mccarthyajb/HL-NTAC

HL-NTAC is a specialized research project that functions more as a code-drop for an academic case study than a living software project. With only 6 stars and no activity in over 1,300 days, it lacks any community momentum or production-grade utility. The defensibility is near-zero as the approach (hierarchical learning for NIDS) is a well-documented academic pattern and the specific implementation is likely tied to older versions of TensorFlow/Keras. While the problem it solves—adversarial robustness in network security—is critical, this repository has been superseded by more modern frameworks like IBM's Adversarial Robustness Toolbox (ART) or newer graph-based neural networks for traffic analysis. Frontier labs pose low risk because this is a highly domain-specific application (Network Intrusion Detection) that falls outside their primary LLM/AGI focus. However, from a competitive standpoint, the project is effectively obsolete in the fast-moving field of AI-driven cybersecurity.