[agents] 2nd Call for Chapter Contributions: Adversarial Machine Learning in Cyber-security (Springer AI)

[Prithviraj Dasgupta]

2nd CALL FOR CHAPTER CONTRIBUTIONS
Adversary Aware Learning Techniques and Trends in Cybersecurity
(Forthcoming book title in Springer – Artificial Intelligence)

Website: https://sites.google.com/view/alec-springer-book

AIMS AND SCOPE
Machine learning-based intelligent systems have experienced a massive 
growth over the past few years, and  are  close to becoming  ubiquitous  
in  the  technology  surrounding  our  daily lives.  Examples  of  such 
systems  are abundant - intelligent consumer  appliances such  as 
automated  home  security  systems, intelligent voice service-enabled 
software
assistants such as Alexa, online recommender systems for social media 
feeds and email spam filters, automated image and biometric data 
recognition software used for homeland  security  applications, 
automated  controllers  on  self-driving  vehicles, all  employ machine 
learning based algorithms for making decisions and taking actions. 
Machine  learning-based  systems  have  been  shown  to be  vulnerable  
to  security  attacks  from  malicious adversaries. The  vulnerability  
of  these  systems  is  further  aggravated  as it  is  non-trivial  to  
establish  the authenticity of data used to train the system, and even 
innocuous perturbations to the training data can be used to manipulate 
the system’s behavior  in unintended  ways.  As  machine  
learning-based  systems  become pervasive  in  our  society,  it  is 
essential  to  direct  research towards  issues  related  to  security,  
trust,  reliability  and robustness of such systems,  so that humans can 
use them in a safe and sustained manner.

The contents of the book will address the overarching need towards 
making automated, machine learning-based systems more robust and 
resilient against adversarial attacks. We invite chapter contributions 
that address current technology trends and solutions, open issues, 
critical challenges and hard problems, and surveys in the area of 
adversarial machine learning relevant to cyber-security.  Topics of 
interest include, but are not limited to the following:
*    Adversary-aware Machine Learning - Reinforcement Learning, Lifelong 
Learning, Deep Learning
*    Adversarial leaning for cybersecurity problems such as network 
intrusion detection, malware detection, Web spoofing, phishing, etc.
*    Generative Adversarial Networks
*    Adversary- aware Prediction, Forecasting and Decision Making Techniques
*    Game Theory and Game Playing to counter adversarial learning
*    Adversarial Issues and Techniques for Cyber-Physical Systems, 
Adversarial Robotics
*    Operations Research related to Adversarial Learning
*    Applications of Adversarial Learning
*    Security Threats and Vulnerabilities from Adversarial Learning
*    Human factors and adversarial learning with human-in-the-loop

IMPORTANT DATES
October 15, 2019 - Deadline for manuscript submissions
December 15, 2019 - Review notifications
December 31, 2019 – Revised manuscripts due
January 15, 2020 – Final accept/reject decisions
January 31, 2020 - Final manuscripts due from authors
Second quarter of 2020: Publication

SUBMISSION INSTRUCTIONS
1.    Manuscripts should be formatted using Springer style guidelines 
available at the Website
2.    Page length of submitted manuscripts should not exceed 20 pages 
including references.
3.    Manuscripts should  be submitted in pdf format via Easychair at 
https://easychair.org/conferences/?conf=alec19

EDITORS
Raj Dasgupta, Joseph Collins, Ranjeev Mittu
Distributed Intelligent Systems Section, Information Technology Division
U.S. Naval Research Laboratory, Washington D.C.
Contact: raj.dasgupta at nrl.navy.mil