Expanding Horizons: The Evolving Landscape of Development Opportunities in Cybersecurity Training Platforms

Abstract

In today’s cybersecurity landscape, offensive security plays a vital role in fortifying systems by identifying vulnerabilities and potential attack vectors. Equally significant is the training of offensive security professionals. This study conducts a comprehensive comparative analysis of renowned offensive security training platforms: Hack The Box, TryHackMe, HackerOne, PicoCTF, and PortSwigger Academy. The goal is to evaluate these platforms across eight criteria, shedding light on their strengths and limitations, while also proposing potential enhancements to address existing gaps. The criteria encompass hints, ranking systems, flags, writeups, user feedback, knowledge domains, difficulty levels, and extensibility. By subjecting these platforms to this comprehensive evaluation, we gain invaluable insights into their individual advantages and areas necessitating improvement. A salient finding of the analysis is the absence of personalized learning pathways and adaptive training based on users’ unique skills and cognitive patterns. To mitigate this gap, prospective offensive security training platforms could leverage machine learning algorithms to create customized learning experiences. By adopting user activity-driven methodologies, these platforms can tailor training content, challenges, and feedback to meet learners’ distinct needs and skill levels. The outcomes of this study contribute to the advancement of offensive security training by outlining the features and attributes of a plausible future platform, grounded in the pivotal considerations necessary for the creation of a more comprehensive and efficient training ecosystem. By integrating personalized learning paths and harnessing the potential of machine learning, forthcoming platforms can provide tailored experiences that optimize learning outcomes and foster enhanced engagement.