Evolutionary based moving target cyber defense

Abstract

A Moving Target (MT) defense constantly changes a system's attack surface, in an attempt to limit the usefulness of the reconnaissance the attacker has collected. One approach to this defense strategy is to intermittently change a system's configuration. These changes must maintain functionality and security, while also being diverse. Finding suitable configuration changes that form a MT defense is challenging. There are potentially a large number of individual configurations' settings to consider, without a full understanding of the settings' interdependencies. Evolution-based algorithms, which formulate better solutions from good solutions, can be used to create a MT defense. New configurations are created based on the security of previous configurations and can be periodically implemented to change the system's attack surface. This approach not only has the ability to discover new, more secure configurations, but is also proactive and resilient since it can continually adapt to the current environment in a fashion similar to systems found in nature. This article presents and compares two genetic algorithms to create a MT defense. The primary difference between the two is based on their approaches to mutation. One mutates values, and the other modifies the domains from which values are chosen.

Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense

“…In particular, they considered the aging aspect of configurations to reflect vulnerability that can be introduced by an aged configuration. In addition, the authors enhanced their GAbased MTD to change computer configurations by changing mutation as well as using the feedback about system security status [84]. Zhuang et al [172] also used a GA to generate system configurations with high diversity for maximizing system security.…”

An evolutionary game for integrity attacks and defences for advanced metering infrastructure

“…Evolutionary game theory has also been successfully applied in the areas of Vehicular Ad hoc NETworks [30], moving target cyber defense [16], IoT service selection for balancing device energy consumption [22], trust cooperative stimulation model for large scale MANETs [37], trust strategy adjustment among nodes in wireless sensor networks [18], a co-evolutionary game theory using replicator dynamics with feedback-evolving games [39], effects of finite populations on evolutionary stable strategies [11], etc.…”

The New Progress of Motive Target Defense Technology

“…2) Shift machine configurations John et al proposed a machine configurations shifting method to obtain a more secure configuration [30,31]. This method firstly codes system configures into chromosomes by found component, executes genetic algorithm, produces offspring with a higher degree of security configuration, and sends the offspring population to achieve component, implementation component in a set of virtual machine to implement the offspring population, reserve grading rules and scan tool is used by assess components (such as Nessus) to evaluate on the safety of new groups.…”