## 4 Comparison of Network Security Posture Assessment Methods  
Compared to the traditional network security situational awareness methods, the model proposed in this paper has a better advantage in weight updating; under the synergy of the IPSO optimization algorithm, the BiLSTM can quickly find the optimal number of neurons and the value of the learning rate, and also is faster than the traditional gradient descent in the descent process of the mean square error. The above three nodes work efficiently, so that the model convergence is more efficient. It reduces the time complexity of the model, and has better adaptability and relevance for time-sensitive task scenarios, like network security. In this paper, CNN neural network [23] and Random Forest algorithm [24] (Random Forest) are selected as the comparison models. And in order to deeply understand and reveal the unique role of IPSO optimization algorithm and BiLSTM neural network to this model and its contribution in situational awareness, this paper also takes the BiLSTM model as the baseline, together with the experimental comparison model. The experimental dataset for training is described as follows:  
The dataset parameters are compiled by the Australian Cyber Security Centre, containing data on normal activity and a wide range of attack behaviors. There are 49 features in five categories, covering nine types of attacks and normal traffic, and the data are clearly labelled. During the training process, the data is divided into the training set and the test set according to 8:2, and is pre-processed by cleaning, standardization, normalization, and coding. The IPSO-BiLSTM model is used to select the features with the encapsulated Wrapper algorithm: IPSO optimizes the BiLSTM parameters (e.g., number of neurons, time step), and the fitting coefficient is used as the objective function to train the model. During the demonstration process (model evaluation) the SMAPE, MAPE, and fitting coefficient are selected to evaluate the model. The IPSO-BiLSTM is compared with CNN, Random Forest, and ordinary BiLSTM, and then ran in the same environment. The results show that the model performs well in most of the metrics and converges quickly. For the methodological comparison, the following three indicators were chosen for this paper. SMAPE, Symmetric Mean Absolute Percentage Error, is an error metric commonly used to assess the accuracy of predictive models. SMAPE has some unique characteristics compared to other error metrics. It is relatively balanced in its treatment of positive and negative errors, and does not produce large deviations, because the predicted value is larger or smaller than the actual value. This makes it a fairer assessment of model performance in some cases. SMAPE is mainly used to compare the degree of the difference between the predicted value and the actual value, which is calculated by the formula:  
\[\text{SMAPE}=\frac{100\%}{\text{n}}\sum_{i=1}^{\text{n}}\frac{|\underline{ \text{y}_{i}}-\hat{\text{y}_{i}}|}{\underline{\text{y}_{i}}|+|\hat{\text{y}_{ i}}|} \tag{9}\]  
Mean Absolute Percentage Error (MAPE) is a metric used to assess the error between the predicted value and the true value. Expressing the error as a percentage provides a more intuitive reflection of the degree of deviation of the predicted value relative to the true value. The error is normalized for each sample, making data from different magnitudes comparable. It is commonly used to assess the accuracy of predictive models in a variety of domains. The formula is as follows:  
\[\text{MAPE}=\frac{100\%}{\text{n}}\sum_{i=1}^{\text{n}}\left|\frac{|\underline {\text{y}_{i}}-\hat{\text{y}_{i}}|}{\underline{\text{y}_{i}}}\right| \tag{10}\]  
The Fit Coefficient (R-squared Score, R\({}^{2}\)) is used to assess how well the regression model fits the observed data. R\({}^{2}=1\) It indicates, that the regression model fits the data perfectly and the predicted values are in perfect agreement with the observed values. The closer to 1, the better the model fits.