CFP : “MLCS 2024: 6th Workshop on MACHINE LEARNING FOR CYBERSECURITY” workshop event co-located with the ECML PKDD 2024 conference
September 9, 2024, Vilnius, Lithuania

http://mlcs.lasige.di.fc.ul.pt
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OVERVIEW
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Cybersecurity is of the utmost importance for computing systems.The ethics guidelines for trustworthy artificial intelligence authored by the European Commission’s Independent High Level Expert Group on Artificial Intelligence on April 2019 have highlighted that machine learning-based artificial intelligence developments in various fields, including cybersecurity, are improving the quality of our lives every day, that AI systems should be resilient to attacks and security, and that they should consider security-by-design principles. The capability to detect, analyze, and defend against threats in (near) real-time conditions is not possible without employing machine learning techniques and big data infrastructure.

The workshop shall provide a forum for discussing novel trends and achievements in machine learning and their role in the development of secure systems. The workshop shall contribute to identifying new application areas as well as open and future research problems related to the application of machine learning in the cybersecurity field.


CALL FOR PAPERS
===============

All topics related to the contribution of machine learning approaches to the security of organisations’ systems and data are welcome. These include, but are not limited to:

--ML for the security and dependability of networks, systems, and software
--ML for open-source threat intelligence and cybersecurity situational awareness
--ML for data security and privacy
--ML for cybersecurity forensic analysis
--ML for the development of smarter security control
--ML for the fight against (cyber)crime, e.g., biometrics, audio/image/video analytics
--ML for vulnerability analysis
--ML for the analysis of distributed ledgers
--ML for malware, anomaly, intrusion and spam detection
--Adversarial machine learning and the robustness of AI models against malicious actions
--Interpretability and Explainability of machine learning models in cybersecurity
--Privacy preserving machine learning
--Trusted machine learning
--Data-centric security
--Scalable/big data approaches for cybersecurity
--Deep learning for automated recognition of novel threats
--Graph representation learning in cybersecurity
--Continuous and one-shot learning
--Informed machine learning for cybersecurity
--User and entity behavior modeling and analysis

PAPER SUBMISSION
================

MLCS welcomes both research papers reporting results from mature work, recently published work, as well as more speculative papers describing new ideas or preliminary exploratory work. Papers reporting industry experiences and case studies will also be encouraged.

Submissions are accepted in two formats:
- Regular research papers with 12 to 16 pages including references. To be published in the proceedings, research papers must be original, not published previously, and not submitted concurrently elsewhere.
- Short research statements of at most 6 pages. Research statements aim at fostering discussion and collaboration. They may review research published previously or outline new emerging ideas.


All submissions should be made in PDF using the Microsoft CMT and must adhere to the Springer LNCS style.


At least one author of each accepted paper must have a full registration and be in Vilnius to present the paper. Papers without a full registration or in-presence presentation won't be included in the post-workshop Springer proceedings.

More info to submit your paper on https://mlcs.lasige.di.fc.ul.pt/


IMPORTANT DATES
===============

Paper Submission Deadline: June, 26 2024
Paper author notification: July, 15 2024
Camera Ready Submission: July, 26 2024

Submission link https://cmt3.research.microsoft.com/ECMLPKDDWorkshops2024/Track/16/Submission/Create

WEB PAGE
https://mlcs.lasige.di.fc.ul.pt/


Kind regards, the organizers:

Annalisa Appice, University of Bari Aldo Moro
Giuseppina Andresini, University of Bari Aldo Moro
Luca Demetrio, Univeristy of Genoa
Ibéria Medeiros, University of Lisbon
Pedro Ferreira, University of Lisbon