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A real-time dynamic concept adaptive learning algorithm for exploitability prediction
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DOI：10.1016/j.neucom.2021.01.144.png)
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Abstract
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Exploitability prediction has become increasingly important in cybersecurity, as the number of disclosed software vulnerabilities and exploits are soaring. Recently, machine learning and deep learning algo-rithms, including Support Vector Machine (SVM), Decision Tree, deep Neural Networks and their ensem-ble models, have achieved great success in vulnerability evaluation and exploitability prediction. However, they make a strong assumption that the data distribution is static over time and therefore fail to consider the concept drift problems due to the evolving system behaviours. In this work, we propose a novel consecutive batch learning algorithm, called Real-time Dynamic Concept Adaptive Learning (RDCAL), to deal with the concept drift and dynamic class imbalance problems existing in exploitability prediction. Specifically, we develop a Class Rectification Strategy (CRS) to handle the 'actual drift' in sam-ple labels and a Balanced Window Strategy (BWS) to boost the minority class during real-time learning. Experimental results conducted on the real-world vulnerabilities collected between 1988 to 2020 show that the overall performance of classifiers, including Neural Networks, SVM, HoeffdingTree and Logistic Regression (LR), improves over 3% by adopting our proposed RDCAL algorithm. Furthermore, RDCAL achieves state-of-the-art performance on exploitability prediction compared with other concept drift algorithms. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Real-time learning
Concept drift
Class imbalance
Class rectification
Balanced window
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