1 research outputs found
ESASCF: Expertise Extraction, Generalization and Reply Framework for an Optimized Automation of Network Security Compliance
The Cyber threats exposure has created worldwide pressure on organizations to
comply with cyber security standards and policies for protecting their digital
assets. Vulnerability assessment (VA) and Penetration Testing (PT) are widely
adopted Security Compliance (SC) methods to identify security gaps and
anticipate security breaches. In the computer networks context and despite the
use of autonomous tools and systems, security compliance remains highly
repetitive and resources consuming. In this paper, we proposed a novel method
to tackle the ever-growing problem of efficiency and effectiveness in network
infrastructures security auditing by formally introducing, designing, and
developing an Expert-System Automated Security Compliance Framework (ESASCF)
that enables industrial and open-source VA and PT tools and systems to extract,
process, store and re-use the expertise in a human-expert way to allow direct
application in similar scenarios or during the periodic re-testing. The
implemented model was then integrated within the ESASCF and tested on different
size networks and proved efficient in terms of time-efficiency and testing
effectiveness allowing ESASCF to take over autonomously the SC in Re-testing
and offloading Expert by automating repeated segments SC and thus enabling
Experts to prioritize important tasks in Ad-Hoc compliance tests. The obtained
results validate the performance enhancement notably by cutting the time
required for an expert to 50% in the context of typical corporate networks
first SC and 20% in re-testing, representing a significant cost-cutting. In
addition, the framework allows a long-term impact illustrated in the knowledge
extraction, generalization, and re-utilization, which enables better SC
confidence independent of the human expert skills, coverage, and wrong
decisions resulting in impactful false negatives