This dissertation introduces a comprehensive framework aimed at reshaping applied cybersecurity education to significantly ease the learning curve, at scale, through three synergistic innovations. These methods address the daunting educational barriers in cybersecurity, enabling learners at all levels to understand complex security concepts more easily. The first innovation, the PWN methodology, redefines the traditional Capture The Flag (CTF) model by offering a structured series of modularized, self-guided challenges. This approach helps simplify complex topics into manageable units, each building on the last, which allows students to progress at their own pace. Over five years and with over 400 systems security challenges developed, this method has effectively helped students evolve from beginners to masters of advanced security exploits. The second component is the DOJO platform, an open-source learning environment that uses containerization technology to provide a pre-configured, browser-based interface. This platform reduces the setup complexities associated with applied cybersecurity and has already given over 10,000 students immediate access to practical learning scenarios, from vulnerability discovery to advanced debugging, in a unified, user-friendly environment. Its seamless integration allows educators to quickly launch new challenges and resources, ensuring a continuous and dynamic educational experience. The third component, the SENSAI tutor, is an AI-driven tutoring system that leverages Large Language Models to offer personalized, intelligent support. Integrated with the PWN methodology and DOJO platform, SENSAI serves as an on-demand mentor, providing tailored advice and problem-solving assistance. It adapts to individual student needs, offering specific guidance and theoretical support to enhance understanding and retention of complex concepts. Together, these three components create a powerful, integrated educational strategy that not only equips students with vital cybersecurity skills but also deepens their understanding of digital vulnerabilities and the strategic thinking needed to mitigate them. This strategy prepares a new generation of cybersecurity professionals to navigate the ever-evolving threats of the digital world.

Reverse engineering is critical to reasoning about how a system behaves. While complete access to a system inherently allows for perfect analysis, partial access is inherently uncertain. This is the case foran individual agent in a distributed system. Inductive Reverse Engineering (IRE) enables analysis under

such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space.

Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to

more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system.

While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant.

IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques.