# Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
## 3 Artificial Neural Networks  
ANNs are based on the concept of the biological neural network. An ANN is composed of a large number of highly interconnected neurons arranged into different layers, i.e., the input layer (IL), the hidden layer (HL) and the output layer (OL), to provide the desired solution to a problem, as shown in Figure 2. These layers are connected through weighted connections. The network gains the ability to modify the weights attached to each connection between nodes to enhance its performance on a particular job. An ANN's performance may be impacted by the quantity of the HL and neurons within them. Although having many neurons in the HL or HLs may increase network complexity, it may also guarantee accurate learning. There are two types of ANN: recurrent neural networks (RNNs) and feed-forward ANNs (FNNs). In FNNs, information travels in only one direction, that is, from the IL through the HL to the OL. By contrast, in RNNs, the information travels in both directions by introducing loops into the network [33]. To train these networks, we employ two important supervised learning algorithms and/or unsupervised learning algorithms. In supervised learning, set of inputs and the expected outputs are supplied to the network during the training process. The error between the produced and required outputs is used to modify the weights of the connections. The quality of the training dataset determines how well the ANNs perform. ANNs using more accurate training datasets produce more accurate outcomes.  
For supervised-learning-based ANNs, data collection for training presents a significant challenge. When the intended outputs are unknown during training, unsupervised learning  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Year** & **Author Name** & **Attack Type** & **Prevention** & **Benefits** \\ \hline
2021 [23] & [PERSON]; [PERSON]; [PERSON] & Forensic examination using AODV Protocol & Examination of IoT networks with AODV protocol, vulnerability analysis, reconstruction of networks & Quantifying impact of black hole attacks, enhancing understanding of network performance. \\ \hline
2023 [24] & [PERSON] & Secure-path selection & Trust-based route selection mechanism, optimized routing & Enhanced security, reliability, and operational lifespan of WSNs. \\ \hline
2023 [25] & [PERSON] and [PERSON] & Network security against attacks & Ranking-based route mutation mechanism, bafflement technique, intruder sink nodes & Enhanced security against internet traffic intruders, robust security at the base-station level. \\ \hline
20223 [32] & [PERSON]. [PERSON] & Routing-attack & Fuzzy and feed-forward neural networks & Enhanced detection and accuracy rates \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 2: General artificial neural network architecture.  
can be used because it does not require input for connection-weight adjustments. As there are no established categories for the classification of the output patterns, the system must create its own representations for the inputs that are provided. The most-often-used learning algorithm for classification in feed-forward ANNs is backpropagation (BP). In a BP algorithm, to reduce the discrepancy between the desired output and the projected output obtained during the forward pass, a gradient-based optimization approach is used to reduce the error in the outputs. This approach involves modifying the weights of the connections between the neurons in the hidden and output layers during the backward pass.