It’s been clear for several years that signature-based anti-virus and Intrusion Prevention / Detection controls are not sufficient to detect modern, fast-changing malware. Sandboxing as become a popular (rightfully so) complementary control to detect “unknown” malware, i.e. malware for which no signature exists yet. The concept is straightforward. Analyze inbound suspicious files by allowing them to run in a virtual machine environment. While sandboxing has been successful, I believe it’s worthwhile to understand its limitations. Here they are:
- Access to the malware in motion, i.e. on the network, is not always available.
- Most sandboxing solutions are limited to Windows
- Malware authors have developed techniques to discover virtualized or testing environments
- Newer malware communication techniques use random, one-time domains and non-HTTP protocols
- Sandboxing cannot confirm malware actually installed and infected the endpoint
- Droppers, the first stage of multi-stage malware is often the only part that is analyzed
Please check out Damballa’s Webcast on the Shortfalls of Security Sandboxing for more details.
Let me reiterate, I am not saying that sandboxing is not valuable. It surely is. However, due to the limitations listed above, we recommend that it be complemented by a log-based anomaly detection control that’s analyzing one or more of the following: outbound DNS traffic, all outbound traffic through the firewall and proxy server, user connections to servers, for retailers – POS terminals connections to servers, application authentications and authorizations. In addition to different network traffic sources, there are also a variety of statistical approaches available including supervised and unsupervised machine learning algorithms.
So in order to substantially reduce the risk of a data breach from unknown malware, the issue is not sandboxing or anomaly detection, it’s sandboxing and anomaly detection.
This post has been cross-posted from www.riskpundit.com.