Automated benchmark network diversification for realistic attack simulation with application to moving target defense

Abstract

With numbers of exploitable vulnerabilities and attacks on networks constantly increasing, it is important to employ defensive techniques to protect one’s systems. A wide range of defenses are available and new paradigms such as Moving Target Defense (MTD) rise in popularity. But to make informed decisions on which defenses to implement, it is necessary to evaluate their effectiveness first. In many cases, the full impact these techniques have on security is not well understood yet. In this paper we propose network defense evaluation based on detailed attack simulation. Using a flexible modeling language, networks, attacks, and defenses are described in high detail, yielding a fine-grained scenario definition. Based on this, an automated instantiator generates a wide range of realistic benchmark networks. These serve to perform simulations, allowing to evaluate the security impact of different defenses, both quantitatively and qualitatively. A case study based on a mid-sized corporate network scenario and different Moving Target Defenses illustrates the usefulness of this approach. Results show that virtual machine migration, a frequently suggested MTD technique, more often degrades than improves security. Hence, we argue that evaluation based on realistic attack simulation is a qualified approach to examine and verify claims of newly proposed defense techniques.

Collaborative Optimization of Game Enemy Design and Network Security Defense Based on Deep Reinforcement Learning

“…Enemy design for new scenes or new gameplay requires a lot of manpower and material resources, and is often not flexible and intelligent enough. Therefore, game developers urgently need a new method to realize the intelligent behavior generation of enemies, so as to enhance the game experience and challenge [6][7].…”