Quantum Algorithms for Trust-Based AI Applications

Kaur, Davinder; Uslu, Süleyman; Durresi, Arjan

doi:10.1007/978-3-031-35734-3_1

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…Numerous studies have explored trust modeling and its application in diverse scenarios. For instance, a trust framework proposed by Reference 11, grounded in measurement theory, finds applicability across various domains, including crime detection, 12 social networks, 13,14 the food-energy sector, [15][16][17][18][19][20] healthcare, 21,22 edge computing, [23][24][25][26][27] quantum computing, 28 and beyond. Recent work 21,[29][30][31] has highlighted trust's utility as an acceptance criterion for artificial intelligence algorithms.…”

Section: Role Of Trustmentioning

confidence: 99%

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

Kaur,

Uslu,

Durresi

et al. 2023

Computational Intelligence

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This study introduces a comprehensive framework designed for detecting and mitigating fake and potentially threatening user communities within 5G social networks. Leveraging geo‐location data, community trust dynamics, and AI‐driven community detection algorithms, this framework aims to pinpoint users posing potential harm. Including an artificial control model facilitates the selection of suitable community detection algorithms, coupled with a trust‐based strategy to effectively identify and filter potential attackers. A distinctive feature of this framework lies in its ability to consider attributes that prove challenging for malicious users to emulate, such as the established trust within the community, geographical location, and adaptability to diverse attack scenarios. To validate its efficacy, we illustrate the framework using synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.

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Section: Role Of Trustmentioning

confidence: 99%

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

Kaur,

Uslu,

Durresi

et al. 2023

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Numerous studies have explored trust modeling and its application in diverse scenarios. For instance, a trust framework proposed by 11 , grounded in measurement theory, finds applicability across various domains, including crime detection 12 , social networks 13,14 , the food-energy sector 15,16,17,18,19,20 , healthcare 21,22 , edge computing 23 , quantum computing 24 , and beyond. Recent work 21,25,26,27 has highlighted trust's utility as an acceptance criterion for artificial intelligence algorithms.…”

Section: Role Of Trustmentioning

confidence: 99%

AI Control for Trust-based Detection of Attackers in 5G Social Networks

Durresi,

Kaur,

Uslu

et al. 2023

Preprint

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This study presents a framework for detecting and mitigating fake and potentially attacking user communities within 5G social networks. This framework utilizes geo-location information, community trust within the network, and AI community detection algorithms to identify users that can cause harm. The framework incorporates an artificial control model to select appropriate community detection algorithms and employs a trust-based strategy to identify and filter out potential attackers. It adapts its approach by utilizing user and attack requirement data through the artificial conscience control model while considering the dynamics of community trust within the network. What sets this framework apart from other fake user detection mechanisms is its capacity to consider attributes challenging for malicious users to mimic. These attributes include the trust established within the community over time, the geographical location, and the framework’s adaptability to different attack scenarios. To validate its efficacy, we apply the framework to synthetic social network data, demonstrating its ability to distinguish potential malicious users from trustworthy ones.

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