Nations, corporations, and individuals constantly need to reason about how to protect their sensitive assets in order to ensure economic growth and prosperity. Decision making for security and privacy of infrastructure and information needs a scientific framework that can handle challenges arising from modern-day heterogeneous, dynamic, and large-scale systems.

GameSec solicits theoretical and practical contributions towards a science of decision making in security. In particular, GameSec publishes papers that apply decision and game theory, as well as related techniques such as dynamic control and mechanism design, to build resilient, secure, and dependable networked systems.

Conference Topics

The goal of GameSec is to bring together academic and indus- trial researchers in an effort to identify and discuss the major technical challenges and recent results that highlight the connection between game theory, control, distributed optimization, economic incentives and real world security, reputation, trust and privacy problems in a variety of technological systems. Submissions should solely be original research papers that have neither been published nor submitted for publication elsewhere.

-Game theory and mechanism design for security and privacy
-Pricing and economic incentives for building dependable and secure systems
-Dynamic control, learning, and optimization and approximation techniques
-Decision making and decision theory for cybersecurity and security requirements engineering
-Socio-technological and behavioral approaches to security
-Risk assessment and risk management
-Security investment and cyber insurance
-Security and privacy for the Internet-of-Things (IoT), cyber-physical systems, resilient control systems
-New approaches for security and privacy in cloud computing and for critical infrastructure
-Security and privacy of wireless and mobile communications, including user location privacy
-Game theory for intrusion detection
-Empirical and experimental studies with game-theoretic or optimization analysis for security and privacy

Special Track on "Data-Centric Models and Approaches"

In cyber and physical security and privacy applications, data plays an important role and presents fundamental challenges. In some domains, it is difficult to gather a large amount of data, and the data available may suffer from severe class imbalance, high noise, and numerous missing entries. In other domains, when multiple agents are involved, how the data presented to the agents impacts their decision making is under-explored. It can be challenging to incorporate data of the available form into the game-theoretic and decision-theoretic models for these domains, since many current approaches apply to precisely defined models and how to define models using the available data is unclear in many cases. In addition to the data-related challenges in cyber and physical security domains, the use of data in many domains leads to security and privacy concerns, and game-theoretic and decision-theoretic models can be designed for addressing such concerns. This special track invites submissions on various data-centric models and approaches, including work on empirical game theory; adversarial machine learning; data collection through crowdsourcing; synthetic data generation; applications of machine learning methods; novel techniques for handling real-world data and evaluating models using data.