# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 5 Open Research Challenges and Discussion
Therefore, blockchain technology with decentralization characteristics provides a novel perspective for WSNs. [PERSON] et al. [100] reviewed the in-depth survey of a blockchain-based approach for malicious node detection and energy drain prevention by integrating a blockchain technique with WSNs named BWSN. [PERSON] et al. [101] investigated whether blockchains on IoT sensor network platforms are vulnerable to DDoS attacks through experiments over a blockchain testbed. However, the energy efficiency of blockchains remains unresolved. * Introducing ML: ML technology can improve the attack detection performance by optimizing the model. Furthermore, ML techniques that can address dynamic situations with a successful learning process have been applied in WSNs. ML technology can be used to identify attacks, risks, and malicious nodes [101]. Evaluations based on both supervised and unsupervised ML for flooding, gray hole, and black hole DoS attacks [102] have been presented. Deep learning models for DoS attack detection in WSNs [103] have also been discussed. Integration between HMMs and Gaussian mixture models for routing security [104] can identify abnormal nodes and pinpoint malicious network behaviors from their origins. Lightweight ML algorithms specialized for ML and privacy in collecting data are promising research challenges. In addition, ML algorithm requires high costs for deployment, such as computing resources, data collection, and long training times. Thus, a lightweight ML algorithm and data collection should the main focus of further research. On the other hand, it is also considerable to create a hybrid ML approach that is suitable for working on such types of embedded devices. Similarly, it is very important to decide the location of the ML training process, such as the edge and controller in SDWSNs mentioned in the network architecture above. * Preparing data set: The data set for attacks is difficult to obtain. Aside from a public WSNs-based dataset, [PERSON] et al. [105] examined a WSNs-BFSF data set using learning models after necessary preprocessing. The WSN-BFSF data set consists of attack traffic data, including black holes, flooding, selective forwarding, and normal traffic data through ns-2 simulation. Specialized in IoT, the SimpleHome_XCS7_1003_WHT_Security_Camera dataset among N-BaIoT data sets [106]was examined. This data set was used to categorize the data set containing attack and normal traffic data with high accuracy using twenty-three supervised and unsupervised ML models. These included naive Bayes, naive Bayes updateable, random forest, and random tree. Table 8 lists the available data sets for security attacks and their properties. In addition to including these data sets, the development of data sets for energy drainage attacks is crucial. * Validation by implementation: Most of the proposed approaches, except for ML-based methods analyzed in this review, were validated by simulation. Thus, it is also required to validate the proposed scheme through empirical experiments in the real world with scenarios that are equivalent to the collected and represented by data set for ML approaches. The data set can be transformed into a feasible format and used in mentioned frameworks such as [88; 89; 90]. The instances of EDA attacks through real implementation on sensor nodes were explained in [107; 108; 109; 110; 111]. Even though these implementations provide us with guidance for implementation, the most recent case was reported in 2016. So, it is required to implement the proposed algorithms on recent commercial products.