## 3 Proposed Framework for Intelligent Cyber Security
### Detection  
After splitting the dataset, the training \(\left(B_{n}^{Train}\right)\) and testing \(\left(B_{n}^{Test}\right)\) of the model are carried out in order to detect attacks. Detection is a crucial step that acquires knowledge from the features and predicts or detects the attack by providing intelligent cyber security. This work has developed Hyperparameter Tuning based on Regularized Long Short-Term Memory (HT-RLSTM). The existing LSTM leads to vanishing and exploding gradient descent problems due to poor initialization of weights. In addition, there are problemscaused by high bias and low variance, low bias and low variance, and improper selection of regularization parameters that lead to a high error rate as well as computation time and cost. Figure 2 shows that this work has tuned (initialized) the weight using a confidence interval (CI) and performed Average Deviation-Based Square-Root Elastic Net Regularization (AD-SREnetReg) in the LSTM to tackle this issue. Avoiding catastrophic forgetting was the added advantage generated by the detection technique, which aids in the detection of adversarial backdoor poisoning attacks. Fundamentally, the memory unit in the RNN is added to store the data and this is improved by the LSTM, which improves on the original hidden layer neural nodes of RNN. Similarly, an input gate, an output gate, and a forget gate are added to the LSTM to assess if past information should be discarded. The RNN is more complicated than the layer cell design. This LSTM network comprises an input gate, an output gate, a forget gate, and a cell state. The introduction of new data is prevented by the input gate, the output data is prevented by the output gate, the stored information is controlled by the forget gate, and the valuable information is stored by using the cell state. Initially, the weight initialization is carried out using a confidence interval, i.e.,  
\[w_{n}=\left[\overline{\mathbb{B}}+Z*\frac{\varphi}{\sqrt{N}},\overline{\mathbb{ B}}-Z*\frac{\varphi}{\sqrt{N}}\right] \tag{24}\]  
where, \(\overline{\mathbb{B}}\) denotes the training and testing samples, \(Z\) indicates the 95% of confidence, \(\varphi\) denotes the standard deviation, and \(N\) denotes the total dataset size. Based on the confidence interval, the weight achieves 95% surety that it belongs within this range, which keeps the weight within a moderate value and avoids the exploding or vanishing gradient descent problem. Thereafter, based on the weight value, the forward propagation process of the LSTM begins. The input gate layer takes data from the preceding concealed layer as well as the  
Figure 2: Proposed HT-RLSTM architecture. current input. As you can observe in the Equation (25). The information is then computed to obtain the following output:  
\[ \begin{split} I_{t}&=\text{Ac}t(w_{i}.[h_{t-1},\text{ B}_{t}]+b_{i})\\ \widetilde{\varsigma}_{t}&=\text{Ac}t_{\text{tanh}}( w_{c}.[h_{t-1},\text{B}_{t}]+b_{c})\end{split}  \tag{25}\]  
where the value range of \(I_{t}\) is (0,1), \(w_{i}\) is the weight of the input gate, \(b_{i}\) is the bias of the input gate, \(w_{c}\) is the weight of the candidate input gate, and \(b_{c}\) is the bias of the candidate input gate. The output of the forget gate has a similar computation formula as the input gate with different weights \(w_{f}\) and bias \(b_{f}\) as shown in Equation (26). \[F_{t}=\text{Ac}t\Big{(}w_{f}.[h_{t-1},\text{B}_{t}]+b_{f}\Big{)} \tag{26}\]  
where, the value range of \(F_{t}\) is (0, 1), \(w_{f}\) is the weight of the forget gate, and \(b_{f}\) is the bias of the forget gate, \(\text{B}_{t}\) is the input value of the current time, and \(h_{t-1}\) is the output value of the last moment. The step of updating from the previous cell state \(\varsigma_{t-1}\) to the current cell state (\(\varsigma_{t}\)), is shown in the Equation (27):  
\[\varsigma_{t}=F_{t}*\varsigma_{t-1}+i_{t}*\widetilde{\varsigma}_{t} \tag{27}\]  
here, the cell state \(\varsigma_{t}\)'s range of value is considered as (0, 1).