Improving Interpretability for Cyber Vulnerability Assessment Using Focus and Context Visualizations
Risk scoring provides a simple and quantifiable metric for decision support in cyber security operations, including prioritizing how to address discovered software vulnerabilities. However, scoring systems are often opaque to operators, which makes scores difficult to interpret in the context of the...
Gespeichert in:
| Veröffentlicht in: | IEEE Symposium on Visualization for Cyber Security (VIZSEC) (Online) S. 30 - 39 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.10.2020
|
| Schlagworte: | |
| ISSN: | 2639-4332 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Risk scoring provides a simple and quantifiable metric for decision support in cyber security operations, including prioritizing how to address discovered software vulnerabilities. However, scoring systems are often opaque to operators, which makes scores difficult to interpret in the context of their own networks, each other, or in a broader threat landscape. This interpretability challenge is exacerbated by recent applications of artificial intelligence (AI) and machine learning (ML) for vulnerability assessment, where opaque machine reasoning can hinder domain experts' trust in the decision-support toolkit or the actionability of its outputs. In this paper, we address this challenge through a combination of visualizations and analytics that complement existing techniques for vulnerability assessment. We present a study toward designing more interpretable visual encodings for decision support for vulnerability assessment. In particular, we consider the problem of making datasets of known vulnerabilities more interpretable at multiple scales, inspired by focus and context principles from the information visualization design community. The first scale considers individually scored vulnerabilities by using an explainable AI (XAI) toolkit for an ML risk-scoring model and by developing new visualizations of CVSS score features. The second scale uses an embedding for vulnerability descriptions to cluster potentially similar vulnerabilities. We outline use cases for these tools and discuss opportunities for applying XAI concepts to cyber risk and vulnerability management. |
|---|---|
| AbstractList | Risk scoring provides a simple and quantifiable metric for decision support in cyber security operations, including prioritizing how to address discovered software vulnerabilities. However, scoring systems are often opaque to operators, which makes scores difficult to interpret in the context of their own networks, each other, or in a broader threat landscape. This interpretability challenge is exacerbated by recent applications of artificial intelligence (AI) and machine learning (ML) for vulnerability assessment, where opaque machine reasoning can hinder domain experts' trust in the decision-support toolkit or the actionability of its outputs. In this paper, we address this challenge through a combination of visualizations and analytics that complement existing techniques for vulnerability assessment. We present a study toward designing more interpretable visual encodings for decision support for vulnerability assessment. In particular, we consider the problem of making datasets of known vulnerabilities more interpretable at multiple scales, inspired by focus and context principles from the information visualization design community. The first scale considers individually scored vulnerabilities by using an explainable AI (XAI) toolkit for an ML risk-scoring model and by developing new visualizations of CVSS score features. The second scale uses an embedding for vulnerability descriptions to cluster potentially similar vulnerabilities. We outline use cases for these tools and discuss opportunities for applying XAI concepts to cyber risk and vulnerability management. |
| Author | Wollaber, Allan B. Gomez, Steven R. Alperin, Kenneth B. |
| Author_xml | – sequence: 1 givenname: Kenneth B. surname: Alperin fullname: Alperin, Kenneth B. email: Kenneth.Alperin@ll.mit.edu organization: Massachusetts Institute of Technology – sequence: 2 givenname: Allan B. surname: Wollaber fullname: Wollaber, Allan B. email: Allan.Wollaber@ll.mit.edu organization: Massachusetts Institute of Technology – sequence: 3 givenname: Steven R. surname: Gomez fullname: Gomez, Steven R. email: Steven.Gomez@ll.mit.edu organization: Massachusetts Institute of Technology |
| BookMark | eNo1T8FKAzEUjKJgW_sFguQHtuYlu01yLIvVguBBW7yVbPatRLbZkqRi-_VG1NMMw8wwMyYXfvBIyC2wGQDTdxt3ekFbZa5mnHE2Y4wBnJExSK5A8Tl_OycjPhe6KIXgV2Qa40f2CM5ETo1Iu9rtw_Dp_Dtd-YRhHzCZxvUuHWk3BFofGwx0c-g9hn99ESPGuEOf6Dr-JJeDPURqfEvrIZd8Jbpx8WB6dzLJDT5ek8vO9BGnfzgh6-X9a_1YPD0_rOrFU-HynFTwBlBBx7TtWqOkkZVGZmw-1yhlpAVhwXTATNUy3cpSm7nFDFLLhvOqEhNy89vrEHG7D25nwnGrRSlLEOIbJT9bdg |
| CODEN | IEEPAD |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/VizSec51108.2020.00011 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Statistics Computer Science |
| EISBN | 172818262X 9781728182629 |
| EISSN | 2639-4332 |
| EndPage | 39 |
| ExternalDocumentID | 9347413 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: United States Air Force funderid: 10.13039/100006831 |
| GroupedDBID | 6IE 6IF 6IL 6IN AAJGR AAWTH ABLEC ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK OCL RIE RIL |
| ID | FETCH-LOGICAL-i203t-2b1e81f09cfda87a759e0ac110b88a7c13c1af10a5d09d749a6ce749797b22553 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 14 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000657259100005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| IngestDate | Wed Aug 27 05:48:34 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i203t-2b1e81f09cfda87a759e0ac110b88a7c13c1af10a5d09d749a6ce749797b22553 |
| PageCount | 10 |
| ParticipantIDs | ieee_primary_9347413 |
| PublicationCentury | 2000 |
| PublicationDate | 2020-Oct. |
| PublicationDateYYYYMMDD | 2020-10-01 |
| PublicationDate_xml | – month: 10 year: 2020 text: 2020-Oct. |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE Symposium on Visualization for Cyber Security (VIZSEC) (Online) |
| PublicationTitleAbbrev | VIZSEC |
| PublicationYear | 2020 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| SSID | ssj0003203511 |
| Score | 2.2132366 |
| Snippet | Risk scoring provides a simple and quantifiable metric for decision support in cyber security operations, including prioritizing how to address discovered... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 30 |
| SubjectTerms | Computer crime Encoding Human-centered computing-Visualization-Visualization application domains-Information Visualization Human-centered computing-Visualization-Visualization systems and tools-Visualization toolkits Machine learning Security and privacy-Systems security-Vulnerability Management Statistics Task analysis Visualization |
| Title | Improving Interpretability for Cyber Vulnerability Assessment Using Focus and Context Visualizations |
| URI | https://ieeexplore.ieee.org/document/9347413 |
| WOSCitedRecordID | wos000657259100005&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09a8MwED2S0CFT2iSl32joWDeWZUfSWEJDpxBoG7IFWSdBoDglH6Xpr68ku06HLp0shI3ME-J0d-_dAdzKLBcqEeicHOXTjCKNpLQ6cu9bO0TrjCCGZhN8MhHzuZw24K7WwhhjAvnM3PthyOXjSu98qGwgWeoMIGtCk_NhqdWq4yksCTmxSgRMYzmYLb-ejc48z935gYmncMW-UdCvLirBiIw7_1v-GPoHNR6Z1nbmBBqm6ELnpx0DqU5nF9r-4ljWXe4B1tECcuAVBiLsnrh7Khntc_ftbPfmq05X8w91lU4SmARk7P5pQ1SBJBSx-tyS2XLjVZiVdrMPr-PHl9FTVHVUiJYOnm2U5NQIamOpLSrBFc-kiZV2EOVCKK4p01RZGqsMY4k8lWqojXtwyXN38DN2Cq1iVZgzIFxohowbpBRTilyoNEWhMonCO0XJOfQ8gov3smjGogLv4u_pS2j7LSpZclfQ2q535hqO9IeDbX0TdvobS_Ws0g |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEB1qFeyp2lb8NgePrt3sJk1yFLFUrKVgLb2VbJKFgmylH2L99SbZdevBi6eEsAthQpjMzHvzAK4FTbiMuLZBjnRlRk4CIVIV2O_TtKNT6wS1F5tggwGfTMSwAjclF8YY48Fn5tZNfS1fz9XapcraIibWAcY7sEsJicKcrVVmVOLIV8UKGjAORXs8-3oxijqku40EIwfiCp1U0C8dFe9GuvX_beAAWls-HhqWnuYQKiZrQP1HkAEV97MBNfd0zDsvN0GX-QK0RRZ6KOwG2Zcqut8k9t_x-s31nS7W78o-nchjCVDX7mmJZKaRb2P1uULj2dLxMAv2Zgteuw-j-15QaCoEM2ueVRAl2HCchkKlWnImGRUmlMqaKOFcMoVjhWWKQ0l1KDQjQnaUsQMTLLFXn8ZHUM3mmTkGxLiKdcyMxlgTrBmXhGguqdDchUXRCTSdBafveduMaWG807-Xr2C_N3ruT_uPg6czqLnjyjFz51BdLdbmAvbUhzXh4tKf-jdz4LAZ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=proceeding&rft.title=IEEE+Symposium+on+Visualization+for+Cyber+Security+%28VIZSEC%29+%28Online%29&rft.atitle=Improving+Interpretability+for+Cyber+Vulnerability+Assessment+Using+Focus+and+Context+Visualizations&rft.au=Alperin%2C+Kenneth+B.&rft.au=Wollaber%2C+Allan+B.&rft.au=Gomez%2C+Steven+R.&rft.date=2020-10-01&rft.pub=IEEE&rft.eissn=2639-4332&rft.spage=30&rft.epage=39&rft_id=info:doi/10.1109%2FVizSec51108.2020.00011&rft.externalDocID=9347413 |