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In this paper, we investigate the importance of a defense system's learningrates to fight against the self-propagating class of malware such as worms andbots. To this end, we introduce a new propagation model based on theinteractions between an adversary (and its agents) who wishes to construct azombie army of a specific size, and a defender taking advantage of standardsecurity tools and technologies such as honeypots (HPs) and intrusion detectionand prevention systems (IDPSes) in the network environment. As time goes on,the defender can incrementally learn from the collected/observed attack samples(e.g., malware payloads), and therefore being able to generate attacksignatures. The generated signatures then are used for filtering next attacktraffic and thus containing the attacker's progress in its malware propagationmission. Using simulation and numerical analysis, we evaluate the efficacy ofsignature generation algorithms and in general any learning-based scheme inbringing an adversary's maneuvering in the environment to a halt as anadversarial containment strategy.

On the Convergence Rates of Learning-based Signature Generation Schemes to Contain Self-propagating Malware