Dear Colleagues,

Cybersecurity has become a major priority for every organization. The right controls and procedures must be put in place to detect potential attacks and protect against them. However, the number of cyber-attacks will be always bigger than the number of people trying to protect themselves against them. New threats are being discovered on a daily basis, making it harder for current solutions to cope with a large amount of data to analyze. Machine-learning systems can be trained to find attacks which are similar to known attacks. In this way, we can detect even the first intrusions of their kind and develop better security measures.

The sophistication of threats has also increased substantially. Sophisticated zero-day attacks may go undetected for months at a time. Attack patterns may be engineered to take place over extended periods of time, making them very difficult for traditional intrusion-detection technologies to detect. Even worse, new attack tools and strategies can now be developed using adversarial machine learning techniques, requiring a rapid co-evolution of defenses that matches the speed and sophistication of machine-learning-based offensive techniques. Based on this motivation, this Special Issue aims to provide a forum for people from academia and industry to communicate their latest results on theoretical advances and industrial case studies that combine machine learning techniques, such as reinforcement learning, adversarial machine learning, and deep learning with significant problems in cybersecurity. Research papers can be focused on offensive and defensive applications of machine learning to security. The potential topics of interest to this Special Issue are listed below. Submissions can contemplate original research, serious dataset collection and benchmarking, or critical surveys.

Potential topics include, but are not limited to, the following:

• Adversarial training and defensive distillation;
• Attacks against machine learning;
• Black-box attacks against machine learning;
• Challenges of machine learning for cyber-security;
• Ethics of machine learning for cyber-security applications;
• Generative adversarial models;
• Graph representation learning;
• Machine-learning forensics;
• Machine-learning threat intelligence;
• Malware detection;
• Neural graph learning;
• One-shot learning; continuous learning;
• Scalable machine learning for cyber security;
• Steganography and steganalysis based on machine-learning techniques;
• Strength and shortcomings of machine learning for cyber-security.

Prof. Dr. Luis Javier García Villalba
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2300 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.