- Home
- Techniques
- Enterprise
- Dynamic Resolution
Dynamic Resolution
Sub-techniques (3)
Adversaries may dynamically establish connections to command and control infrastructure to evade common detections and remediations. This may be achieved by using malware that shares a common algorithm with the infrastructure the adversary uses to receive the malware's communications. These calculations can be used to dynamically adjust parameters such as the domain name, IP address, or port number the malware uses for command and control.
Adversaries may use dynamic resolution for the purpose of Fallback Channels. When contact is lost with the primary command and control server malware may employ dynamic resolution as a means to reestablishing command and control.[1][2][3]
Procedure Examples
| Name | Description |
|---|---|
| MAZE |
MAZE has forged POST strings with a random choice from a list of possibilities including "forum", "php", "view", etc. while making connection with the C2, hindering detection efforts.[7] |
| NETEAGLE |
NETEAGLE can use HTTP to download resources that contain an IP address and port number pair to connect to for C2.[6] |
| RTM |
RTM has resolved Pony C2 server IP addresses by either converting Bitcoin blockchain transaction data to specific octets, or accessing IP addresses directly within the Namecoin blockchain.[8][9] |
Mitigations
| Mitigation | Description |
|---|---|
| Network Intrusion Prevention |
Network intrusion detection and prevention systems that use network signatures to identify traffic for specific adversary malware can be used to mitigate activity at the network level. Malware researchers can reverse engineer malware variants that use dynamic resolution and determine future C2 infrastructure that the malware will attempt to contact, but this is a time and resource intensive effort.[4][5] |
| Restrict Web-Based Content |
In some cases a local DNS sinkhole may be used to help prevent behaviors associated with dynamic resolution. |
Detection
Detecting dynamically generated C2 can be challenging due to the number of different algorithms, constantly evolving malware families, and the increasing complexity of the algorithms. There are multiple approaches to detecting a pseudo-randomly generated domain name, including using frequency analysis, Markov chains, entropy, proportion of dictionary words, ratio of vowels to other characters, and more [10]. CDN domains may trigger these detections due to the format of their domain names. In addition to detecting algorithm generated domains based on the name, another more general approach for detecting a suspicious domain is to check for recently registered names or for rarely visited domains.
References
- Brumaghin, E. et al. (2017, September 18). CCleanup: A Vast Number of Machines at Risk. Retrieved March 9, 2018.
- Dunwoody, M.. (2017, April 3). Dissecting One of APT29’s Fileless WMI and PowerShell Backdoors (POSHSPY). Retrieved April 5, 2017.
- ESET. (2017, December 21). Sednit update: How Fancy Bear Spent the Year. Retrieved February 18, 2019.
- Sternfeld, U. (2016). Dissecting Domain Generation Algorithms: Eight Real World DGA Variants. Retrieved February 18, 2019.
- Kasza, A. (2015, February 18). Using Algorithms to Brute Force Algorithms. Retrieved February 18, 2019.
- FireEye Labs. (2015, April). APT30 AND THE MECHANICS OF A LONG-RUNNING CYBER ESPIONAGE OPERATION. Retrieved May 1, 2015.
- Mundo, A. (2020, March 26). Ransomware Maze. Retrieved May 18, 2020.
- Eisenkraft, K., Olshtein, A. (2019, October 17). Pony’s C&C servers hidden inside the Bitcoin blockchain. Retrieved June 15, 2020.
- Duncan, B., Harbison, M. (2019, January 23). Russian Language Malspam Pushing Redaman Banking Malware. Retrieved June 16, 2020.
- Jacobs, J. (2014, October 2). Building a DGA Classifier: Part 2, Feature Engineering. Retrieved February 18, 2019.