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Research DGA. Different malware families have used DGA, so find one particular instance of DGA and use the information provided by security researchers to diagram how it functioned. What defenses could be used to combat DGA? Create a two-page paper on your research. All assignments must be submitted in APA-7 format
The Domain Generation Algorithm (DGA) is a core technique used by advanced malware to ensure resilient and evasive communication with its Command and Control (C2) server. It works by programmatically generating hundreds or thousands of seemingly random domain names (e.g., axwscwsslmiagfah.com) on the fly, using a shared input like the current date, time, or a unique seed value. The attacker pre-registers just one or a few of these domains on a given day, knowing that the infected client (the "bot") will try them all until it finds the active C2 server. This method renders traditional security defenses like blacklisting ineffective because the rendezvous point is constantly changing, making it a "whack-a-mole" problem for defendersThe Domain Generation Algorithm (DGA) is a core technique used by advanced malware to ensure resilient and evasive communication with its Command and Control (C2) server. It works by programmatically generating hundreds or thousands of seemingly random domain names (e.g., axwscwsslmiagfah.com) on the fly, using a shared input like the current date, time, or a unique seed value. The attacker pre-registers just one or a few of these domains on a given day, knowing that the infected client (the "bot") will try them all until it finds the active C2 server. This method renders traditional security defenses like blacklisting ineffective because the rendezvous point is constantly changing, making it a "whack-a-mole" problem for defenders
Domain Generation Algorithms (DGAs) represent an evolutionary leap in malware design, transforming static, easily blocked communication channels into dynamic, highly resilient infrastructure. This paper examines the functionality of DGAs, focusing on the sophisticated Gameover Zeus (GOZ) botnet, and outlines a multi-layered defensive strategy utilizing predictive analytics and behavioral monitoring to combat this pervasive threat.
Gameover Zeus (GOZ) was a notorious peer-to-peer (P2P) variant of the Zeus banking Trojan, responsible for stealing over $100 million before its initial takedown in 2014. While its initial version relied on a P2P structure, later variants incorporated a DGA to rebuild and maintain its C2 infrastructure with heightened resilience. The GOZ DGA is a classic example of a time-dependent pseudo-random algorithm.
A. DGA Functionality
The GOZ DGA, in its new-generation form, was designed to generate a vast number of domains—up to 1,000 domains per week in some variants. Its functioning can be diagrammed as a sequence of deterministic steps:
Seed Generation: The algorithm begins with an initial seed derived from several inputs. Crucially, this often included the current system date (Year, Month, Day) and a hardcoded key unique to the malware version. This combination ensures that every infected host, regardless of location, generates the same list of domains on the same day.
Hashing and Input: The initial seed, along with a domain sequence number (an index from 1 to 1000), is often passed through a cryptographic hash function, such as MD5 or SHA-1. This hashing process creates a 128-bit or 160-bit output.