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In recent years, deep learning gained proliferating popularity in thecybersecurity application domain, since when being compared to traditionalmachine learning, it usually involves less human effort, produces betterresults, and provides better generalizability. However, the imbalanced dataissue is very common in cybersecurity, which can substantially deteriorate theperformance of the deep learning models. This paper introduces a transferlearning based method to tackle the imbalanced data issue in cybersecurityusing Return-Oriented Programming (ROP) payload detection as a case study. Weachieved 0.033 average false positive rate, 0.9718 average F1 score and 0.9418average detection rate on 3 different target domain programs using 2 differentsource domain programs, with 0 benign training data samples in the targetdomain. The performance improvement compared to the baseline is a trade-offbetween false positive rate and detection rate. Using our approach, the numberof false positives is reduced by 23.20%, and as a trade-off, the number ofdetected malicious samples is reduced by 0.50%.

Tackling Imbalanced Data in Cybersecurity with Transfer Learning: A Case with ROP Payload Detection