Coevolutionary modeling of cyber attack patterns and mitigations using public datasets

Abstract

The evolution of advanced persistent threats (APTs) spurs us to explore computational models of coevolutionary dynamics arising from efforts to secure cyber systems from them. In a first for evolutionary algorithms, we incorporate known threats and vulnerabilities into a stylized "competition" that pits cyber attack patterns against mitigations. Variations of attack patterns that are drawn from the public CAPEC catalog offering Common Attack Pattern Enumeration and Classifications. Mitigations take two forms: software updates or monitoring, and the software that is mitigated is identified by drawing from the public CVE dictionary of Common Vulnerabilities and Exposures. In another first, we quantify the outcome of a competition by incorporating the public Common Vulnerability Scoring System -CVSS. We align three abstract models of population-level dynamics where APTs interact with defenses with three competitive, coevolutionary algorithm variants that use the competition. A comparative study shows that the way a defensive population preferentially acts, e.g. shifting to mitigating recent attack patterns, results in different evolutionary outcomes, expressed as different dominant attack patterns and mitigations. CCS CONCEPTS• Computing methodologies → Multi-agent systems.