Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network

Sarode, Shraddha

doi:10.5120/ijca2021921657

Cited by 1 publication

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“…The interpretability metric can be defined based on the complexity of explanations provided by the model, such as the number of features considered or the clarity of the explanations [35]. The solution to this optimization problem will yield a fault detection model 𝑀 that effectively detects faults in loT sensor networks while providing trans‫ץ‬ ↓ ‫ר‬t and interpretable explanations for its decisions [37].…”

Section: Proposed Methdologymentioning

confidence: 99%

Fault Detection in IoT Sensor Networks with XAI-LCS: Explainable AI-driven Diagnosis for Low-Cost Sensor

Dattatray G. Takale

2024

jes

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In Internet of Things networks (IoT), accurate monitoring data delivery without interruptions is vital, especially for high-risk use cases, such as in the industrial field. Existing approaches to AI-based fault diagnosis have various disadvantages such as being computationally expensive or lacking transparency and being difficult to trust. To overcome these limitations this research introduces a novel method for IoT devices namely XAI-LCS. This technique uses the eXtreme Gradient Boosting (XGBoost) algorithm for early sensor fault detection. XAI-LCS is oriented towards detecting different types of faults including bias, drift, complete failure, and precision degradation, as well as accounting for data imbalances and avoiding biased detections. The proposed solution achieves a 98 % validation accuracy in diagnosing four sensor fault types. The XAI component which provides explanations for the AI-based model processes, enhances the trust and transparency of the developed solution. As a result, this study contributes to improving sensor application failures in IoT.

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Section: Proposed Methdologymentioning

confidence: 99%

Fault Detection in IoT Sensor Networks with XAI-LCS: Explainable AI-driven Diagnosis for Low-Cost Sensor

Dattatray G. Takale

2024

jes

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show abstract

Review of Machine Learning Techniques for Detection of Routing Attacks in Wireless Sensor Network

Cited by 1 publication

References 0 publications

Fault Detection in IoT Sensor Networks with XAI-LCS: Explainable AI-driven Diagnosis for Low-Cost Sensor

Fault Detection in IoT Sensor Networks with XAI-LCS: Explainable AI-driven Diagnosis for Low-Cost Sensor

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