Detecting Interference in Wireless Sensor Network Received Samples: A Machine Learning Approach

O'Mahony, George D.; Harris, Philip J.; Murphy, Colin C.

doi:10.1109/wf-iot48130.2020.9221332

Cited by 12 publications

(5 citation statements)

References 13 publications

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“…A single decision tree may not be able to predict the class of an object accurately, but multiple of them, with each one progressively learning from the mistakes of the other, can be a robust model. Tis is the idea of the Adaboost algorithm [36].…”

Section: Machine-learning Model Representationmentioning

confidence: 99%

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Nguyen-Minh,

Tran Hoang,

Pham Thanh

2023

Security and Communication Networks

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Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-learning models in jamming detection by analyzing how the approach works on individual observations of vehicles in different scenarios. We propose a machine learning-based approach that explores hidden rules of how the observation changes under a reactive jamming attack. Our proposed machine learning-based approach improves detection accuracy compared to existing methods. The performance of the proposed method depends closely on dataset selection. Though we evaluate our approach with synthetic data, the data are generated with justifiable simulations calibrated to match a real-referenced dataset. Our analysis provides a connection between the machine-learning domain and vehicular networks by utilizing specific domain techniques for jamming detection.

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Section: Machine-learning Model Representationmentioning

confidence: 99%

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Nguyen-Minh,

Tran Hoang,

Pham Thanh

2023

Security and Communication Networks

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“…Regarding WSN privacy and safety, the authors in [149] evaluated ML algorithms for interference detection that focus entirely on the analysis of samples received in-phase (I) and Quadratic phase (Q). Mitigation measures can be used once an intrusion has been identified, highlighting the need for interference detection.…”

Section: Ml-based Wsn Diversified Securitymentioning

confidence: 99%

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

Ahmad

Wazirali

Abu-Ain

2022

Sensors

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Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

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“…Deep learning methods are methods where the features used do not need to be prepared before using them for training. Because ANNs can iteratively learn their own features, use large amounts of data, and are less constrained by assumptions about that data, they are extremely flexible and can handle many kinds of tasks [e.g., [41][42][43][44][45][46][47]. These methods have been used on a variety of topics including image processing, video segmentation, and speech recognition [48][49][50].…”

Section: Machine Learningmentioning

confidence: 99%

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

2022

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Vocalizations in animals, particularly birds, are critically important behaviors that influence their reproductive fitness. While recordings of bioacoustic data have been captured and stored in collections for decades, the automated extraction of data from these recordings has only recently been facilitated by artificial intelligence methods. These have yet to be evaluated with respect to accuracy of different automation strategies and features. Here, we use a recently published machine learning framework to extract syllables from ten bird species ranging in their phylogenetic relatedness from 1 to 85 million years, to compare how phylogenetic relatedness influences accuracy. We also evaluate the utility of applying trained models to novel species. Our results indicate that model performance is best on conspecifics, with accuracy progressively decreasing as phylogenetic distance increases between taxa. However, we also find that the application of models trained on multiple distantly related species can improve the overall accuracy to levels near that of training and analyzing a model on the same species. When planning big-data bioacoustics studies, care must be taken in sample design to maximize sample size and minimize human labor without sacrificing accuracy.

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Detecting Interference in Wireless Sensor Network Received Samples: A Machine Learning Approach

Cited by 12 publications

References 13 publications

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?

Machine Learning for Wireless Sensor Networks Security: An Overview of Challenges and Issues

The impacts of fine-tuning, phylogenetic distance, and sample size on big-data bioacoustics

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