## 4 Detection Method-Based Classification of Anomaly Detection Models in WSNs
### Classification-Based Anomaly Detection Models for WSNs
A number of anomaly detection models for WSN that were designed based on these formulations were analyzed with regards to some characteristics, including the type of input data, the exploitation of spatial, temporal and feature correlations, the setup of SVM thresholds, outlier types, the determination of anomaly type, outlier degree, the effects of dynamic topology change, the dynamic data distribution, and the network heterogeneity. The scope of the investigation was limited to the study of improvement and feasibility of the proposed techniques for detecting data anomalies in harsh environments. The result of the analysis revealed that the quarter-sphere formulations are the most feasible for outlier detection in WSNs due to their low computational cost and communication overhead which leads to high efficiency. Hyper-plane- and hypersphere-based techniques have expensive computational costs and cannot cope with different data distributions. Hyper-ellipsoid formulations incur high communication overhead with a computational cost lower than that of quarter sphere techniques. _[PERSON] et al._[88]: proposed a new one-class principal component classifier (OCPCC)-based anomaly detection model to detect anomalies in an online manner. It was claimed that the proposed model achieved high detection effectiveness compared to the one-class SVM model. However, the computational complexity produced by the calculation of principal components online and the clustering method used for calculating the detection threshold incurred high energy consumption that affects the lifetime of the network. _[PERSON] et al._[89]: the authors of this work proposed an adaptive algorithm to adjust the anomaly rate parameter represented by a model parameter of a one-class quarter-sphere SVM for anomaly detection in WSNs. The proposed model was aimed at improving the detection performance and minimizing the error rate when operating in an online manner. It was stated that the anomaly rate parameter selection is important to obtain optimal detection performance. The authors concluded that the proposed adaptive model improved the detection effectiveness compared to the static model when the ratio of anomalies varies in the dataset. However, the parameter tracking algorithm increased the computational cost of the proposed adaptive model compared to the static model. _[PERSON] et al._[90]: in this work, an ensemble based classification system which is composed of five diverse binary classifiers was proposed for anomaly detection in sensor measurements. These classifiers are the average based classifier, autoregressive linear predictor based classifier, neural network based classifier, neural network autoregressive predictor based classifier, and adaptive neuro-fuzzy inference system (ANFIS)-based classifier. Two kinds of input data which included measurement time series collected by the sensor under investigation and the measurements gathered from neighboring sensors were used as input for the ensemble classifier system which was implemented in the base station. The authors stated that the ensemble system assured the diversity of classifiers to build an efficient decision making system. However, the main drawback of the proposed systems is the high communication overhead produced by sending all the time series data of the sensor and its neighbors to the base station. Some limitations of classification based models are summarized as follows:  
* Classification techniques like SVM are computationally expensive and hence quickly consume the sensors' energy. These techniques are inefficient for the design of online detection models which are favorable for some WSN applications. Some of the proposed models have inefficiently dealt with highly dynamic data streaming. Although some adaptive classification-based models such as [41] were introduced, these models incurred prohibited computational cost that drain the limited energy of sensors quickly.