One of the cybersecurity challenges is which attacks are meaningful to my organization from the threat intelligence landscape. In this challenge, cybersecurity performers will be expected to provide, at a minimum, the following deliverables: one topic of negotiated deliverables specific to the proposed effort by the administrator, one white paper explaining the reason this topic was chosen, the social impact, the research field (e.g., exploratory technologies), and justifications for funding by a source (e.g., institution, venture capital, DoD). 

The proposed effort is a cybersecurity challenge to detect and identify potential threats to an organization. This organization is part of the critical infrastructure that supplies national services (e.g., energy, medical, financial). You or your team will find as many security alerts relevant to that sector of that industry. A review of the Open-Source Intelligence (OSINT) gathered by the performer and team will be documented in the APA7 format and saved in a spreadsheet in a CSV format. The number of documents gathered should be no less than 15 sources from the past four years and no more than 30 documents in total. These documents may include reports, experimental and simulated data sets, proposed architectures, protocols, software codes, publications, model data, metrics, validation data, and other associated documentation and results.

1. Report on any theory used (e.g., Game theory) and related algorithms, if any (e.g., Unified theory). 

2. Report on updated expectations for energy efficiency improvements, data handling capabilities of the proposed approach, and preliminary discussion of potential hardware implementation. In one use case, the author used generative AI to develop threat intelligence data that increased productivity by 51%. The author evaluated the alert metrics using a statistical algorithm and discovered a significant correlation between attacks and alerts. The instrument used for analysis was IBM SPSS.

3. Report on approach the approach taken that led to the author's conclusion. For example, the author used a quantitative method for the experiment and found that the initial training data of 8K was insignificant. In comparison, the results were significant in the 328K dataset. The author's conclusion was better OSINT resulted in better alerts.

Challenge Inspired: AIxCC (https://aicyberchallenge.com/ )