MLHat @ KDD 2020

MLHat: The First International Workshop on Deployable Machine Learning for Security Defense

August 24, 2020 — Virtual Space
co-located with 25TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining

News

Following KDD’s announcement, this workshop will be conducted fully virtual.

Workshop agenda & prerecorded talks are out!

Workshop only registration link ($50)

Authors acceptance notification is out!
Talk video due: July 24, 2020 (instructions)
Camera ready due: July 29, 2020 (instructions)

Keynote

David Wagner, UC Berkeley

David Wagner is Professor of Computer Science at the University of California at Berkeley, working in the area of computer security. His research has analyzed and contributed to the security of cellular networks, 802.11 wireless networks, electronic voting systems, and other widely deployed systems. He currently serves as a member of the federal committee responsible for drafting technical standards for electronic voting systems.

Keynote details

Call for Papers

Important Dates

  • Paper submission deadline: May 27 June 12, 2020, 23:59 pm Hawaii Time
  • Acceptance notification: June 22 July 3, 2020
  • Talk video due: July 24, 2020 (instructions)
  • Camera ready due: July 22 July 29, 2020 (instructions)
  • Workshop: August 24, 2020

Overview

In recent years, we have seen machine learning algorithms, particularly deep learning algorithms, revolutionizing many domains such as computer vision, speech, and natural language processing. In contrast, the impact of these new advances in machine learning is still fairly limited in the domain of security defense. While there is research progress in applying  machine learning for threat forensics, malware analysis, intrusion detection, and vulnerability discovery, there are still grand challenges to be addressed before a machine learning system can be deployed and operated in practice as a critical component of cyber defense. Major challenges include but not limited to the scale of the problem (billions of known attacks), adaptability (hundreds of millions of new attacks every year),  inference speed and efficiency (compute resource is constrained), adversarial attacks (highly motivated evasion and poisoning attacks), the urging demand for explainability (for threat investigation), and the need for integrating human (e.g., SOC analysts) in the loop. 

This workshop aims to bring together academic researchers and industry practitioners to discuss the open challenges, potential solutions, and best practices to deploy machine learning at scale for security defense. The goal is to define new machine learning paradigms under various security application contexts and identifying exciting new future research directions. At the same time, the workshop will also have a strong industry presence to provide insights into the challenges in deploying and maintaining machine learning models, and the much needed discussion on the capabilities that the state-of-the-arts failed to provide.

Topics of Interest

Topics of interest include (but not limited to):

  • Malware analysis, detection, classification, and attribution
  • Vulnerability discovery using machine learning
  • ML applications for cloud infrastructure and IoT security
  • Network attack detection, classification, and analysis
  • Spam, phishing, online scam detection
  • Malicious behaviors in online social networks
  • Sequence analysis for system/network events
  • Anomaly detection
  • Model verdict explainability in security applications
  • Privacy preserving security data collection and sharing
  • Robustness of machine learning models against adversarial attacks
  • Concept drift detection and explanation
  • Interactive machine learning for security
  • Few-shot learning for security applications
  • Resource constrained machine learning
  • Deep and shallow learning applications

Submission Guidelines

We welcome different types of papers, including:  

  • Novel research papers
  • Work-in-progress papers
  • Visionary and position papers
  • Papers that describe real-world security data sets
  • Relevant work that has been previously published.

We ask the authors to clearly specify the paper type in the abstract, to help reviewers assess the contributions. Submissions must be in PDF. The main content of the paper should be no more than 7 pages (with unlimited number of pages for appendices and references). The paper should be formatted based on the standard double-column ACM Sigconf Proceedings Style. Each submission will be single-blind reviewed by at least 3 PC members

Accepted papers will be archived as full papers in Springer Communications in Computer and Information Science (CCIS) and some will be presented orally during the workshop. At least one author of each accepted paper must attend the workshop to present the work in order for the paper to be archived.

Submission Site

https://cmt3.research.microsoft.com/DMLSD2020

Committee

Workshop Chairs

Gang Wang, UIUC, USA
Arridhana Ciptadi, Blue Hexagon, USA
Ali Ahmadzadeh, Blue Hexagon, USA

Program Committee

Sadia Afroz, Avast
Shang-Tse Chen, National Taiwan University
Yin Chen, Google
Neil Gong, Duke University
Zhou Li, UC Irvine
Shirin Nilizadeh, University of Texas at Arlington
B. Aditya Prakash, Georgia Tech
Gianluca Stringhini, Boston University
Ting Wang, Penn State University
Xinyu Xing, Penn State University