The Learning and Mining for Cybersecurity (LEMINCS) workshop aims to boost the interest for security and privacy in data mining and machine learning, with specific interests in analyzing and detecting threats in cybersecurity domain, determining trustworthiness of data and results, to catching fake news, and pushing the envelope in fair and accountable mining methods. In other words, we aim to increase the data science footprint in the cyber security domain.

What's LEMINCS about?

Over the last decades we have become more and more interconnected with each other: our computers are constantly connected to the internet, we store our data in cloud services, and our normal household devices have become smarter and remotely accessible. An unfortunate by-product of these advances is both a significant increase in information leaks, privacy breaches, as well as malicious behaviour. This includes increase and industrialization of malware, more sophisticated targeted attacks of companies and persons, as well as, malicious behavior over social and peer-to-peer networks. Moreover, as the decision systems are becoming more and more datadriven, it is vital to avoid any algorithmic bias, as this may lead to undesired results, for example by making certain groups of people more vulnerable. While there has been great success stories in using data mining techniques in cyber security domain, such as, spam detection, the consensus of the cyber security experts is that more data science techniques are needed in order to detect, act upon, and prevent malicious behaviour, algorithmic bias, and preserve privacy.

The goal of LEMINCS is to increase data science footprint in cyber security domain. We are interested in novel methodology papers that have strong applications in security, privacy, as well as, successful applications of existing methodology. In addition to more traditional problem settings, such as malware analysis, we are also highly interested in developing topics such as adversarial machine learning, malicious behaviour in social network (e.g., spreading fake news), and assessing whether the developed algorithms are fair.

Important Dates

Submission Fri, May 3, 2019, 23:59 Hawaii Time
Notification Fri, May 31, 2019
Camera-ready Fri, July 19, 2019
Workshop Mon, August 5, 2019

Call for Papers

Topics of interests for the workshop include, but are not limited to:

  • Detection and analysis of malware and targeted attacks
  • Scalable mining algorithms for analyzing code, program behaviour, and log events
  • Quantification of reliability of input, robustness of results.
  • Advances in adversarial machine learning
  • Detection of doctored content (deep fakes, etc)
  • Privacy-preservation in data mining and machine learning
  • Formal definitions of, and methods to assert fairness in machine learning and data mining.
  • Outlier detection techniques
  • Malicious behaviour in peer-to-peer and social networks

Submission Information

All papers will be peer reviewed, single-blinded. We welcome many kinds of papers, such as (and not limited to):

  • Novel research papers
  • Demo papers
  • Work-in-progress papers
  • Visionary papers (white papers)
  • Appraisal papers of existing methods and tools (e.g., lessons learned)
  • Real world cybersecurity data sets
  • Relevant work that has been previously published
  • Work that will be presented at the main conference of KDD

Note that we especially encourage position papers, as well as data set submissions. Both are extremely important for the field as the cyber security field is changing at neck-breaking pace, and there is a significant shortage of modern data.

Authors should clearly indicate in their abstracts the kinds of submissions that the papers belong to, to help reviewers better understand their contributions. Submissions must be in PDF, written in English, no more than 10 pages long — shorter papers are welcome — and formatted according to the standard double-column ACM Sigconf Proceedings Style.

The accepted papers will be posted on the workshop website and will not appear in the KDD proceedings.

For accepted papers, at least one author must attend the workshop to present the work.

For paper submission, proceed to the LEMINCS 2019 submission website.


Jilles Vreeken
CISPA Helmholtz Center for Information Security
Nikolaj Tatti
University of Helsinki
Contact us at:
lemincs.kdd (at)

Sponsors, Supporters & Friends

Program Committee

Leman Akoglu (CMU, USA)
Idan Amit (Palo Alto Networks, USA)
Ignacio Arnaldo (U. Madrid)
Asia J. Biega (MSR Montreal, Canada)
Dotan Di Castro (Bosch-AI, Israel)
Duen Horng 'Polo' Chau (Georgia Tech, USA)
Li Chen (Intel, USA)
Shang-Tse Chen (Georgia Tech, USA)
Nilaksh Das (Georgia Tech, USA)
Mario Fritz (CISPA, Germany)
Kathrin Grosse (CISPA, Germany)
Antti Honkela (U. Helsinki, Finland)
Dmitry Komashinskiy (F-Secure, Finland)
B. Aditya Prakash (Virginia Tech, USA)
Naren Ramakrishnan (Virginia Tech, USA)
Manuel-Gomez Rodriguez (MPI-SWS, Germany)
Ash Shahi (Palo Alto Networks, USA)
Oleksii Starov (Palo Alto Networks, USA)
Antti Ukkonen (U. Helsinki, Finland)
Yang Zhang (CISPA, Germany)