# An Ensemble-Based Machine Learning Approach for Cyber-Attacks Detection in Wireless Sensor Networks
## 2 Related Work
The authors of [20] proposed an anomaly detection model using the ensemble principle for feature selection in the case of unknown data distribution, lack of labeled data, new attack types, and the existence of several biasing parameters. They evaluated the proposed model using the KDD'99 dataset with detection rate, false alarm rate, and F1-score. The authors of [21] demonstrated the performance of several ensemble techniques, including Random Forest, Bagging, and Boosting, to detect various types of attacks, such as a DoS using the NSL_KDD dataset. The authors of [22] proposed an ensemble random  
Figure 1: Ensemble techniques: Bagging, Boosting, and Stacking workflow. forest model for anomaly detection in WSN using a set of base learners (DT, NB, and K-NN) and examined it using a real sensor dataset called AReM. The authors in [23] evaluated homogeneous ensemble models, such as hoeffding adaptive tree (HAT), and adaptive RF and heterogeneous ensemble models built using two base classifiers (adaptive RF and NB) against individual models such KNN, SVM, and NB using the WSN-DS dataset. The authors of [24] implemented a 3-step cascaded multi-layer perceptron (MLP)-based ensemble learning model for intrusion detection using the KDD Cup99 and NSL-KDD datasets. In [25], the authors proposed an ensemble method using RF, Density- Based Spatial Clustering of Applications with Noise (DBSCAN) and Restricted Boltzmann Machine (RBM) as base classifiers and using two types of Bayesian algorithm as a combination methods for intrusion detection in WSN and using NSL-KDD dataset. These studies presented the use of ensemble techniques for efficient intrusion detection systems; however, most ensembles developed to detect possible vulnerabilities rely on only one type of base-level classifier. These techniques have a deeper and more complex structure than single conventional ML classifiers because of the multiple ML-based classification methods have been integrated; therefore, ensemble ML techniques must be investigated further to evaluate their performance since WSNs have unique specifications. We propose using WSS, a lightweight ensemble-based ML approach, to detect DoS attacks on WSNs. Our proposed approach comprises a set of individual supervised ML classifiers that are evaluated based on several evaluation metrics, in which one classifier will be identified and selected as the most effective classifier to detect cyber-attacks to gain higher detection performance quickly. The proposed approach is designed to be simpler than other well-known ensemble techniques since there is no integration of the outcomes of the base ML models, it is diverse in terms of selecting the base classifiers, and flexible in adopting any number of ML models and any set of evaluation metrics. We can prioritize certain metrics with higher weights, or scores, if they have higher importance based on the learning conditions and application domain with our proposed approach. A similar approach for classifying and detecting DoS attacks on WSNs, considering their special requirements for the different ML learning workflow phases, has not been presented in previous studies to the best of the author's knowledge; however, a similar concept was presented in [26] to dynamically select the best classifier to detect GPS spoofing attacks targeting unmanned aerial vehicles.