PHY-layer location privacy-preserving access point selection mechanism in next-generation wireless networks

Farhang, Sadegh; Hayel, Yézékaël; Zhu, Quanyan

doi:10.1109/cns.2015.7346836

Cited by 27 publications

(9 citation statements)

References 17 publications

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“…On the physical layer degree, location safety can be spilled by way of using passage manner choice calculations in 5G bendy networks [24]. International mobile subscriber identity (IMSI) getting attacks can be applied to uncover the character of a supporter via getting the IMSI of the endorser's user equipment (UE).…”

Section: Privacy Challenges In 5gmentioning

confidence: 99%

5G Security and Privacy Issues: A Perspective View

Yesmin

Agasti

Chakrabarti

2021

Smart Innovation, Systems and Technologies

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Subsequent to tremendous prominence and development of cell innovation, 5G network is being considered as a focal area for future correspondence design. 5G will be supporting a great many existing gadgets without satisfactory inherent security, just as new gadgets whose outrageous processing force will make them appealing focuses for programmers. 5G will relate normal structures that will require greater security to undertake well-being of the basic foundation as well as security of the general public all in all. Here, a portion of the identified security problems of 5G networks is being discussed. The majority of the data depends on writing survey and reports distributed by unmistakable media transmission giants and different books part. Subsequently, this article edifies its per users on what is anticipated from 5G network and what are the fundamental security and protection issues in accomplishing those objectives.

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Section: Privacy Challenges In 5gmentioning

confidence: 99%

5G Security and Privacy Issues: A Perspective View

Yesmin

Agasti

Chakrabarti

2021

Smart Innovation, Systems and Technologies

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“…5G networks will be denser and of higher capacity than current 4G technology, using Massive MIMO technology [41]. Due to the high density of small cells, the knowledge of the cell, which is associated with a data subject, can easily reveal the location information of that subject [42]. Densification would bring out location privacy issues, affecting therefore further GDPR obligations and rights.…”

Section: B High Traffic Densitymentioning

confidence: 99%

GDPR Interference With Next Generation 5G and IoT Networks

Rizou

Alexandropoulou-Egyptiadou

Psannis

2020

IEEE Access

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This article examines the specific data protection framework with regards to 5G networks, which is the current high-end evolution of the previous four generations of cellular technology networks [1]. Taking into consideration practical issues, that have emerged from 5G (Fifth Generation) technology, the scope is the presentation of legal solutions. As this digital mobile transformation will begin from 2020, affecting applications of a wide range of services in energy sector, transport services, banking sector, health field, as well as in industrial control systems [2], and progressively in everyday life through all smart devices. Ιt is crucial to specialize and sum up the interference between European data protection legal framework and 5G networks, in order to provide a new path to the addressing issues.

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“…The information asymmetry can be either leveraged or created by the attacker or the defender for achieving a successful cyber deception. For example, techniques such as honeynets [56,22], moving target defense [6,14,3], obfuscation [57,58,59,60], and mix networks [61] have been introduced to create difficulties for attackers to map out the system information.…”

Section: Literaturementioning

confidence: 99%

Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

Huang

Zhu

2020

Adaptive Autonomous Secure Cyber Systems

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The increasing instances of advanced attacks call for a new defense paradigm that is active, autonomous, and adaptive, named as the '3A' defense paradigm. This chapter introduces three defense schemes that actively interact with attackers to increase the attack cost and gather threat information, i.e., defensive deception for detection and counter-deception, feedback-driven Moving Target Defense (MTD), and adaptive honeypot engagement. Due to the cyber deception, external noise, and the absent knowledge of the other players' behaviors and goals, these schemes possess three progressive levels of information restrictions, i.e., from the parameter uncertainty, the payoff uncertainty, to the environmental uncertainty. To estimate the unknown and reduce the uncertainty, we adopt three different strategic learning schemes that fit the associated information restrictions. All three learning schemes share the same feedback structure of sensation, estimation, and actions so that the most rewarding policies get reinforced and converge to the optimal ones in autonomous and adaptive fashions. This work aims to shed lights on proactive defense strategies, lay a solid foundation for strategic learning under incomplete information, and quantify the tradeoff between the security and costs.Recent instances of WannaCry ransomware, Petya cyberattack, and Stuxnet malware have demonstrated the trends of modern attacks and the corresponding new security challenges as follows.

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PHY-layer location privacy-preserving access point selection mechanism in next-generation wireless networks

Cited by 27 publications

References 17 publications

5G Security and Privacy Issues: A Perspective View

5G Security and Privacy Issues: A Perspective View

GDPR Interference With Next Generation 5G and IoT Networks

Strategic Learning for Active, Adaptive, and Autonomous Cyber Defense

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