The Security and Privacy of Mobile-Edge Computing: An Artificial Intelligence Perspective

Wang, Cheng; Yuan, Zenghui; Zhou, Pan; Xu, Zichuan; Li, Ruixuan; Wu, Dapeng Oliver

doi:10.1109/jiot.2023.3304318

Cited by 8 publications

(2 citation statements)

References 141 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [17] introduced a cooperative defense framework for defending against DDoS attacks in mobile edge computing, which could adapt to traffic changes by automatically coordinating container-carrying defense resources among the edge nodes. Myneni et al [18] proposed a distributed deep defense framework by using edge computing approaches, which could detect and mitigate DDoS attacks near the data source; this defense framework could significantly reduce unnecessary bandwidth consumed by DDoS traffic going from edge network to edge network.…”

Section: Related Workmentioning

confidence: 99%

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Miao,

Li,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

Edge computing brings computation and storage resources to the edge of the mobile network to solve the problems of low latency and high real-time demand. However, edge computing is more vulnerable to malicious attacks due to its open and dynamic environments. In this article, we investigate security defense strategies in edge computing systems, focusing on scenarios with one attacker and multiple defenders to determine optimal defense strategies with minimal resource allocation. Firstly, we formulate the interactions between the defenders and the attackers as the mean-field Stackelberg game model, where the state and the objective functions of the defenders are coupled through the mean-field term, and are strongly influenced by the strategy of the attacker. Then, we analyze the local optimal strategies of the defenders given an arbitrary strategy of the attackers. We demonstrate the Nash equilibrium and the mean-field equilibrium for both the defenders and the attackers. Finally, simulation analysis will illustrate the dynamic evolution of the defense strategy of the defenders and the trajectory of the attackers based on the proposed Stackelberg game model.

show abstract

Section: Related Workmentioning

confidence: 99%

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Miao,

Li,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Privacy concerns arise as personal and sensitive data may be processed locally. Implementing strong encryption, secure communication protocols, and access controls becomes essential to safeguard data integrity and confidentiality [186]- [190]. Furthermore, privacy-preserving techniques must be integrated into edge analytics to ensure responsible handling of information, considering regulatory requirements and user expectations.…”

Section: Security and Privacy In Edge Computingmentioning

confidence: 99%

Tackling security and privacy challenges in the realm of big data analytics

Janet Ngesa

2023

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

As organizations increasingly harness the power of big data analytics to derive insights and drive decision-making, the paramount concerns of security and privacy have come to the forefront. This paper presents a comprehensive framework for addressing the multifaceted challenges of big data security and privacy. Drawing on a synthesis of cutting-edge technologies, encryption methods, and access control mechanisms, our approach aims to fortify the entire big data lifecycle. The paper delves into innovative strategies for secure data storage, transmission, and processing, ensuring that sensitive information is shielded from unauthorized access or malicious attacks. Additionally, the framework incorporates robust privacy-preserving techniques, including anonymization and differential privacy, to uphold individual confidentiality. Through a meticulous analysis of current trends, emerging threats, and regulatory landscapes, this paper not only provides theoretical insights but also practical guidelines for organizations seeking to navigate the intricate landscape of big data while safeguarding the integrity, security, and privacy of the vast datasets at their disposal.

show abstract

Dual-Q network deep reinforcement learning-based computation offloading method for industrial internet of things

Du,

Wu,

Gao

2024

J Supercomput

View full text Add to dashboard Cite

The Security and Privacy of Mobile-Edge Computing: An Artificial Intelligence Perspective

Cited by 8 publications

References 141 publications

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Mean-Field Stackelberg Game-Based Security Defense and Resource Optimization in Edge Computing

Tackling security and privacy challenges in the realm of big data analytics

Dual-Q network deep reinforcement learning-based computation offloading method for industrial internet of things

Contact Info

Product

Resources

About