Jammer Localization Through Smart Estimation of Jammer’s Transmission Power

Aldosari, Waleed; Zohdy, Mohamed; Olawoyin, Richard

doi:10.1109/naecon46414.2019.9058093

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The distance ratio is based on the signal-to-noise ratio when the jamming signal affects boundary and edge nodes, which was introduced in [32]. As the jammer moves closer to a node, we observe that the SNR decreases to reach the system threshold value before becoming jammed.…”

Section: System Modelmentioning

confidence: 99%

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Aldosari¹

2023

Computers, Materials &Amp; Continua

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This paper presents a machine-learning method for detecting jamming UAVs and classifying nodes during jamming attacks on Wireless Sensor Networks (WSNs). Jamming is a type of Denial of Service (DoS) attack and intentional interference where a malicious node transmits a high-power signal to increase noise on the receiver side to disrupt the communication channel and reduce performance significantly. To defend and prevent such attacks, the first step is to detect them. The current detection approaches use centralized techniques to detect jamming, where each node collects information and forwards it to the base station. As a result, overhead and communication costs increased. In this work, we present a jamming attack and classify nodes into different categories based on their location to the jammer by employing a single node observer. As a result, we introduced a machine learning model that uses distance ratios and power received as features to detect such attacks. Furthermore, we considered several types of jammers transmitting at different power levels to evaluate the proposed metrics using MATLAB. With a detection accuracy of 99.7% for the k-nearest neighbors (KNN) algorithm and average testing accuracy of 99.9%, the presented solution is capable of efficiently and accurately detecting jamming attacks in wireless sensor networks.

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Section: System Modelmentioning

confidence: 99%

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Aldosari¹

2023

Computers, Materials &Amp; Continua

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“…In this scenario, the jammer location is fixed, e.g., any interferer working at the same frequency band with the legal system can be considered as a jammer. If the UAV detects a jamming attack from a malicious jammer and then also estimates the jammer's location, e.g., using a method proposed in [38], [53], [54], it has the capability to move to an optimal position combining with optimal power allocation to mitigate the effects of jamming attacks. It is noticed that the main objective of the UAV is to provide the required communication reliability.…”

Section: A Scenario I: Fixed Jammermentioning

confidence: 99%

Defeating Jamming Using Outage Performance Aware Joint Power Allocation and Access Point Placement in Uplink Pairwise NOMA

Dao

Hoang

Girs

et al. 2021

IEEE Open J. Commun. Soc.

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In this paper, an uplink pairwise Non-Orthogonal Multiple Access (NOMA) scenario using a mobile access point (AP) or an unmanned aerial vehicle in the presence of a jamming attack is considered. To mitigate the influence of the jamming attack, a joint power allocation and AP placement design is proposed. Accordingly, closed-form expressions of the overall outage probability (OOP) and the individual outage probability (IOP) considering imperfect channel state information for each of the source nodes the AP serves, are derived over Nakagami-m fading channels using dynamic decoding order and fixed pairwise power allocation. We conduct an investigation of the effect of different parameters such as power allocation, source node placements, AP placement, target rates, and jammer location on the OOP and the IOP performance. By adapting the power allocation and the AP placement to the jamming attack, the communication reliability can be increased significantly compared to neglecting the presence of the jammer or treating the jammer as noise. Since the malicious jammer and the AP have conflicting interests in terms of communication reliability, we formulate a non-cooperative game for the two players considering their positions and the power allocation of the NOMA nodes as their strategies and the OOP as utility function. We propose using hybrid simulated annealing -greedy algorithms to address the joint power allocation and AP placement problem for the cases of both a fixed and a mobile jammer. Finally, the Nash equilibrium points are obtained and then the UAV goes directly to this position and keeps staying there to save power consumption.

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“…In this section, we described the concept of distance ratio which was introduced in [ 29 ]. Moreover, we extended the distance ratio concept to three-dimensional space

.…”

Section: Concept Of Distance Ratio ( )mentioning

confidence: 99%

Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization

Aldosari

Moinuddin

Aljohani

et al. 2020

Sensors

Self Cite

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Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel. In this work, the considered jammer is a UAV hovering around the target area to block the communication channel between two transceivers. We proposed a three-dimensional (3-D) UAV jamming localization scheme to track and detect the jammer position at each time step by employing a single boundary node observer. For this purpose, we developed two distributed Extended Kalman Filter (EKF) based schemes: (1) the Distributed EKF (DEKF) scheme using the information of the received power from the jammer at a single nearby boundary node only and (2) Distance Ratio aided Distributed EKF (DEKF-DR) based scheme utilizing an edge node in addition to a single boundary node. Extensive simulations are conducted in order to evaluate the performance of the proposed distributed algorithms for a 3-D trajectory and compared with that of the conventional Centralized EKF (EKF-Centr) based method (which is also modified for the 3-D scenario). The results show the clear supremacy of the proposed distributed algorithms with much lesser complexity in contrast to the conventional EKF-Centr technique.

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Jammer Localization Through Smart Estimation of Jammer’s Transmission Power

Cited by 4 publications

References 15 publications

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Received Power Based Unmanned Aerial Vehicles (UAVs) Jamming Detection and Nodes Classification Using Machine Learning

Defeating Jamming Using Outage Performance Aware Joint Power Allocation and Access Point Placement in Uplink Pairwise NOMA

Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization

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