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Domain generation algorithms (DGAs) can be categorized into three types:zero-knowledge, partial-knowledge, and full-knowledge. While prior researchmerely focused on zero-knowledge and full-knowledge types, we characterizetheir anti-detection ability and practicality and find that zero-knowledge DGAspresent low anti-detection ability against detectors, and full-knowledge DGAssuffer from low practicality due to the strong assumption that they are fullydetector-aware. Given these observations, we propose PKDGA, a partialknowledge-based domain generation algorithm with high anti-detection abilityand high practicality. PKDGA employs the reinforcement learning architecture,which makes it evolve automatically based only on the easily-observablefeedback from detectors. We evaluate PKDGA using a comprehensive set ofreal-world datasets, and the results demonstrate that it reduces the detectionperformance of existing detectors from 91.7% to 52.5%. We further apply PKDGAto the Mirai malware, and the evaluations show that the proposed method isquite lightweight and time-efficient.

PKDGA: A Partial Knowledge-based Domain Generation Algorithm for Botnets