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Centrality metrics have been studied in the network science research. They have been used in various networks, such as communication, social, biological, geographic, or contact networks under different disciplines. In particular, centrality metrics have been used in order to study and analyze targeted attack behaviors and investigated their effect on network resilience. Although a rich volume of centrality metrics has been developed from 1940s, only some centrality metrics (e.g., degree, betweenness, or cluster coefficient) have been commonly in use. This paper aims to introduce various existing centrality metrics and discusses their applicabilities in various networks. In addition, we conducted extensive simulation study in order to demonstrate and analyze the network resilience of targeted attacks using the surveyed centrality metrics under four real network topologies. We also discussed algorithmic complexity of centrality metrics surveyed in this work. Through the extensive experiments and discussions of the surveyed centrality metrics, we encourage their use in solving various computing and engineering problems in networks.

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Defensive deception is a promising approach for cyberdefense. Although defensive deception is increasingly popular in the research community, there hasn't been a systematic investigation of its key components, the underlying principles, and its tradeoffs in various problem settings. This survey paper focuses on defensive deception research centered on game theory and machine learning, since these are prominent families of artificial intelligence approaches that are widely employed in defensive deception. This paper brings forth insights, lessons, and limitations from prior work. It closes with an outline of some research directions to tackle major gaps in current defensive deception research.

Existing defensive deception (DD) approaches apply game theory, assuming that an attacker and defender play the same, full game with all possible strategies. However, in deceptive settings, players may have different beliefs about the game itself. Such structural uncertainty is not naturally handled in traditional game theory. In this work, we formulate an attackdefense hypergame where multiple advanced persistent threat (APT) attackers and a single defender play a repeated game with different perceptions. The hypergame model systematically evaluates how various DD strategies can defend proactively against APT attacks. We present an adaptive method to select an optimal defense strategy using hypergame theory for strategic defense as well as machine learning for adaptive defense. We conducted in-depth experiments to analyze the performance of the eight schemes including ours, baselines, and existing counterparts. We found the DD strategies showed their highest advantages when the hypergame and machine learning are considered in terms of reduced false positives and negatives of the NIDS, system lifetime, and players' perceived uncertainties and utilities. We also analyze the Hyper Nash Equilibrium of given hypergames and discuss the key findings and insights behind them.