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.
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 that 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.
The objective of this one day workshop is to attract research of novel methods, techniques in an emerging area of malware detection and analysis using Computational Intelligence methods. The contributions should be previously unpublished or substantially improved previous works (with at least 60% of new material). Authors of papers that fit these criteria are invited to submit their papers.
The following activities are planned as a part of the workshop:
(i) Panel Discussion "Reliability of Intelligent Malware Detection". It will be discussed how utilization computer vision-inspired techniques to evade machine learning by modifying corresponding features of malicious binaries to avoid malware detection;
(ii) Invited Talk on "Modern Malware Landscape" (TBD);
(iii) Poster Session: selected papers will be given an opportunity to present their posters, especially focusing on works where MSc/PhD student is a main author;
(iv) Hands-On practical demonstration of particular scenarios of Machine Learning can be used to identify malware.
May 2, 2019 (extended from Apr 12): Due date for full workshop paper submissions
May 22, 2019 (extended from May 10): Paper acceptance notification
June 14, 2019 (extended from Jun 3): Complete papers submission
Aug 10-12, 2019: Workshops and conference
Geir Olav Dyrkolbotn
Associate Professor email@example.com
PhD Candidate firstname.lastname@example.org
Assistant Professor email@example.com
Professor of Computer Science firstname.lastname@example.org
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)
The authors are invited to submit: full-length papers (12-15+ pages) and short papers (6-8+ pages) through the online submission system.
Authors should consult Springer's authors' guidelines and use their proceedings templates, either for LaTeX or for Word, for the preparation of their papers. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each paper, acting on behalf of all of the authors of that paper, must complete and sign a Consent-to-Publish form, through which the copyright for their paper is transferred to Springer.
All papers will undergoo peer-review before the acceptance decision will be made.
The authors of accepted papers must guarantee their presence at the workshop for the presentation. At least one author of each accepted paper must register for the conference.
All accepted papers will be published in workshop proceedings in "Communications in Computer and Information Science" (CCIS) (Springer, ISSN: 1865-0929)
The information can be found on the conference webpage.
Selected best papers will be offered an opportunity of publication in IJCAI 2019 conference main proceedings publication.
All inquiries regarding the workshop, please, forward to workshop chairs.