Domain Generation Algorithms

Adversaries may make use of Domain Generation Algorithms (DGAs) to dynamically identify a destination for command and control traffic rather than relying on a list of static IP addresses or domains. This has the advantage of making it much harder for defenders block, track, or take over the command and control channel, as there potentially could be thousands of domains that malware can check for instructions.[1][2][3]

DGAs can take the form of apparently random or "gibberish" strings (ex: when they construct domain names by generating each letter. Alternatively, some DGAs employ whole words as the unit by concatenating words together instead of letters (ex: Many DGAs are time-based, generating a different domain for each time period (hourly, daily, monthly, etc). Others incorporate a seed value as well to make predicting future domains more difficult for defenders.[1][2][4][5]

Adversaries may use DGAs for the purpose of Fallback Channels. When contact is lost with the primary command and control server malware may employ a DGA as a means to reestablishing command and control.[4][6][7]

ID: T1483

Tactic: Command And Control

Platform:  Linux, macOS, Windows

Permissions Required:  User

Data Sources:  Process use of network, Packet capture, Network device logs, Netflow/Enclave netflow, DNS records

Contributors:  Sylvain Gil, Exabeam; Barry Shteiman, Exabeam; Ryan Benson, Exabeam

Version: 1.0



BONDUPDATER uses a DGA to communicate with command and control servers.[8]


CCBkdr can use a DGA for Fallback Channels if communications with the primary command and control server are lost.[4]


CHOPSTICK can use a DGA for Fallback Channels, domains are generated by concatenating words from lists.[7]


Ebury has used a DGA to generate a domain name for C2.[9]


POSHSPY uses a DGA to derive command and control URLs from a word list.[6]


This technique may be difficult to mitigate since the domains can be registered just before they are used, and disposed shortly after. Malware researchers can reverse-engineer malware variants that use DGAs and determine future domains that the malware will attempt to contact, but this is a time and resource intensive effort.[1][10] Malware is also increasingly incorporating seed values that can be unique for each instance, which would then need to be determined to extract future generated domains. In some cases, the seed that a particular sample uses can be extracted from DNS traffic.[5] Even so, there can be thousands of possible domains generated per day; this makes it impractical for defenders to preemptively register all possible C2 domains due to the cost. In some cases a local DNS sinkhole may be used to help prevent DGA-based command and control at a reduced cost.

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. Signatures are often for unique indicators within protocols and may be based on the specific protocol used by a particular adversary or tool, and will likely be different across various malware families and versions. Adversaries will likely change tool C2 signatures over time or construct protocols in such a way as to avoid detection by common defensive tools. [11]


Detecting dynamically generated domains can be challenging due to the number of different DGA algorithms, constantly evolving malware families, and the increasing complexity of the algorithms. There is a myriad of approaches for 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.[12] CDN domains may trigger these detections due to the format of their domain names. In addition to detecting a DGA domain 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.

Machine learning approaches to detecting DGA domains have been developed and have seen success in applications. One approach is to use N-Gram methods to determine a randomness score for strings used in the domain name. If the randomness score is high, and the domains are not whitelisted (CDN, etc), then it may be determined if a domain or related to a legitimate host or DGA.[13] Another approach is to use deep learning to classify domains as DGA-generated.[14]