Risk Prediction for Imbalanced Data in Cyber Security : A Siamese Network-based Deep Learning Classification Framework
Degang Sun, Zhengrong Wu, Yan Wang, Qiujian Lv, Bo Hu · 2019
Risk prediction plays an important role in network security which can be used to predict riskiest parts and then proactive measures can be adopted to avoid potential damage. Most existing literature model risk prediction problems as binary classification problems by using machine learning methods. However, these traditional machine learning models have poor performance - tending to misclassify the risky ones into the category of risk-free - on risk prediction task when the datasets are imbalanced or small in size. In this paper, we propose a Siamese Network Classification Framework (SNCF) that can map the Siamese network to a classification based on the similarity to alleviate imbalance for risk prediction. Experimental results on imbalanced data in risk prediction verify that the deep learning-based classification architecture SNCF has better efficiency when compared with other algorithms.