Innovative Attack Detection Solutions for Wireless Networks With Application to Location Security

Orlando, Danilo; Bartoletti, Stefania; Palamà, Ivan; Bianchi, Giuseppe; Melazzi, Nicola Blefari

doi:10.1109/twc.2022.3192225

Cited by 7 publications

(4 citation statements)

References 42 publications

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“…The research by [94] brings forward a scalable solution for Next Generation Internet of Things (NGIoT) networks, leveraging beam pattern features of mmWave devices. In contrast, [95] and [96] provide varied methodologies, focusing on the 5G mmWave 60 GHz band and Akaike, Bayesian, and Generalized Information Criteria, respectively. Expanding the horizon, [97] and [98] delve into the potentials and challenges within wireless communications, with [99] concluding our discussion by introducing Thermal Pattern Analysis as a promising tool for spoofing attack prediction.…”

Section: Spoofing Attacks In V2x Communication Scenariosmentioning

confidence: 99%

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Herman Muraro Gularte,

Alfredo Ruiz Vargas,

Paulo Javidi da Costa

et al. 2024

IEEE Access

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Vehicle-to-Everything (V2X) communication, essential for enhancing road safety, driving efficiency, and traffic management, must be robust against cybersecurity threats for successful deployment and acceptance. This survey comprehensively explores V2X security challenges, focusing on prevalent cybersecurity threats such as jamming, spoofing, Distributed Denial of Service (DDoS), and eavesdropping attacks. These threats were selected due to their prevalence and ability to compromise the integrity and reliability of V2X systems. Jamming can disrupt communications, spoofing can lead to data and identity manipulation, DDoS attacks can saturate system resources, and eavesdropping can compromise user privacy and information confidentiality. Addressing these major threats ensures that V2X systems are robust and secure for successful deployment and widespread acceptance. An extensive review uncovered a global landscape of V2X cybersecurity research. We highlight contributions from China in scientific publications and the United States in patent innovations, with notable advancements from leading corporations such as Qualcomm, LG, Huawei, Intel, Apple, and Samsung. This work educates and informs on the current state of V2X cybersecurity and identifies emerging trends and future research directions based on a year-byyear analysis of the literature and patents. The findings underscore the evolving cybersecurity landscape in V2X systems and the importance of continued innovation and research in this critical field. The survey navigates the complexities of securing V2X communications, emphasizing the necessity for advanced security protocols and technologies, and highlights innovative approaches within the global scientific and patent research context. By providing a panoramic view of the field, this survey sets the stage for future advancements in V2X cybersecurity INDEX TERMS Vehicle-to-everything, connected and automated vehicle, intellectual property in V2X cybersecurity, cyberattacks and attacks, V2X security.

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Section: Spoofing Attacks In V2x Communication Scenariosmentioning

confidence: 99%

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Herman Muraro Gularte,

Alfredo Ruiz Vargas,

Paulo Javidi da Costa

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…where h 2 ∼ CN (0, β 2 ) is the channel gain of the wireless link from the computational node N 2 to the edge server, and P 2 denotes the transmit power at the computational node N 2 . Particularly, P 1 and P 2 should satisfy the following constraint [16,17],…”

Section: System Modelmentioning

confidence: 99%

Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence

Lin

Zhang

et al. 2023

EAI Endorsed Scal Inf Syst

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This paper aims to investigate an intrusion detection network for digital power grid networks, which consists of an edge server and two computational nodes that work collaboratively to detect any potential intrusion in the network. The primary objective of this study is to enhance the effectiveness of intrusion detection in the network. To achieve this objective, we first define the outage probability of the intrusion detection system under consideration. This is done to provide a measure of the probability that the system fails to detect an intrusion when it occurs. We then derive a closed-form expression for the outage probability to enable further analysis on the system behavior. Since the system resources, such as transmit power, are limited, we further design a transmit power allocation strategy to improve the system performance. This strategy seeks to optimize the allocation of transmit power across the different nodes of the intrusion detection network to maximize the likelihood of detecting intrusions while minimizing the resource usage. Finally, to evaluate the performance of the proposed system, we conduct simulations and provide results that demonstrate the accuracy of the closed-form expression and the effectiveness of the transmit power allocation strategy. These simulation results serve as evidence of the efficacy of the proposed approach in detecting intrusions in a resource-constrained network, especially for the digital power grid networks.

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“…Deep learning-based models have already surpassed classical machine learning-based approaches in various text mining fields [15][16][17]. One of the key steps in traditional machine learning is to extract features, but feature extraction has a disadvantage of relying mainly on human labor and each extraction method is not universal.…”

Section: Introductionmentioning

confidence: 99%

Research on the Performance of Text Mining and Processing in Power Grid Networks

Zhou

Lin

et al. 2023

EAI Endorsed Scal Inf Syst

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This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks.

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Innovative Attack Detection Solutions for Wireless Networks With Application to Location Security

Cited by 7 publications

References 42 publications

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Safeguarding the V2X Pathways: Exploring the Cybersecurity Landscape Through Systematic Review

Research on Intrusion Detection Technology of Computing Nodes in Digital Power Grid based on Artificial Intelligence

Research on the Performance of Text Mining and Processing in Power Grid Networks

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