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dimva 2025 : Conference on Detection of Intrusions and Malware & Vulnerability Assessment

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Link: https://www.dimva.org/dimva2025/
 
When Jul 9, 2025 - Jul 11, 2015
Where Graz,Austria
Submission Deadline Dec 4, 2024
Notification Due Jan 22, 2025
Categories    security   machine learning
 

Call For Papers

General Information

The annual DIMVA conference serves as a premier forum for advancing the state of the art in the broader areas of intrusion detection, malware analysis, and vulnerability assessment. Each year, DIMVA brings together international experts from academia, industry, and government to present and discuss novel research in these areas. DIMVA is organized by the special interest group Security - Intrusion Detection and Response (SIDAR) of the German Informatics Society (GI). The conference proceedings will appear in the Springer Lecture Notes in Computer Science (LNCS) series.
Topics of Interest

DIMVA solicits submissions of high-quality, original scientific papers presenting novel research on malware analysis, intrusion detection, vulnerability assessment, and related systems security topics.

Topics of interest include, but are not limited to:
Intrusions

Novel approaches and domains
Insider detection
Prevention and response
Data leakage, exfiltration, and poisoning
Result correlation and cooperation
Evasion and other attacks
Potentials and limitation
Operational experiences
Privacy, legal, and social aspects
Targeted attacks
Analysis or detection of cryptocurrency heists

Malware

Automated analyses
Behavioral models
Prevention and containment
Classification
Lineage
Forensics and recovery
Underground economy
Vulnerabilities in malware
Financially targeted malware (e.g., ransomware, DeFi)

Vulnerability detection

Vulnerability prevention
Vulnerability analysis
Exploitation and defenses
Hardware vulnerabilities
Situational awareness
Active probing
Vulnerabilities in decentralized systems

Papers will be judged on novelty, significance, correctness, and clarity. We expect all papers to provide enough details to enable reproducibility of the experimental results. We encourage papers that bridge research in different communities. We also welcome experience papers that clearly articulate lessons learnt.

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