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Weaknesses in computer systems such as faults, bugs and errors in thearchitecture, design or implementation of software provide vulnerabilities thatcan be exploited by attackers to compromise the security of a system. CommonWeakness Enumerations (CWE) are a hierarchically designed dictionary ofsoftware weaknesses that provide a means to understand software flaws,potential impact of their exploitation, and means to mitigate these flaws.Common Vulnerabilities and Exposures (CVE) are brief low-level descriptionsthat uniquely identify vulnerabilities in a specific product or protocol.Classifying or mapping of CVEs to CWEs provides a means to understand theimpact and mitigate the vulnerabilities. Since manual mapping of CVEs is not aviable option, automated approaches are desirable but challenging.  We present a novel Transformer-based learning framework (V2W-BERT) in thispaper. By using ideas from natural language processing, link prediction andtransfer learning, our method outperforms previous approaches not only for CWEinstances with abundant data to train, but also rare CWE classes with little orno data to train. Our approach also shows significant improvements in usinghistorical data to predict links for future instances of CVEs, and therefore,provides a viable approach for practical applications. Using data from MITREand National Vulnerability Database, we achieve up to 97% prediction accuracyfor randomly partitioned data and up to 94% prediction accuracy in temporallypartitioned data. We believe that our work will influence the design of bettermethods and training models, as well as applications to solve increasinglyharder problems in cybersecurity.

V2W-BERT: A Framework for Effective Hierarchical Multiclass Classification of Software Vulnerabilities