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.