  Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures,
encompassing both stationary and mobile sensors. These sensors self-organize
and establish multi-hop connections for communication, collectively sensing,
gathering, processing, and transmitting data about their surroundings. Despite
their significance, WSNs face rapid and detrimental attacks that can disrupt
functionality. Existing intrusion detection methods for WSNs encounter
challenges such as low detection rates, computational overhead, and false
alarms. These issues stem from sensor node resource constraints, data
redundancy, and high correlation within the network. To address these
challenges, we propose an innovative intrusion detection approach that
integrates Machine Learning (ML) techniques with the Synthetic Minority
Oversampling Technique Tomek Link (SMOTE-TomekLink) algorithm. This blend
synthesizes minority instances and eliminates Tomek links, resulting in a
balanced dataset that significantly enhances detection accuracy in WSNs.
Additionally, we incorporate feature scaling through standardization to render
input features consistent and scalable, facilitating more precise training and
detection. To counteract imbalanced WSN datasets, we employ the SMOTE-Tomek
resampling technique, mitigating overfitting and underfitting issues. Our
comprehensive evaluation, using the WSN Dataset (WSN-DS) containing 374,661
records, identifies the optimal model for intrusion detection in WSNs. The
standout outcome of our research is the remarkable performance of our model. In
binary, it achieves an accuracy rate of 99.78% and in multiclass, it attains an
exceptional accuracy rate of 99.92%. These findings underscore the efficiency
and superiority of our proposal in the context of WSN intrusion detection,
showcasing its effectiveness in detecting and mitigating intrusions in WSNs.
