular open source security projects. Given the popularity, a recurring question is what use cases can one address with osquery in an enterprise environment?
This previous blog post explored ways to use osquery for macOS malware analysis. Using the same methodology introduced there, we analyzed five additional macOS malware variants and recorded their behavior to understand the techniques they used. Below, you’ll find the techniques used by Calisto, Dummy, HiddenLotus, LamePyre and WireLurker. Read on to explore how to translate the techniques used by these malware into queries you can run to hunt for the active presence or historical artifacts using osquery.