Research by Ashwin Vamshi and Abhijit Mohanta
Quick-Look Summary:
- IcedID appears to be taking the place of Emotet, based on a significant influx of samples in our threat intelligence systems
- A majority of these IcedID samples are distributed via xlsm files attached to emails
- We’ve identified three ways these Excel 4 Macros are evading detection
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.
As attackers continually evolve their tactics, the arsenal of tools at hand for defenders needs to respond to attacker complexity while still enabling day-to-day business to happen.
When it comes to detecting malware, the arms race between attackers and defenders is certainly nothing new. The once seemingly simple battle between nuisance script kiddie worms and simple anti-virus software evolved over time into a much more complex and layered approach towards stopping powerful weapons against organizations to extort, incur damages, and steal intellectual property. For a long time now, malware-detection technologies have become more sophisticated as malware works harder than ever to gain access to a target machine and then conceal its presence as it runs.
As a part of a pretty crazy week (Microsoft/RDS, Apple/Mojave/High Sierra, Adobe Acrobat/ Flash Player) when it comes to security updates, some new speculative execution vulnerabilities were disclosed and fixed.
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.
Osquery, at its most basic level, is an operating system instrumentation framework that exposes the OS as a SQL database. SQL queries can be run to view information about the systems similar to any SQL database, providing a unified cross platform framework (i.e. endpoints running on multiple operating systems can be queried using the industry standard database language: SQL.
This structured approach for collecting and accessing data introduces great flexibility, making it useful for multiple purposes. For example, queries can be constructed to audit infrastructure for compliance, vulnerabilities, malware analysis and intrusion detection, etc. Data collected by osquery can be useful to anybody from IT support teams to CSIRTs. However, in this blog post we’ll narrow our focus and explore how to use osquery specifically for macOS malware analysis (though the methodologies discussed are the same for Windows and Linux operating systems).