PixelGame: Infrared Small Target Segmentation as a Nash Equilibrium

Zhou, Heng; Tian, Chunna; Zhenxi, Zhang,; Li, Chengyang; Xie, Yongqiang; Li, Zhongbo

doi:10.1109/jstars.2022.3206062

Cited by 9 publications

(1 citation statement)

References 67 publications

(116 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…So, if both rivals reach optimal policies, they use their best response strategy against the opponent. This is a game in which the policies of the defender and the attacker are interlinked, with no player changing their strategy that (𝜇 𝑑 * ,𝑛 , 𝜇 𝒶 * ,𝑛 ) is a NE [25]. The main objective of a player during a MDP is to develop an optimum policy 𝜇 to maximize the expected discounted reward sum, as follows:…”

Section: Initiatementioning

confidence: 99%

Multi-Defender Strategic Filtering Against Multi Agent Cyber Epidemics on Multi-Environment Model for Smart Grid Protection

Bitirgen,

Başaran Filik

2023

E3S Web of Conf.

View full text Add to dashboard Cite

The growing cyber space with the developments in cyber network technologies in smart grid (SG) systems has necessitated questioning the reliability of networks and taking precautions against possible cyber threats. For this reason, defensive strategies and approaches against cyber attacks must be improved to sustain secure information flow of the network connections used in electricity generation, transmission, distribution, and consumption. This paper proposes a multi-agent multi environment deep reinforcement learning (MM-DRL) based defender response against cyber epidemics consisting coordinated cyber-attacks (multi-CAs) in the same time frame scheme to sustain security for SG networks. In this regard, the PMU-connected 123-bus system is integrated as a Markov game. MM-DRL approach is implemented for subenvironments of a typical SG system. Multi-CAs game aims to coordinate PMU signals across intersections to improve the network efficiency of a SG. DRL has been applied to data control recently and demonstrated promising performance where each data signal is regarded as an agent. Conversely, multi-CAs are self-renewing emerging causative agent of electricity theft, network disturbances, and data manipulation in SG systems characterized with wide characteristic diversity and rapid evolution. The game results show that the presented request response algorithm is able to minimize system attack damage and maintain protection duties when compared to a benchmark without request response. In addition, the performance of the MM-DRL approach compared to other developed methods is examined.

show abstract

Section: Initiatementioning

confidence: 99%