01 Ago MLCS 2019 | Workshop on Machine Learning for Cybersecurity Co-located with ECMLPKDD 2019
Important Dates (Lisbon, GMT)
- Paper submission deadline: June 7, 2019, 11:59 PM
- Acceptance notification: July 19, 2019
- Camera ready submission: July 26, 2019
- Deadline for participation in competition: August 24, 2019
- Notification of competition results: August 31, 2019
- Workshop: September 20, 2019
The last decade has been a critical one regarding cybersecurity, with studies estimating the cost of cybercrime to be up to 0.8 percent of the global GDP. The capability to detect, analyse, and defend against threats in (near) real-time conditions is not possible without employing machine learning techniques and big data infrastructures.
This gives rise to cyberthreat intelligence and analytic solutions, such as (informed) machine learning on big data and open-source intelligence, to perceive, reason, learn, and act against cyber adversary techniques and actions.
Moreover, organisations’ security analysts have to manage and protect systems and deal with the privacy and security of all personal and institutional data under their control.
The aim of this workshop is to provide researchers with a forum to exchange and discuss scientific contributions, open challenges and recent achievements in machine learning and their role in the development of secure systems.
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;
- Vulnerability analysis;
- The analysis of distributed ledgers;
- Malware, anomaly, 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 behavior modeling and analysis.
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. However, it should be noticed that papers based on recently published work will not be considered for publication in the proceedings.
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 EasyChair platform and must adhere to the Springer LNCS style.
Templates are available here. All regular workshop papers (except papers reporting recently published work) will be published in the workshop proceedings. Research statements will be
published online in the workshop program page.
- Annalisa Appice, Università degli Studi di Bari, Italy
- Battista Biggio, Università degli Studi di Cagliari, Italy
- Donato Malerba, Università degli Studi di Bari, Italy
- Fabio Roli, Università degli Studi di Cagliari, Italy
- Ibéria Medeiros, Universidade de Lisboa, LASIGE, Potugal
- Michael Kamp, University of Bonn, Fraunhofer IAIS, Germany
- Pedro M. Ferreira, Universidade de Lisboa, LASIGE, Portugal
Invited Speaker TBA
Program Committee (confirmed members)
- Aikaterini Mitrokotsa, Chalmers University of Technology, Sweden
- Alysson Bessani, University of Lisbon – LASIGE, Portugal
- Cagatay Turkay, City University London, United Kingdom
- Gianluigi Folino, CNR-ICAR, Italy
- Giorgio Giacinto, University of Cagliary, Italy
- Ilir Gashi, CSR / City University London, United Kingdom
- Leonardo Aniello, University of Southampton, United Kingdom
- Luis Muñoz-González, Imperial College London, United Kingdom
- Marc Dacier, Eurecom, France
- Marco Vieira, University of Coimbra, Portugal
- Miguel Correia, University of Lisbon, Portugal
- Mihalis Nicolaou, The Cyprus Institute, Cyprus
- Pavel Laskov, University of Liechtenstein, Liechtenstein
- Rogério de Lemos, University of Kent, United Kingdom
- Sara Madeira, University of Lisbon, Portugal
- Tommaso Zoppi, University of Florence, Italy
- Vasileios Mavroeidis, University of Oslo, Norway
- V.S. Subrahmanian, Dartmouth College, USA
Web page: http://mlcs.lasige.di.fc.ul.pt