Titlebook: Decision and Game Theory for Security; 10th International C Tansu Alpcan,Yevgeniy Vorobeychik,Gy?rgy Dán Conference proceedings 2019 Spring

Euphonious

https://doi.org/10.1007/978-1-4471-0471-1result of this process gives the insurer an estimated risk on the insured, which then determines the contract terms. The post-screening mechanism involves at least two contract periods whereby the second-period premium is increased if a loss event occurs during the first period..Prior work shows tha

machination

救護(hù)車

強(qiáng)制性

跳動(dòng)

Choosing Protection: User Investments in Security Measures for Cyber Risk Management,e different measures. Participants tended to invest preferably in the IDS, irrespective of the benefits from this investment. They were able to identify the firewall and insurance conditions in which investments were beneficial, but they did not invest optimally in these measures. The results imply

consolidate

Conflagration

Realistic versus Rational Secret Sharing,g scenarios. In such circumstances the secret sharing scheme facilitates a power sharing agreement in the society. We also state that non-reconstruction may be beneficial for this society and give several examples.

傲慢物

Solving Cyber Alert Allocation Markov Games with Deep Reinforcement Learning,between sub-games. Due to the large sizes of the action and state spaces, we present a technique that uses deep neural networks in conjunction with Q-learning to derive near-optimal Nash strategies for both attacker and defender. We assess the effectiveness of these policies by comparing them to opt

樂器演奏者

Adaptive Honeypot Engagement Through Reinforcement Learning of Semi-Markov Decision Processes,ion. Meanwhile, the penetration probability is kept at a low level. The results show that the expected utility is robust against attackers of a large range of persistence and intelligence. Finally, we apply reinforcement learning to the SMDP to solve the .. Under a prudent choice of the learning rat

jabber