Training and certifications are critical to industry but often lack hands-on experience. This project is designed to bridge the gap from theory to practice. Project goals include: (i) developing hands-on laboratory exercises as a supplement for year-1 course material on ethical hacking; and (ii) training InfoSec professionals who require good understanding and proven capability in ethical hacking.
Project outcomes inclued: (i) lab modules corresponding to theoretical underpinnings for various core knowledge units of Ethical Hacking; (ii) separate instructor-led and student practice activities; (iii) solutions for both instructor-led and student practice lab activities; and (iv) assessment for measuring student learning outcomes for each module, with solutions.
Project Website: http://csis.gmu.edu/ehc/
To improve the security of complex networked systems and develop effective network hardening strategies, it is critical to consider the potential impact of zero-day vulnerabilities. Although known attack patterns can be easily modeled, handling zero-day vulnerabilities is inherently difficult due to their unpredictable nature. In fact, current approaches to network hardening only consider known vulnerabilities. As a consequence, the resulting hardening recommendations may be unrealistic and far from optimal. Additionally, most existing approaches assume that complete attack graphs have been generated, which may be unfeasible in practice for large networks. To overcome these limitations, we propose a goal-centric approach to network hardening that takes into account both known and zero-day vulnerabilities and only requires partial attack graphs. First, we propose to develop polynomial algorithms that, starting from a set of hardening goals, can build the partial attack graph that is relevant for the attacker to reach one of those goals. These algorithms must analyze known vulnerabilities and also hypothesize potential zero-day vulnerabilities. Second, leveraging our previous work on network hardening, we will develop polynomial algorithms that can identify sets of configuration changes that would prevent the attacker from reaching any of the goals considered.