Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

Mrabet, Zakaria El; Arjoune, Youness; Ghazi, Hassan El; Majd, Badr Abou Al; Kaabouch, Naima

doi:10.3390/jsan7030026

Cited by 15 publications

(5 citation statements)

References 35 publications

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“…Nevertheless, its rate of recognition is very less 18 . In the study of Reference 19, a Kalman filter framework‐based solution for identifying primary user emulation as‐saults with a non‐stationary primary user was developed. Even though the limitation of this work is locating the primary user's initial position and the difficulty of managing the measurement uncertainty.…”

Section: Literature Surveymentioning

confidence: 99%

“…User access to the spectrum/database is restricted via access control. Sensitive/confidential information of users should be protected 12–20 . Cellular wireless networks must improve their capacities as demand for mobile broadband networks grows.…”

Section: Introductionmentioning

confidence: 99%

“…Sensitive/confidential information of users should be protected. [12][13][14][15][16][17][18][19][20] Cellular wireless networks must improve their capacities as demand for mobile broadband networks grows. One of the most difficult problems in cognitive radio networks (CRN) is the identification of PUEA.…”

Section: Introductionmentioning

confidence: 99%

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To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

Mazumdar

Gohel

2023

Engineering Reports

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The growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

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Section: Literature Surveymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

Mazumdar

Gohel

2023

Engineering Reports

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“…The communication between the smart meters, the home appliances, and the AMI headend is defined through several communication protocols such as Z-wave and Zigbee [9,10,12,17].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure

Mrabet

Ezzari

Elghazi

et al. 2019

Proceedings of the 2nd International Conference on Networking, Information Systems &Amp; Security

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Smart grid is an alternative solution of the conventional power grid which harnesses the power of the information technology to save the energy and meet today's' environment requirements. Due to the inherent vulnerabilities in the information technology, the smart grid is exposed to a wide variety of threats that could be translated into cyber-attacks. In this paper, we develop a deep learning-based intrusion detection system to defend against cyber-attacks in the advanced metering infrastructure network. The proposed machine learning approach is trained and tested extensively on an empirical industrial dataset which is composed of several attack' categories including the scanning, buffer overflow, and denial of service attacks. Then, an experimental comparison in terms of detection accuracy is conducted to evaluate the performance of the proposed approach with Naïve Bayes, Support Vector Machine, and Random Forest. The obtained results suggest that the proposed approaches produce optimal results comparing to the other algorithms. Finally, we propose a network architecture to deploy the proposed anomalybased intrusion detection system across the Advanced Metering Infrastructure network. In addition, we propose a network security architecture composed of two types of Intrusion detection system types, Host and Network-based, deployed across the Advanced Metering Infrastructure network to inspect the traffic and detect the malicious one at all the levels.Despite the wide variety of industrial cyber-attacks, they usually attempt to compromise these three security parameters: confidentiality, availability, and integrity [11]. The first class of attacks which target the confidentiality includes attacks such as Traffic analysis attack [17], Modbus network scanning [2], and DNP3 network scanning [7] which target the two industrial protocol Modbus and DNP3, respectively. The second category of attacks which target the availability of the system includes attack such as Puppet attack [25], the Time delay switch attack and the Time synchronization attack [27] which are considered as a denial of service attack. The third category of attacks includes the false data injection attack, popping the Home Machine Interface (HMI) [17], Masquerade attack [17], and jamming attack [13].Defending against these various cyber-attacks in a distributed and heterogeneous system, such as smart grid, is challenging. The smart grid consists of several system and protocols including the supervisory and control and data acquisition (SCADA), the demand response system, the automation substation, and the advanced metering infrastructure (AMI). In this paper, we will focus on defending against cyber-attacks in the AMI network because it constitutes the main connection point between the Home Area Network (HAN) with the Neighborhood Area Network (NAN) and the Wide Area Network (WAN). The AMI Advanced metering infrastructure (AMI) is responsible for collecting, measuring and analyzing energy, water, and gas usage. It allows two-way communication from th...

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“…Similarly, the angle-received signal-strength-based approaches were used in [12,13] to detect the PUE attack. Recently, a PUE detection mechanism for non-stationary PU was proposed in [14] by using the Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

Primary User Localization and Its Error Analysis in 5G Cognitive Radio Networks

Saeed

Nam

Al-Naffouri

et al. 2019

Sensors

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It is crucial to estimate the location of primary users (PUs) for the development of cognitive radio networks (CRNs). Great efforts have been made in the past to develop localization algorithms with better accuracy but low computation. In CRNs, PUs do not cooperate with secondary users (SUs), which makes the localization task challenging. Due to this feature, received signal strength (RSS)-based PU localization techniques, such as centroid localization (CL) and multidimensional scaling (MDS), are the best candidates. However, most of the CL- and MDS-based PU localization methods consider omnidirectional wireless communication. Therefore, in this paper we propose a PU localization method which uses the RSS values at different sectors of the SU antenna, where a scoring strategy is applied to all the sectors to estimate the PU location. Two different scoring functions are proposed. Numerical results show that the proposed localization method is robust to PU locations and channel conditions. The proposed method is validated in terms of various network parameters, such as the number of SUs, beamwidth of the SU sectors, size of the grid, and placement of the PUs. Results show that increasing the number of SUs improve the localization accuracy due to an increased number of measurements. However, the localization accuracy degrades with an increase in the beamwidth of the SU sector because the faraway grid points also participate in the localization. The results are also compared with the conventional CL for PU localization. Compared with conventional CL, it offers a significant improvement in the performance.

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Primary User Emulation Attacks: A Detection Technique Based on Kalman Filter

Cited by 15 publications

References 35 publications

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure

Primary User Localization and Its Error Analysis in 5G Cognitive Radio Networks

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