[DFSci] Call For Papers: The 1st International Workshop on Human-oriented Intelligent Defence Against Malware Threats (HIDAMT)

[Andrii Shalaginov]

Dear Colleague,

(Apologies for multiple postings)

You are welcome to submit your contributions to the workshop on
Human-oriented Intelligent Defence Against Malware Threats, which will
be held as a part of the 28th International Joint Conference on
Artificial Intelligence (IJCAI) 2019. The conference will take place on
the 10-16th of August 2019 in Macao, China.

Workshop webpage: https://folk.ntnu.no/andriis/hidamt2019/

*** IMPORTANT DATES ***
Apr 12, 2019: Due date for full workshop paper submissions
May 10, 2019: Paper acceptance notification
Jun 3, 2019: Complete papers submission
Aug 10-12, 2019: Workshops and conference

*** INTRODUCTION ***
Recent cybersecurity incidents involving malware demonstrated how
serious the consequences can be for both individual users and large
organizations. McAfee report on malware threats shows that over four
quarters of 2017 there were identified 690 millions of malware samples,
which is an extreme number considering amount of manual work required to
process even a tiny fraction of those. Many malware analysis across
different security organizations spent hours trying to analyze and
understand functionality of malware. At the same time overwhelming
amount of malicious threats and malware forms cause considerable delays
from the time malware has been discovered to the time a corresponding
efficient signature was created. Moreover, the malware infection is no
longer limited to personal computers, but now also hits such components
as Internet of Things and Industrial Control Systems, which were
previously unaffected and the cybersecirty impact was underestimated.
>From before Machine Learning and Computational Intelligence have
demonstrated advantages of application in cybersecurity-related tasks.
In particular, many researchers have been employing such techniques to
mitigate obfuscation, polymorphous and encryption while building
intelligent malware detection mechanisms. Intelligent malware analysis
and detection is an emerging topic of cybersecurity that has to go in
line with advancement of malware developers and consistent presence of
zero-day attacks. Our focus is not only to build and effective Machine
Learning-based malware protection, but also comprise models that are to
be understood by human experts. Therefore, we believe that Machine
Learning-aided human-oriented approaches will ensure timely response to
malware threats. Moreover, those can serve as a stepping stone in faster
and more efficient analysis of novel malware as well as similarity-based
identification of adversarial attacks on Machine Learning.

*** PROPOSED TOPICS ***
Note that the topics are not limited to this proposed list.

1. Automated pre-processing phase
- Efficient features identification and construction
- Novel approaches for malware categorization
- Human-understandable characterization of malware
- Indicators of Compromise as successful identification tool
- Information Fusion and Open Threats Intelligence
2. Advanced computational methods
- Deep Learning models
- Similarity-based analysis to avoid evasion
- Big Data-oriented optimization of detection
- Hybrid Intelligence and Soft Computing
- Secure and robust models to avoid adversarial attacks
3. Combating malware in a wild
- End-point implementations
- Novel malware collection and sharing platforms
- Applications in Decision Support Systems
- Human reasoning in Machine Learning-aided malware detection
- Real-time defence and online learning
- Explainable rules derived from train Machine Learning models

*** PROGRAM CO-CHAIRS ***
Andrii Shalaginov, Norwegian University of Science and Technology
Geir Olav Dyrkolbotn, Center for Cyber and Information Security
Sergii Banin, Norwegian University of Science and Technology
Ali Dehghantanha, University of Guelph
Katrin Franke, Norwegian University of Science and Technology

*** PROGRAM COMMITTEE ***
Olaf M. Maennel (Tallinn University of Technology)
Asif Iqbal (KTH Royal Institute of Technology)
Oleksandr Semeniuta (Norwegian University of Science and Technology)
Mamoun Alazab (Charles Darwin University)
Vasileios Mavroeidis (University of Oslo)
Sreyasee Das Bhattacharjee (University of North Carolina at Charlotte)
Igor Kotsiuba (Pukhov Institute for modeling in Energy Engineering)
Mark Scanlon (University College Dublin)
Piotr Andrzej Kowalski (AGH University of Science and Technology)
Reza Parizi (Kennesaw State University)
Mohammad Hamoudeh (Manchester Metropolitan University)
Gregory Epiphaniou (University of Wolverhampton)
Bojan Kolosnjaji (Technical University of Munich)
Shih-Chieh Su (‎Microsoft)

-- 
Best regards / Med vennlig hilsen,
―-
Andrii SHALAGINOV, PhD
Postoctoral Researcher in Digital Forensics
Malware Analyst
IEEE Member

Norwegian University of Science and Technology
NTNU | http://www.ntnu.edu/employees/andrii.shalaginov