Machine Learning for Cybersecurity Threats, Challenges, and Opportunities Ⅱ

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. 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:

• 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 Garcia Villalba
Prof. Dr. Rafael T. de Sousa Jr.
Dr. Robson de Oliveira Albuquerque
Dr. Ana Lucila Sandoval Orozco
Guest Editors

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