Network security and privacy is an important application area, which has inspired a great deal of research in decision theory, statistics, and machine learning. Recent algorithmic and theoretical advances mean that is now possible to employ principled approaches for solving complex decision and estimation problems, such as those encountered in networked systems. Algorithms in the physical, network, and application layer, must take into account the possibility of malicious or honest-but-curious participants, as well as adapt to changing network conditions and system failures.

This Special Issue invites principled research papers on topics at the intersection of network security and privacy on the one hand, and decision theory, machine learning, and statistics on the other. We invite inter-disciplinary papers within this area. Sample topics include: secure multi-party computation, learning in games, network monitoring, intrusion detection and response, privacy-preserving learning and decision-making, differential privacy, and cryptography. However, all submissions must have a clear relevance, or application potential, to network security and privacy.