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Detection of Intrusions and Malware, and Vulnerability Assessment

17th International Conference, DIMVA 2020, Lisbon, Portugal, June 24–26, 2020, Proceedings

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Paperback, 500 blz. | Engels
Springer International Publishing | e druk, 2020
ISBN13: 9783030526825
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Springer International Publishing e druk, 2020 9783030526825
Onderdeel van serie Lecture Notes in Computer Science
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Samenvatting

This book constitutes the proceedings of the 17th International Conference on Detection of Intrusions and Malware, and Vulnerability Assessment, DIMVA 2020, held in Lisbon, Portugal, in June 2020.*

The 13 full papers presented in this volume were carefully reviewed and selected from 45 submissions. The contributions were organized in topical sections named: vulnerability discovery and analysis; attacks; web security; and detection and containment.

​*The conference was held virtually due to the COVID-19 pandemic.

Specificaties

ISBN13:9783030526825
Taal:Engels
Bindwijze:paperback
Aantal pagina's:500
Uitgever:Springer International Publishing

Inhoudsopgave

<p>Vulnerability Discovery and Analysis.- Automated CPE labeling of CVE summaries with Machine Learning.- Backstabber's Knife Collection: A Review of Open Source Software Supply Chain Attacks.- Putting Attacks in Context: A Building Automation Testbed for Impact Assessment from the Victim’s Perspective.- Attacks.- Fast and Furious: Outrunning Windows Kernel Notification Routines from User-Mode.- HAEPG: An Automatic Multi-hop Exploitation Generation Framework.- Understanding Android VoIP Security: A System-level Vulnerability Assessment.- Web Security.- Web Runner 2049: Evaluating Third-Party Anti-bot Services.- Short Paper - Taming The Shape Shifter: Detecting Anti-fingerprinting Browsers.- It Never Rains but It Pours: Analyzing and Detecting Fake Removal Information Advertisement Sites.- On the Security of Application Installers & Online Software Repositories.- Detection and Containment.- Distributed Heterogeneous N-Variant Execution.- Sec2graph: Network Attack Detection based on Novelty Detection on Graph Structured Data.- Efficient Context-Sensitive CFI Enforcement through a Hardware Monitor.</p>

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