# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Framework for Energy Drainage Attacks in WSNs  
We reviewed studies to address the problem and present a new method to resolve it. Some studies have integrated several methods into a framework. In this section, we will introduce such examples.  
[PERSON] et al. [88] proposed a novel DoS attack detection framework called cluster-based WSNs and variable selection ensemble machine learning algorithms (CBWSN_VSEMLA). The model is dependent on the fuzzy C-means (FCM) clustering technique, ensemble ML algorithms, and principal component analysis (PCA) for feature selection algorithms. After completing five steps for the fuzzy C-means (FCM) clustering technique, a variable selection ensemble machine learning algorithm (VSEMLA) was devised to detect four DoS attacks: black hole, grey hole, flooding, and scheduling attacks. Clustering was optimized through the fuzzy coefficient partition (FCP) by computing the optimal number of clusters. Novel WSN-DS data involves four types of attacks, and normal traffic was given to PCA and the ensemble ML algorithm. Next, model prediction for DoS attacks was developed. For the classifiers in ensemble ML algorithms, bagging, LogitBoost, and RandomForest algorithms were used. [PERSON] et al. [89] devised a novel hybrid trust management framework called the multi-layered assessment system for trustworthiness enhancement and reliability (MASTER). Under the clustering architecture, MASTER focuses on detecting and mitigatingconventional energy drainage attacks, such as Sybil, black hole, ballot stuffing, and on-off attacks. The architecture builds direct and indirect trust evaluation and combines them. An SVM with an RBF kernel was used to train the model for trust evaluation. The model was trained with 5776 training samples and 1156 cross-validation samples. To develop the trust model and generate the trust value, the co-location relationship, co-work relationship, cooperativeness frequency duration, and reward (R) were obtained from the sensor device. Two types of trust, communication trust and data trust were evaluated at three levels under the clustered architecture.  
[PERSON] et al. [90] proposed a novel framework to detect malicious behavior detection caused by false data injection attacks (FDIAs) using the ML algorithm. Through the framework, large-scale sensors are categorized into correlation groups by extracting spatiotemporal correlation information from sensor data. Furthermore, a consistency criterion was also developed to identify abnormal sensor nodes. A real data set was obtained from the U.S. smart grid, and simulated data sets were obtained using both the simple FDIA and the stealthy FDIA strategies. This detection framework consists of correlation grouping, prediction, and testing. For the group, the temporal correlation between time-series signals from sensors was obtained, and subsequent detection methods were performed within each group. Based on the normal and actual temporal correlation of the cross-correlation within each group, consistency was achieved between the pairwise sensor data to detect abnormal nodes.