MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs This paper proposes an innovative intrusion detection approach for Wireless Sensor Networks (WSNs) that integrates Machine Learning (ML) techniques with the SMOTE-TomekLink algorithm. The approach addresses the challenges of imbalanced datasets, low detection rates, computational overhead, and false alarms in WSNs. The SMOTE-TomekLink algorithm is used to balance the dataset by synthesizing minority instances and removing Tomek links, resulting in a more accurate and reliable intrusion detection system. Additionally, feature scaling through standardization is employed to ensure consistent and scalable input features, facilitating more precise training and detection. The proposed model is evaluated using the Wireless Sensor Network Dataset (WSN-DS) containing 374,661 records, and it achieves an accuracy rate of 99.78% in binary classification scenarios and 99.92% in multiclass classification scenarios. The results demonstrate the effectiveness of the proposed model in detecting and mitigating intrusions in WSNs. The paper also discusses the methodology, data preprocessing, data balancing using SMOTE-TomekLink, and the evaluation of various ML algorithms for intrusion detection in WSNs. The results show that the Random Forest (RF) algorithm performs the best in terms of accuracy, precision, recall, and F1-score, making it the optimal choice for intrusion detection in WSNs. The study highlights the importance of data balancing techniques in enhancing the performance of intrusion detection models in WSNs and underscores the effectiveness of machine learning approaches in accurately detecting intrusions and ensuring the security of WSNs.MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs This paper proposes an innovative intrusion detection approach for Wireless Sensor Networks (WSNs) that integrates Machine Learning (ML) techniques with the SMOTE-TomekLink algorithm. The approach addresses the challenges of imbalanced datasets, low detection rates, computational overhead, and false alarms in WSNs. The SMOTE-TomekLink algorithm is used to balance the dataset by synthesizing minority instances and removing Tomek links, resulting in a more accurate and reliable intrusion detection system. Additionally, feature scaling through standardization is employed to ensure consistent and scalable input features, facilitating more precise training and detection. The proposed model is evaluated using the Wireless Sensor Network Dataset (WSN-DS) containing 374,661 records, and it achieves an accuracy rate of 99.78% in binary classification scenarios and 99.92% in multiclass classification scenarios. The results demonstrate the effectiveness of the proposed model in detecting and mitigating intrusions in WSNs. The paper also discusses the methodology, data preprocessing, data balancing using SMOTE-TomekLink, and the evaluation of various ML algorithms for intrusion detection in WSNs. The results show that the Random Forest (RF) algorithm performs the best in terms of accuracy, precision, recall, and F1-score, making it the optimal choice for intrusion detection in WSNs. The study highlights the importance of data balancing techniques in enhancing the performance of intrusion detection models in WSNs and underscores the effectiveness of machine learning approaches in accurately detecting intrusions and ensuring the security of WSNs.