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Peer review is the method employed by the scientific community for evaluatingresearch advancements. In the field of cybersecurity, the practice ofdouble-blind peer review is the de-facto standard. This paper touches on theholy grail of peer reviewing and aims to shed light on the performance of AI inreviewing for academic security conferences. Specifically, we investigate thepredictability of reviewing outcomes by comparing the results obtained fromhuman reviewers and machine-learning models. To facilitate our study, weconstruct a comprehensive dataset by collecting thousands of papers fromrenowned computer science conferences and the arXiv preprint website. Based onthe collected data, we evaluate the prediction capabilities of ChatGPT and atwo-stage classification approach based on the Doc2Vec model with variousclassifiers. Our experimental evaluation of review outcome prediction using theDoc2Vec-based approach performs significantly better than the ChatGPT andachieves an accuracy of over 90%. While analyzing the experimental results, weidentify the potential advantages and limitations of the tested ML models. Weexplore areas within the paper-reviewing process that can benefit fromautomated support approaches, while also recognizing the irreplaceable role ofhuman intellect in certain aspects that cannot be matched by state-of-the-artAI techniques.

Unveiling the Sentinels: Assessing AI Performance in Cybersecurity Peer Review