Rigorous Analysis of Data Orthogonalization for Self-Organizing Maps in Machine Learning Cyber Intrusion Detection for LoRa Sensors

Nair, Manish; Cappello, Tommaso; Dang, Shuping; Beach, M.A.

doi:10.1109/tmtt.2022.3223122

Cited by 3 publications

(8 citation statements)

References 42 publications

(64 reference statements)

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“…Machine learning (ML) enabled RF fingerprinting has emerged as one of the most effective approaches for device authentication and RF cyber-physical security enhancement [5], [6]. Specifically, raw in-phase/quadrature (I/Q) samples derived after the down-conversion/heterodyning of an RF waveform can be applied to a convolutional neural network (CNN) for the purposes of classifying modulation schemes and radio identification.…”

Section: Rf Fingerprintingmentioning

confidence: 99%

“…One of the crucial requirements before applying data sets for RF fingerprinting assisted IoT device authentication is to understand of the attributes of the data set at hand, especially the correlation among I/Q samples. Failure to do so could result in inaccurate analytics [5], [6]. For example, the high correlation between the raw data sets of LoRa I/Q vectors is one of the most compelling factors facilitating the effective cyber intrusion by rogue devices when spoofing on LoRa waveforms.…”

Section: Testbed Configuration and Data Set Collectionmentioning

confidence: 99%

“…As a direct consequence of high correlation, a raw data set of LoRa I/Q samples are orthogonally inseparable as provided in data set 1 [6]. With an orthogonally inseparable data set, the non-convex training problem in ML classifiers becomes NPhard or polynomial-time hard.…”

Section: Testbed Configuration and Data Set Collectionmentioning

confidence: 99%

“…• Enhancing ML model performance and convergence across designed metrics, e.g., the mean squared error (MSE) and accuracy (ACC) of CNNs [6]. This is architecturally similar to AlexNet commonly employed in device authentication [14], which utilizes the adaptive stochastic gradient descent (ASGD) solver to derive the set of optimal neural weights through a back-propagation mechanism that maximizes or minimizes a chosen metric.…”

Section: Localization and Authentication Using Pre-processed Data Setsmentioning

confidence: 99%

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IoT Device Authentication Using Self-Organizing Feature Map Data Sets

Nair¹,

Dang²,

Beach³

2023

IEEE Commun. Mag.

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Sensors and actuators connected via the Internet of Things (IoT) have now become embedded within our critical infrastructure offering improved observation and control as well as reduced costs. Given that software defined radios (SDRs) can be readily programmed to imitate IoT devices, there is now a greater risk that assets can be spoofed or compromised. This necessitates an urgent need for IoT device authentication, avoiding the need to upgrade the many thousands of individual devices. However, the lack of publicly available data sets severely hampers the development of effective authentication algorithms and mechanisms. In this regard, this article introduces a technique for facilitating IoT device authentication when the radio frequency (RF) characteristics are highly correlated using self-organizing feature maps (SOFMs), thus aiming to promote state-of-the-art research in this field. The associated techniques demonstrated in this article exploit a novel data set of RF fingerprints and are, in particular, suitable for low-cost and long-range wireless application scenarios of the IoT, e.g., LoRa. Here, a well trained convolutional neural network (CNN) based on the SOFM data set can rapidly profile apparently correlated RF fingerprint patterns and thereby ascertain the nature of a specific device (friend or foe). In this way, a reliable and efficient IoT device authentication strategy for LoRa devices can be established. The experimental results presented in this article substantiate the effectiveness and efficiency of the SOFM based approach, and the data sets are introduced in detail and shared with the research community.

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Section: Rf Fingerprintingmentioning

confidence: 99%

Section: Testbed Configuration and Data Set Collectionmentioning

confidence: 99%

Section: Testbed Configuration and Data Set Collectionmentioning

confidence: 99%

Section: Localization and Authentication Using Pre-processed Data Setsmentioning

confidence: 99%

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IoT Device Authentication Using Self-Organizing Feature Map Data Sets

Nair¹,

Dang²,

Beach³

2023

IEEE Commun. Mag.

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“…There are many personal data in these data, such as blood pressure, pulse, electrocardiograms, place environment data, area humidity, room temperature, etc. Another authentication scenario is to consider the types of entities involved in the remote client and server scenarios [13]. Clients want to access servers' services.…”

Section: Introductionmentioning

confidence: 99%

Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions

Yalli,

Hasan,

Haron

et al. 2023

IJACSA

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The Internet of Things (IoT) data is the main component for finding the basis that allows decisions to be made intelligently and enables other services to be explored and used. Data originates from smart things that have the capabilities to connect and share data enormously with other things in the IoT ecosystem. However, the level of intelligence obtained and the type of services provided, all depend on whether the data is trusted or not. High-quality data is the most trusted;, it can be used to extract meaningful insights from an event and can also be used to provide good services to humans. Therefore, decisions based on high-quality and trusted data could be good, whereas those based on low-quality or untrusted data are not only bad but could also have severe consequences. The term Quality of Data (QoD) is used to represent data trustworthiness and is used throughout this paper. To the best of our knowledge, this work is the first to coin the term QoD. The problems that hinder QoD are identified and discussed. One if it is an outlier, it is a major feature of the data that degrades its overall quality. Several machine-learning techniques that detect outliers have been studied and presented, with few data-cleaning techniques. This paper aims to present the elements necessary to ensure QoD by presenting the overview of the IoT state-of-the-art. Then, data quality, data in IoT, and outliers are studied, and some quality assurance techniques that maintain data quality is presented. A comprehensive taxonomy is shown to provide state-of-the-art data in IoT. Open issues and future directions were suggested at the end of the paper.

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The Use of SNN for Ultralow-Power RF Fingerprinting Identification With Attention Mechanisms in VDES-SAT

Jiang

Sha

2023

IEEE Internet Things J.

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Rigorous Analysis of Data Orthogonalization for Self-Organizing Maps in Machine Learning Cyber Intrusion Detection for LoRa Sensors

Cited by 3 publications

References 42 publications

IoT Device Authentication Using Self-Organizing Feature Map Data Sets

IoT Device Authentication Using Self-Organizing Feature Map Data Sets

Quality of Data (QoD) in Internet of Things (IOT): An Overview, State-of-the-Art, Taxonomy and Future Directions

The Use of SNN for Ultralow-Power RF Fingerprinting Identification With Attention Mechanisms in VDES-SAT

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