Machine Learning

Call for papers

Workshop Description

The most notable achievement for big data is the development of large-scale machine learning, such as deep learning, reinforcement learning, etc. Cybersecurity is becoming an increasingly important problem with the ubiquity of the Internet nowadays. It is intriguing and practically significant to establish the degree to which modern machine learning can contribute to cybersecurity. There are already several tasks in cyber systems, which can be resolved by conventional machine learning techniques such as optimal thresholding in identifying malicious attacks. Recently, the rapid adoption of deep learning shed new light on cybersecurity problems with novel problem formulations and novel weapons to existing problems, which include:
a) Adaptive Defense in Information Systems
b) End-to-end Cyber Defense
c) Smart Decision-making for Vulnerability Assessment
d) Sequential Planning for Next-Generation Intrusion Detection and Response

Key dates

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Submission Instructions

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Invited Speakers

Chengcui Zhang (University of Alabama at Birmingham)
Thamar Solorio (University of Houston)
Hongbo Liu (Indiana University-Purdue University)
Tongping Liu (University of Texas at San Antonio)

Organizing Committee

Bo Liu (Auburn University)
Ji Liu (University of Rochester)
Qi Wang (Northwestern Polytechnical University, China)
Anthony Skjellum (Auburn University)
Saad Biaz (Auburn University)
Tongping Liu (University of Texas at San Antonio)
Chongjie Zhang (Tsinghua University, China)

Note that the workshop presentations will run in a joint session “Data Mining in Cyber” with the workshop “Machine Learning in Cyber” focusing on theoretical machine learning aspects for cyber security.