Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

Hoang, Tiep M.; Tuan, Hoang Duong; Lambotharan, Sangarapillai; Hanzo, Lajos

doi:10.1109/access.2021.3059648

Cited by 38 publications

(22 citation statements)

References 211 publications

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“…The SVM is a classification model that takes an input into a high-dimensional feature space, and depending on the data type, it uses kernel functions to separate it into two or three dimensions. The data points in the margin limits are the support vectors and help to calculate the optimized hyperplane; this process is shown in Figure 3 [32]. In Figure 3, H is the separating hyperplane.…”

Section: Support Vector Machinementioning

confidence: 99%

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Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

Muñoz

Martínez

Hernández

2022

Sensors

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Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

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Section: Support Vector Machinementioning

confidence: 99%

“…The first objective is to separate the two parts of the data in this case linearly [33]. The second is to find the margin as in (6) [32], where x is the argument, w is the weight of the margin, and b is a constant. In Figure 3, H is the separating hyperplane.…”

Section: Support Vector Machinementioning

confidence: 99%

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

Muñoz

Martínez

Hernández

2022

Sensors

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“…SVM [45] is a supervised learning model used in machine learning, and it is used for regression and classification analysis. A labeled dataset is passed to the SVM model, and it is able to categorize further data.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

2021

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Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

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“…TTN is based on Long Range Wide Area Network (LoRaWAN) [2], a Low Power Wide Area Network (LPWAN) technology that operates on top of the proprietary LoRa protocol stack (originally developed to connect battery and low-power devices wirelessly to the internet) [2]. It constitutes a STAR network topology that uses gateway devices for receiving data from nodes and forwarding it onto LoRaWAN servers [3]. LoRaWAN allows geographically spread devices connectivity, securing bidirectional communication, mobility, and localisation services, and provides open-source software for hardware gateways and backend services [4].…”

Section: Introductionmentioning

confidence: 99%

“…overview of LoRa specifications in Europe. Ten channels are defined in total, with eight having multi data-rate of 250bps-5.5Kbps, a single channel with high data rate (11Kbps), and a single Frequency Shift Keying (FSK) channel at 50kbps [3].…”

Section: Introductionmentioning

confidence: 99%

LoRaWAN Performance Evaluation and Resilience under Jamming Attacks

Kalokidou

Nair

Beach

2022

2022 Sensor Signal Processing for Defence Conference (SSPD)

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There is an increasing deployment of Internet-of-Things (IoT) networks, from smart meters and smart lighting to humidity soil sensors and medical wearable devices. Long Range (LoRa) is one such over-the-air (OTA) transmission IoT standard, having a wide range of applications in smart cities, agriculture and health. It facilitates the inter-connection of services and smooth exchange of information. However, owing to its wireless interface, it is susceptible, as all wireless networks are, to OTA attacks. In this paper, we initially obtain the Bit Error Rate (BER) and Packet Error Rate (PER) of LoRa, in order to investigate the impact of continuous and reactive jamming attacks on it. We show that overall, LoRa can achieve a good performance even under a jamming attack, subject to parameters such as the transmit power, the Spreading Factor (SF) and the Coding Rate (CR). Moreover, it is proven that the impact on BER and PER is similar irrespective of whether the attack occurs with total frame synchronization or is synchronized to after the preamble transmission. Lastly, we apply a detection scheme, based on previous values of Received Signal Strength Indicator (RSSI) and PER to successfully identify malicious attacks.

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Physical Layer Security: Detection of Active Eavesdropping Attacks by Support Vector Machines

Cited by 38 publications

References 211 publications

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks

Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

LoRaWAN Performance Evaluation and Resilience under Jamming Attacks

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