Dear Colleagues,

Over the past decade, the rise of new technologies, such as the Internet of Things and associated interfaces, have dramatically increased the attack surface of consumers and critical infrastructure networks. New threats are being discovered on a daily basis making it harder for current solutions to cope with the large amount of data to analyse. Numerous machine learning algorithms have found their ways in the field of cyber-security in order to identify new and unknown malware, improve intrusion detection systems, enhance spam detection, or prevent software exploit to execute.

While these applications of machine learning algorithms have been proven beneficial for the cyber-security industry, they have also highlighted a number of shortcomings, such as the lack of datasets, the inability to learn from small datasets, the cost of the architecture, to name a few. On the other hand, new and emerging algorithms, such as Deep Learning, One-shot Learning, Continuous Learning and Generative Adversarial Networks, have been successfully applied to solve natural language processing, translation tasks, image classification and even deep face recognition. It is therefore crucial to apply these new methods to cyber-security and measure the success of these less-traditional algorithms when applied to cyber-security.

This Special Issue on machine learning for cyber-security is aimed at industrial and academic researcher applying non-traditional methods to solve cyber-security problems. The key areas of this Special Issue include, but are not limited to:

+ Generative Adversarial Models;
+ One-shot Learning;
+ Continuous Learning;
+ Challenges of Machine Learning for Cyber Security;
+ Strength and Shortcomings of Machine Learning for Cyber-Security;
+ Graph Representation Learning;
+ Scalable Machine Learning for Cyber Security;
+ Neural Graph Learning; Machine Learning Threat Intelligence;
+ Ethics of Machine Learning for Cyber Security Applications

Dr. Xavier Bellekens
Guest Editor

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