MLCS@ECML-PKDD 2023 : The 5th Workshop on Machine Learning for CyberSecurity (EXTENDED Deadline)
Call For Papers
MLCS2023@ECMLPKDD: The 5th Workshop on Machine Learning for Cybersecurity 2023 (September 22, 2023)
Co-located with the International Conference on European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD)
18 -- 22 September 2023
Cybersecurity is the practice of securing networks, systems and any other digital infrastructure from malicious attacks. Traditional cybersecurity relies on the static control of security cyberspace monitoring according to the pre-specified rules. However, this passive defence methodology is no longer useful in protecting cyberspace against new cybersecurity threats.
Nowadays, the capability to detect, analyse and defend against threats in (near) real-time conditions is not possible without employing sophisticated machine learning techniques and big data infrastructures. This gives rise to cyberthreat intelligence and analytic solutions to perceive, reason, learn and act against cyber adversary techniques and actions. On the other hand, it is important to consider that machine learning can be used also by attackers to continually improve their techniques and refine their offensive capabilities.
The aim of this workshop is to provide researchers with a forum to exchange and discuss scientific contributions and open challenges, both theoretical and practical, related to the use of machine learning in cybersecurity. We want to foster joint work and knowledge exchange between the cybersecurity community, and researchers and practitioners from the machine learning field and its crossing with big data, data science and visualization. The workshop shall provide a forum for discussing how machine learning today impacts cybersecurity, for better and for worse.
The workshop follows the success of the four previous editions (MLCS 2019- 2022) co-located with ECML-PKDD 2019-2022 - in all the previous editions the workshop gained strong interest, with attendance between 30 and 40 participants.
CALL FOR PAPERS
MLCS 2023 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.
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:
- Machine learning for:
--- the security and dependability of networks, systems, and software
---open-source threat intelligence and cybersecurity situational awareness
---data security and privacy
---cybersecurity forensic analysis
---the development of smarter security control
---the fight against (cyber)crime, e.g., biometrics, audio/image/video analytics
---the analysis of distributed ledgers
---malware, anomaly, and intrusion detection
- Vulnerability analysis:
--- the analysis of distributed ledgers
--- malware, anomaly, spam and intrusion 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 behaviour modelling and analysis
MLCS 2023 welcomes both research papers reporting results from mature work, and 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:
- Original, unpublished contributions with 12-16 pages including references.
- Already published or preliminary work of at most 6 pages.
Acceptance will be based on relevance, technical soundness, originality, and clarity of presentation.
Based on the quality and number of accepted regular papers, regular workshop papers (except papers reporting recently published work or preliminary work) will be “tentatively” published in the workshop post-proceedings.
At least one author of each accepted paper must have a full registration and be in Turin to present the paper (registration instructions at https://2023.ecmlpkdd.org/attending/registration/). Papers without a full registration or in-presence presentation won't be included in the post-workshop Springer proceedings.
All submissions should be made in PDF using the Microsoft CMT platform: at the link https://cmt3.research.microsoft.com/ and must adhere to the Springer LNCS style. (Templates are available at https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines?countryChanged=true)
To submit your paper, kindly refer to the instructions provided on the Microsoft Conference Management Tool (CMT) platform. You can access these instructions by visiting the following link: https://cmt3.research.microsoft.com/docs/help/author/author- submission-form.html. Once on the platform, utilize the filter option to search for the "European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - Workshop and Tutorial Track". Then, select "Machine Learning for Cybersecurity (MLCS 2023)" under the "Create new submission" tab. Alternatively, you may use this link to access the submission page directly
Based on the quality and number of accepted papers and the regular workshop papers (except papers reporting recently published work or preliminary work) will be “tentatively” published in the workshop post-proceedings.
Paper Submission Deadline: June 19, 2023 (extended)
Paper author notification: July 12, 2023
Camera Ready Submission: July 26, 2023
Workshop: September 22, 2023
Kind regards, the organizers:
Giuseppina Andresini, University of Bari Aldo Moro
Annalisa Appice, University of Bari Aldo Moro
Ibéria Medeiros, University of Lisbon
Pedro Ferreira, University of Lisbon