2021 IEEE Symposium on Visualization for Cyber Security (VizSec)最新文献

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2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/vizsec53666.2021.00003

引用次数: 0

Decision Support for Sharing Data using Differential Privacy 基于差分隐私的数据共享决策支持

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/VizSec53666.2021.00008

Mark F. St. John, G. Denker, Peeter Laud, Karsten Martiny, A. Pankova, Dusko Pavlovic

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引用次数: 8

BUCEPHALUS: a BUsiness CEntric cybersecurity Platform for proActive anaLysis Using visual analyticS BUCEPHALUS:一个以业务为中心的网络安全平台，用于使用可视化分析进行主动分析

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/VizSec53666.2021.00007

M. Angelini, G. Blasilli, Silvia Bonomi, S. Lenti, A. Palleschi, G. Santucci, Emiliano De Paoli

引用次数: 1

Towards Visual Analytics Dashboards for Provenance-driven Static Application Security Testing 面向源驱动静态应用程序安全测试的可视化分析仪表板

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/VizSec53666.2021.00010

A. Schreiber, Tim Sonnekalb, L. Kurnatowski

引用次数: 3

Visual Decision-Support for Live Digital Forensics 实时数字取证的可视化决策支持

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/VizSec53666.2021.00012

Fabian Böhm, Ludwig Englbrecht, Sabrina Friedl, G. Pernul

{"title":"Visual Decision-Support for Live Digital Forensics","authors":"Fabian Böhm, Ludwig Englbrecht, Sabrina Friedl, G. Pernul","doi":"10.1109/VizSec53666.2021.00012","DOIUrl":"https://doi.org/10.1109/VizSec53666.2021.00012","url":null,"abstract":"Performing a live digital forensics investigation on a running system is challenging due to the time pressure under which decisions have to be made. Newly proliferating and frequently applied types of malware (e.g., fileless malware) increase the need to conduct digital forensic investigations in real-time. In the course of these investigations, forensic experts are confronted with a wide range of different forensic tools. The decision, which of those are suitable for the current situation, is often based on the cyber forensics experts’ experience. Currently, there is no reliable automated solution to support this decision-making. Therefore, we derive requirements for visually supporting the decision-making process for live forensic investigations and introduce a research prototype that provides visual guidance for cyber forensic experts during a live digital forensics investigation. Our prototype collects relevant core information for live digital forensics and provides visual representations for connections between occurring events, developments over time, and detailed information on specific events. To show the applicability of our approach, we analyze an exemplary use case using the prototype and demonstrate the support through our approach.","PeriodicalId":183519,"journal":{"name":"2021 IEEE Symposium on Visualization for Cyber Security (VizSec)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115356340","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

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2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/vizsec53666.2021.00001

引用次数: 0

User-Centered Design of Visualizations for Software Vulnerability Reports 以用户为中心的软件漏洞报告可视化设计

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/VizSec53666.2021.00013

S. L. Reynolds, Tobias Mertz, Steven Arzt, J. Kohlhammer

引用次数: 3

AI Total: Analyzing Security ML Models with Imperfect Data in Production AI Total:分析生产中不完美数据的安全ML模型

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-10-01 DOI: 10.1109/VizSec53666.2021.00006

Awalin Sopan, Konstantin Berlin

{"title":"AI Total: Analyzing Security ML Models with Imperfect Data in Production","authors":"Awalin Sopan, Konstantin Berlin","doi":"10.1109/VizSec53666.2021.00006","DOIUrl":"https://doi.org/10.1109/VizSec53666.2021.00006","url":null,"abstract":"Development of new machine learning models is typically done on manually curated data sets, making them unsuitable for evaluating the models’ performance during operations, where the evaluation needs to be performed automatically on incoming streams of new data. Unfortunately, pure reliance on a fully automatic pipeline for monitoring model performance makes it difficult to understand if any observed performance issues are due to model performance, pipeline issues, emerging data distribution biases, or some combination of the above. With this in mind, we developed a web-based visualization system that allows the users to quickly gather headline performance numbers while maintaining confidence that the underlying data pipeline is functioning properly. It also enables the users to immediately observe the root cause of an issue when something goes wrong. We introduce a novel way to analyze performance under data issues using a data coverage equalizer. We describe the various modifications and additional plots, filters, and drill-downs that we added on top of the standard evaluation metrics typically tracked in machine learning (ML) applications, and walk through some real world examples that proved valuable for introspecting our models.","PeriodicalId":183519,"journal":{"name":"2021 IEEE Symposium on Visualization for Cyber Security (VizSec)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125440501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Developing Visualisations to Enhance an Insider Threat Product: A Case Study 开发可视化以增强内部威胁产品:案例研究

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-09-17 DOI: 10.1109/VizSec53666.2021.00011

Martin Graham, R. Kukla, O. Mandrychenko, D. Hart, J. Kennedy

引用次数: 2

VulnEx: Exploring Open-Source Software Vulnerabilities in Large Development Organizations to Understand Risk Exposure VulnEx:探索大型开发组织中的开源软件漏洞以了解风险暴露

2021 IEEE Symposium on Visualization for Cyber Security (VizSec) Pub Date : 2021-08-13 DOI: 10.1109/VizSec53666.2021.00014

Frederik L. Dennig, E. Cakmak, H. Plate, D. Keim

引用次数: 1