Enabling a Zero Trust Architecture in a 5G-enabled Smart Grid
- URL: http://arxiv.org/abs/2210.01739v2
- Date: Fri, 21 Oct 2022 06:03:41 GMT
- Title: Enabling a Zero Trust Architecture in a 5G-enabled Smart Grid
- Authors: Mohammad Ali Alipour, Saeid Ghasemshirazi, Ghazaleh Shirvani
- Abstract summary: A smart grid (SG) requires a prompt and dependable connection to provide real-time monitoring through the IoT.
5G could be considered a catalyst for upgrading the existing power grid systems.
This article analyzes the Zero Trust (ZT) architecture specific to the power system of IoT and uses that knowledge to develop a security protection architecture.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: One of the most promising applications of the IoT is the Smart Grid (SG).
Integrating SG's data communications network into the power grid allows
gathering and analyzing information from power lines, distribution power
stations, and end users. A smart grid (SG) requires a prompt and dependable
connection to provide real-time monitoring through the IoT. Hence 5G could be
considered a catalyst for upgrading the existing power grid systems.
Nonetheless, the additional attack surface of information infrastructure has
been brought about by the widespread adoption of ubiquitous connectivity in 5G,
to which the typical information security system in the smart grid cannot
respond promptly. Therefore, guaranteeing the Privacy and Security of a network
in a threatening, ever-changing environment requires groundbreaking
architectures that go well beyond the limitations of traditional, static
security measures. With "Continuous Identity Authentication and Dynamic Access
Control" as its foundation, this article analyzes the Zero Trust (ZT)
architecture specific to the power system of IoT and uses that knowledge to
develop a security protection architecture.
Related papers
- Autonomous Adaptive Security Framework for 5G-Enabled IoT [0.8738214980779235]
5G can provide more rapid connection speeds, lower latency, faster downloads, and capability to connect more devices.
5G-enabled IoT networks increase systems vulnerabilities to security threats due to these dynamics.
This task specifies new adaptive strategies of security intelligence with associated scenarios to meet the challenges of 5G-IoT characteristics.
arXiv Detail & Related papers (2024-06-04T13:17:04Z) - Establishing Trust in the Beyond-5G Core Network using Trusted Execution Environments [4.235733335401408]
We review the security implications introduced in B5G networks, and the security mechanisms that are supported by the 5G standard.
We propose a vertical extension of Zero Trust, namely, Zero Trust Execution, to model untrusted execution environments.
We provide an analysis on how to establish trust in Beyond-5G network architectures using Trusted Execution Environments.
arXiv Detail & Related papers (2024-05-20T17:02:18Z) - GAN-GRID: A Novel Generative Attack on Smart Grid Stability Prediction [53.2306792009435]
We propose GAN-GRID a novel adversarial attack targeting the stability prediction system of a smart grid tailored to real-world constraints.
Our findings reveal that an adversary armed solely with the stability model's output, devoid of data or model knowledge, can craft data classified as stable with an Attack Success Rate (ASR) of 0.99.
arXiv Detail & Related papers (2024-05-20T14:43:46Z) - Penetration Testing of 5G Core Network Web Technologies [53.89039878885825]
We present the first security assessment of the 5G core from a web security perspective.
We use the STRIDE threat modeling approach to define a complete list of possible threat vectors and associated attacks.
Our analysis shows that all these cores are vulnerable to at least two of our identified attack vectors.
arXiv Detail & Related papers (2024-03-04T09:27:11Z) - Generative AI for Secure Physical Layer Communications: A Survey [80.0638227807621]
Generative Artificial Intelligence (GAI) stands at the forefront of AI innovation, demonstrating rapid advancement and unparalleled proficiency in generating diverse content.
In this paper, we offer an extensive survey on the various applications of GAI in enhancing security within the physical layer of communication networks.
We delve into the roles of GAI in addressing challenges of physical layer security, focusing on communication confidentiality, authentication, availability, resilience, and integrity.
arXiv Detail & Related papers (2024-02-21T06:22:41Z) - FedDiSC: A Computation-efficient Federated Learning Framework for Power
Systems Disturbance and Cyber Attack Discrimination [1.0621485365427565]
This paper proposes a novel Federated Learning-based privacy-preserving and communication-efficient attack detection framework, known as FedDiSC.
We put forward a representation learning-based Deep Auto-Encoder network to accurately detect power system and cybersecurity anomalies.
To adapt our proposed framework to the timeliness of real-world cyberattack detection in SGs, we leverage the use of a gradient privacy-preserving quantization scheme known as DP-SIGNSGD.
arXiv Detail & Related papers (2023-04-07T13:43:57Z) - Artificial Intelligence Empowered Multiple Access for Ultra Reliable and
Low Latency THz Wireless Networks [76.89730672544216]
Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era.
To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required.
This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
arXiv Detail & Related papers (2022-08-17T03:00:24Z) - Intelligent Zero Trust Architecture for 5G/6G Tactical Networks:
Principles, Challenges, and the Role of Machine Learning [4.314956204483074]
We highlight the challenges and introduce the concept of an intelligent zero trust architecture (i-ZTA) as a security framework in 5G/6G networks with untrusted components.
This paper presents the architectural design of an i-ZTA upon which modern artificial intelligence (AI) algorithms can be developed to provide information security in untrusted networks.
arXiv Detail & Related papers (2021-05-04T13:14:29Z) - Towards Self-learning Edge Intelligence in 6G [143.1821636135413]
Edge intelligence, also called edge-native artificial intelligence (AI), is an emerging technological framework focusing on seamless integration of AI, communication networks, and mobile edge computing.
In this article, we identify the key requirements and challenges of edge-native AI in 6G.
arXiv Detail & Related papers (2020-10-01T02:16:40Z) - Smart Home, security concerns of IoT [91.3755431537592]
The IoT (Internet of Things) has become widely popular in the domestic environments.
People are renewing their homes into smart homes; however, the privacy concerns of owning many Internet connected devices with always-on environmental sensors remain insufficiently addressed.
Default and weak passwords, cheap materials and hardware, and unencrypted communication are identified as the principal threats and vulnerabilities of IoT devices.
arXiv Detail & Related papers (2020-07-06T10:36:11Z) - 5G Security and Privacy: A Research Roadmap [24.802753928579477]
5G - the latest generation of cellular networks - combines different technologies to increase capacity, reduce latency, and save energy.
We outline recent approaches supporting systematic analyses of 4G LTE and 5G protocols and their related defenses.
arXiv Detail & Related papers (2020-03-30T16:36:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.