MaL2CSec 2019 : Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurity
MaL2CSec 2019 : Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurity

MaL2CSec 2019 : Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurity

Stockholm, Sweden
Event Date: June 20, 2019 - June 20, 2019
Submission Deadline: April 12, 2019
Notification of Acceptance: April 27, 2019
Camera Ready Version Due: May 12, 2019

Call for Papers

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MaL2CSec: Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurity

KTH Royal Institute of Technology
Stockholm, Sweden, June 20, 2019

Conference website
Submission link
Abstract registration deadline April 12, 2019 (March 31, 2019)
Submission deadline April 12, 2019 (March 31, 2019)

Description & Scope:

The Internet has become in the key piece of any business activity. The crime activity is not an exception. Some crimes previous to Internet, such as thefts and scams, have found in Internet the perfect tool for developing their activity. The Internet allows criminals hiding their real identity and the possibility to purchase specific tools for thieving sensitive data with a very low investment. Over the last years, Internet Crime (e-Crime) has changed its business model, becoming more professional. The more skilled criminals offer their services to other criminals with less IT skills. An example of this is the Malware sophistication, that is increasing more intelligent, versatile, available, and is affecting a broader range of targets and devices. Malware serves a multitude of malign purposes: From logging keystrokes for steal sensitive user data, to sophisticated and professional malware which can intercept and alter data or hijack the victim’s user session.
In consequence, Cybersecurity acquires major relevance for every organization. The right controls and procedures must be put in place to detect potential attacks and protect against them. However, the number of cyber-attacks will be always bigger than the number of people trying to protect against attacks. New threats are being discovered on a daily basis making it harder for current solutions to cope with the large amount of data to analyse. Machine learning systems can be trained to find attacks, which are similar to known attacks. This way we can extract hidden value (e.g. anomaly detection, pattern identification, predictions…) from the security-related data and to detect even the first intrusions of their kind and develop better security measures.
The sophistication of threats has also increased substantially. Sophisticated zero-day attacks may go undetected for months at a time. Attack patterns may be engineered to take place over extended periods of time, making them very difficult for traditional intrusion detection technologies to detect. Worse, new attack tools and strategies can now be developed using adversarial machine learning techniques, requiring rapid co-evolution of defenses that match the speed and sophistication of machine learning-based offensive techniques.

This workshop aims at providing a forum for people from academia and industry to communicate their latest results on theoretical advances, industrial case studies, that combines machine learning techniques such as reinforcement learning, adversarial machine learning, and deep learning to help detect, predict and solve e-crimes much faster rate. Research papers can be focused, also, on offensive and defensive applications of machine learning to security. Potential topics include, but are not limited to:

- Adversarial training and defensive distillation
- Attacks against machine learning
- Black-box attacks against machine learning
- Blockchain technologies
- Challenges of machine learning for cyber security
- Cyber Threats Intelligence
- Emails mining & Authorship Identification
- Ethics of machine learning for cyber security applications
- Events correlations
- Fraud Detection
- Generative adversarial models
- Graph representation learning
- Industrial systems
- IoT Forensics
- Machine learning forensics
- Machine learning threat intelligence
- Malware Analysis & Detection
- Malware detection
- Mobile Forensics
- Network Forensics Readiness
- Neural graph learning
- One-shot learning; continuous learning
- Scalable machine learning for cyber security
- Steganography and steganalysis based on machine learning techniques
- Strength and shortcomings of machine learning for cyber-security

This topic list is not meant to be exhaustive. Papers that are considered out of scope may be rejected without full review. We encourage submissions that are "far-reaching" and "risky."

Important Dates
All deadlines are Anywhere on Earth (AoE = UTC-12h) (

Research Papers Pre-registration of abstract: April 12, 2019 (March 31, 2019)
Submission deadline: April 12, 2019 (March 31, 2019)
Notification: April 27, 2019
Camera ready deadline: May 12, 2019
Workshop: June 20, 2019

Instructions for Paper Submissions
All submissions must be original work. Plagiarism (whether of others or self) will be grounds for rejection. The submitter must clearly document any overlap with previously published or simultaneously submitted papers from any of the authors. Failure to point out and explain overlap will be grounds for rejection. Simultaneous submission of the same paper to another venue with proceedings or a journal is not allowed and will be grounds for automatic rejection. Submitting multiple distinct papers is of course allowed. MaL2CSec 2019 includes an author response period, which gives authors the chance to comment on reviews their papers received. Papers may not be withdrawn between the start of the author response period and acceptance notification. Contact the program committee chairs if there are questions about this policy.

Anonymous Submission
Papers must be submitted in a form suitable for anonymous review: no author names or affiliations may appear on the title page, and papers should avoid revealing their identity in the text. When referring to your previous work, do so in the third person, as though it were written by someone else. Only blind the reference itself in the (unusual) case that a third-person reference is infeasible. Contact the program chairs if you have any questions. Papers that are not properly anonymized may be rejected without review.

Page Limit and Formatting
Papers must not exceed 10 pages total (including the references and appendices). Papers must be typeset in LaTeX in A4 format (not "US Letter") using the IEEE conference proceeding template with the appropriate options [LaTeX template, Template instructions, IEEE Template Repository]. Failure to adhere to the page limit and formatting requirements can be grounds for rejection.

Submissions must be in Portable Document Format (.pdf). Authors should pay special attention to unusual fonts, images, and figures that might create problems for reviewers. Your document should render correctly in Adobe Reader XI and when printed in black and white.

Conference Submission Server
Papers must be submitted using Easychair platform ( and submissions may be updated at any time until the deadline for submissions.


Luis Javier García Villalba, Universidad Complutense de Madrid, Spain
Julio César Hernández Castro, University of Kent, United Kingdom
Ana Lucila Sandoval Orozco, Universidad Complutense de Madrid, Spain

Program Committee
Claudia Jacy Barenco Abbas, University of Brasilia, Brazil
Michael Brengel, Center for Information Security (CISPA), Germany
Manuela Cabral, Policia Judiciaria - Ministerio da Justiça, Portugal
Michele Carminati, Politecnico di Milano, Italy
Andrés Caro, Uniersidad de Extremadura, Spain
Steven de Muler, Belgian Federal Police, Belgium
Robson de Oliveira Albuquerque, University of Brasilia, Brazil
Rafael Timoteo de Sousa Junior, University of Brasilia, Brazil
Jesús E. Díaz Verdejo, Universidad de Granada, Spain
Francesca Ferrero, Research Centre on Security and Crime (RISSC), Italy
Rachel Finn, Trilateral Research, UK
Luis Javier García, Universidad Complutense de Madrid, Spain
Alejandro González, Spanish National Police, Spain
Julio Hernandez-Castro, University of Kent, UK
Eli Hoyberghs, Belgian Federal Police, Belgium
Darren Hurley-Smith, University of Kent, UK
Sonia Jimenez, Spanish National Police, Spain
Holger Nitsch, College of the Bavarian Police - Germany
Mario Polino, Politecnico di Milano, Italy
Sarina Ronert, College of the Bavarian Police, Germany
Christian Rossow, Center for Information Security (CISPA), Germany
Ana Lucila Sandoval, Universidad Complutense de Madrid, Spain
Berta Santos, Policia Judiciaria - Ministerio da Justiça, Portugal
Tom Sloan, University of Kent, UK
Stefano Zanero, Politecnico di Milano, Italy

The Workshop on Machine Learning for Cybersecurity (MaL2CSec) will be co-located with the 4th IEEE European Symposium on Security and Privacy (EuroS&P 2019) and it will be held in Stockholm, Sweden on June 20, 2019. Please consult the Euro S&P website for more details. Our schedule will be made available closer to the date of the event.

If you have any questions please do not hesitate to contact our organizing committee: Luis Javier García Villalba ([email protected]), Julio Hernández-Castro ([email protected]) or Ana Lucila Sandoval Orozco ([email protected]).

Credits and Sources

[1] MaL2CSec 2019 : Workshop on Machine Learning for Cyber-Crime Investigation and Cybersecurity

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