On-location / Digital Conference

International Conference on Data Security using Machine Learning (ICDSML-27)

28th - 29th May 2027,Montreal, Canada

In Association With:

Call for Paper


Important Dates


Early Bird Registration

28th Apr 2027

Paper Submission Deadline

3rd May 2027

Registration Deadline

13th May 2027

Conference Date

28th - 29th May 2027

Conference Updates:

"Stay updated with Science Cite Conference news."

  • Early-Bird Registration Reminder:
    Early-bird registration for the Science Cite Conference in Montreal ends soon! Register Now!
  • Certificate of Presentation – Recognizing Your Contribution:
    Receive a Certificate of Presentation to recognize your participation in Montreal conference.
  • Peer Review Process:
    The peer review process will begin soon for Montreal conference.
  • Networking with Global Experts:
    Join global experts at our conference in Montreal.
  • Opportunity for Scopus-Indexed Journal Publication:
    Your research could be published in a Scopus-Indexed Journal. Submit Your Abstract
  • SDG-Inspired Conference Focus:
    Present your work aligned with Sustainable Development Goals.

Call For Papers

The ICDSML bridges the gap between academia and industry by promoting research with practical applications. It provides a platform for professionals and researchers to share insights that drive real-world impact.

The conference focuses on Data Security using Machine Learning, encouraging applied research, case studies, and industry-driven innovations.

Authors are invited to submit papers addressing, but not limited to, the following areas:

  • Machine learning for cybersecurity
  • Data encryption techniques using ML
  • Anomaly detection in network security
  • Threat intelligence using machine learning
  • Privacy-preserving machine learning methods
  • Fraud detection in financial systems
  • Security challenges in ML applications
  • Data integrity in machine learning
  • Machine learning for malware detection
  • Risk assessment using predictive analytics
  • Secure data sharing in ML systems
  • Adversarial machine learning techniques
  • Incident response using machine learning
  • Vulnerability assessment in software systems
  • Machine learning for identity verification
  • Security protocols for ML models
  • Future trends in data security
  • Machine learning for cloud security
  • Data breach detection strategies
  • Ethical considerations in data security

Assessment

Submissions will be evaluated based on applicability, innovation, and research contribution. Accepted papers will be presented and considered for publication in relevant journals and proceedings.

Registration

Complete your registration to participate in discussions that bridge academia and industry, and gain exposure to practical insights.

Publication

Selected papers will be considered for publication platforms that support academic and industry collaboration.

Indexed / Supported By

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Academic Institutions Whose Scholars Have Contributed

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