short-paper

A Conceptual Replication on Predicting the Severity of Software Vulnerabilities

Authors:

Sefa Eren Sahin,

Ayse TosunAuthors Info & Claims

EASE '19: Proceedings of the 23rd International Conference on Evaluation and Assessment in Software Engineering

Pages 244 - 250

https://doi.org/10.1145/3319008.3319033

Published: 15 April 2019 Publication History

Abstract

Software vulnerabilities may lead to crucial security risks in software systems. Thus, prioritization of the vulnerabilities is an important task for security teams, and assessing how severe the vulnerabilities are would help teams during fixing and maintenance activities. We replicated a prior work which aims to predict the severity of software vulnerabilities by grouping vulnerabilities into different severity levels. We follow their approach on feature extraction using word embeddings, and on prediction model using Convolutional Neural Networks (CNNs). In addition, Long Short Term Memory (LSTM) and Extreme Gradient Boosting (XGBoost) models are used. We also extend the replicated work by aiming to predict severity scores rather than levels. We carried out two experiments for predicting severity levels and severity scores of 82,974 vulnerabilities. On predicting the severity levels, our LSTM and CNN models perform similarly with an F1 score of 0.756 F1 score and 0.752, respectively. On predicting the severity scores, LSTM, CNN and XGBoost models perform 16.14%, 17.03%, 18.91% MAPE values, respectively.

References

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Zhuobing Han, Xiaohong Li, Zhenchang Xing, Hongtao Liu, and Zhiyong Feng. 2017. Learning to Predict Severity of Software Vulnerability Using Only Vulnerability Description. In 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME). IEEE, Shanghai, 125--136.

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Digital Library

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Sefa Eren Sahin. 2019. Predict severity of software vulnerabilities using Deep Neural Networks and Gradient Boosting (XGBoost): erensahin/vulnerability-severity-predictor. https://github.com/erensahin/vulnerability-severity-predictor

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Georgios Spanos, Lefteris Angelis, and Dimitrios Toloudis. 2017. Assessment of Vulnerability Severity using Text Mining. ACM, 49.

Digital Library

Cited By

Dhawan NSharma RChattopadhyay SChoudhary AJain V(2024)Enhancing Green Bean Anthracnose Severity Detection via Integrated CNN-LSTM Models2023 4th International Conference on Intelligent Technologies (CONIT)10.1109/CONIT61985.2024.10626282(1-4)Online publication date: 21-Jun-2024
https://doi.org/10.1109/CONIT61985.2024.10626282
A MKota KBabu AS S(2024)CVE Severity Prediction From Vulnerability Description - A Deep Learning ApproachProcedia Computer Science10.1016/j.procs.2024.04.294235(3105-3117)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.04.294
Malhotra RVidushi (2024)Text mining based an automatic model for software vulnerability severity predictionInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02371-215:8(3706-3724)Online publication date: 31-May-2024
https://doi.org/10.1007/s13198-024-02371-2
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Index Terms

A Conceptual Replication on Predicting the Severity of Software Vulnerabilities
1. Security and privacy
  1. Software and application security
2. Software and its engineering
  1. Software creation and management
    1. Software post-development issues
      1. Software evolution

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cover image ACM Other conferences

EASE '19: Proceedings of the 23rd International Conference on Evaluation and Assessment in Software Engineering

April 2019

345 pages

ISBN:9781450371452

DOI:10.1145/3319008

Program Chairs:
Shaukat Ali,
Vahid Garousi

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 15 April 2019

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EASE '19

EASE '19: Evaluation and Assessment in Software Engineering

April 15 - 17, 2019

Copenhagen, Denmark

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EASE '19 Paper Acceptance Rate 20 of 73 submissions, 27%;

Overall Acceptance Rate 71 of 232 submissions, 31%

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Cited By

Dhawan NSharma RChattopadhyay SChoudhary AJain V(2024)Enhancing Green Bean Anthracnose Severity Detection via Integrated CNN-LSTM Models2023 4th International Conference on Intelligent Technologies (CONIT)10.1109/CONIT61985.2024.10626282(1-4)Online publication date: 21-Jun-2024
https://doi.org/10.1109/CONIT61985.2024.10626282
A MKota KBabu AS S(2024)CVE Severity Prediction From Vulnerability Description - A Deep Learning ApproachProcedia Computer Science10.1016/j.procs.2024.04.294235(3105-3117)Online publication date: 2024
https://doi.org/10.1016/j.procs.2024.04.294
Malhotra RVidushi (2024)Text mining based an automatic model for software vulnerability severity predictionInternational Journal of System Assurance Engineering and Management10.1007/s13198-024-02371-215:8(3706-3724)Online publication date: 31-May-2024
https://doi.org/10.1007/s13198-024-02371-2
Chen BZou WCai BMeng QLiu WLi PChen L(2024)An empirical study on the potential of word embedding techniques in bug report management tasksEmpirical Software Engineering10.1007/s10664-024-10510-329:5Online publication date: 25-Jul-2024
https://doi.org/10.1007/s10664-024-10510-3
Wang RXu SJi XTian YGong LWang K(2024)An extensive study of the effects of different deep learning models on code vulnerability detection in Python codeAutomated Software Engineering10.1007/s10515-024-00413-431:1Online publication date: 31-Jan-2024
https://doi.org/10.1007/s10515-024-00413-4
Malhotra RVidushi V(2023)Impact of Word Embedding Methods on Software Vulnerability Severity Prediction Models2023 13th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence56041.2023.10048868(293-297)Online publication date: 19-Jan-2023
https://doi.org/10.1109/Confluence56041.2023.10048868
Bassi DSingh H(2023)A Systematic Literature Review on Software Vulnerability Prediction ModelsIEEE Access10.1109/ACCESS.2023.331261311(110289-110311)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3312613
Sun XYe ZBo LWu XWei YZhang TLi B(2023)Automatic software vulnerability assessment by extracting vulnerability elementsJournal of Systems and Software10.1016/j.jss.2023.111790204:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.jss.2023.111790
ElGhondakly RMoussa SBadr N(2023)Service-oriented model-based fault prediction and localization for service compositions testing using deep learning techniquesApplied Soft Computing10.1016/j.asoc.2023.110430143:COnline publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1016/j.asoc.2023.110430
Feltus C(2022)Learning Algorithm Recommendation Framework for IS and CPS SecurityInternational Journal of Systems and Software Security and Protection10.4018/IJSSSP.29323613:1(1-23)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.4018/IJSSSP.293236
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