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.
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.
|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|
Topics of interests for the workshop include, but are not limited to:
All papers will be peer reviewed, single-blinded. We welcome many kinds of papers, such as (and not limited to):
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.