A Trust-Based Tool for Detecting Potentially Damaging Users in Social Networks

Rittichier, Kaley J.; Kaur, Davinder; Uslu, Süleyman; Durresi, Arjan

doi:10.1007/978-3-030-84913-9_9

Cited by 9 publications

(3 citation statements)

References 24 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

Self Cite

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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

Self Cite

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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“…Ruan et al [24] proposed a measurement theory-based trust management framework to provide improved flexibility to context-dependent applications by supporting multiple formulations and a new metric: the confidence of trust. Applications of this framework include stock market prediction using Twitter data [25], trust management in environmental decision making [26][27][28], and the detection of crime [29], fake users [30], and damaging users [31]. These applications show the potential of utilizing a trust management framework to facilitate decision making in various fields by measuring and assessing trust.…”

Section: Introductionmentioning

confidence: 99%

Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

Uslu

Kaur

Durrësi

et al. 2022

Sensors

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Billions of Internet of Things (IoT) devices and sensors are expected to be supported by fifth-generation (5G) wireless cellular networks. This highly connected structure is predicted to attract different and unseen types of attacks on devices, sensors, and networks that require advanced mitigation strategies and the active monitoring of the system components. Therefore, a paradigm shift is needed, from traditional prevention and detection approaches toward resilience. This study proposes a trust-based defense framework to ensure resilient IoT services on 5G multi-access edge computing (MEC) systems. This defense framework is based on the trustability metric, which is an extension of the concept of reliability and measures how much a system can be trusted to keep a given level of performance under a specific successful attack vector. Furthermore, trustability is used as a trade-off with system cost to measure the net utility of the system. Systems using multiple sensors with different levels of redundancy were tested, and the framework was shown to measure the trustability of the entire system. Furthermore, different types of attacks were simulated on an edge cloud with multiple nodes, and the trustability was compared to the capabilities of dynamic node addition for the redundancy and removal of untrusted nodes. Finally, the defense framework measured the net utility of the service, comparing the two types of edge clouds with and without the node deactivation capability. Overall, the proposed defense framework based on trustability ensures a satisfactory level of resilience for IoT on 5G MEC systems, which serves as a trade-off with an accepted cost of redundant resources under various attacks.

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A Trust-Based Tool for Detecting Potentially Damaging Users in Social Networks

Cited by 9 publications

References 24 publications

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

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

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

Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

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