## 3 The Proposed Architecture
### The Modules in the Knowledge-Based Cognitive Architecture
It was discovered that these variables could be used in combination with the variable that the engineers were using previously. **The pre-processing module** (3): In this module, the data from the satellite network are retrieved and pre-processed. One of important pre-processing steps is data filtering. It refers to the process of defining, detecting, and correcting errors in raw data, in order to minimise the impact on subsequent analyses. There may be multiple filters. The techniques used for filtering depend on the requirements of data analysis and the data itself. In this work, the filters applied in this module focused on removing missing attribute values caused when a sensor broke down in a terminal. After this initial filtering, PCA was applied for dimensionality reduction. The results of the dimension reduction were then sense-checked using rules from the knowledge base that embeds domain specific knowledge. For example, as mentioned before, it was reveal through PCA that a set of signal propagation variables could be used in combination to predict the state of a terminal. **The prediction and pattern recognition module** (4): As noted in the literature review, many machine learning techniques can be used for anomaly detection. Nevertheless, no matter what kind of technique is used, function mapping is needed to represent the relationbetween the output y and the input features (\(x_{1},\dots\ x_{n}\)), which are the observed variables in the network (Equation (1)). This function can be represented with any type of machine learning model. In this work, a variety of machine learning algorithms were applied. These included supervised learning techniques such as decision trees and support vector machines, and unsupervised learning techniques such as k-means and k-nearest neighbour. \[y=f(x1,\dots\ xn) \tag{1}\]  
The prediction and pattern recognition module provided us the opportunity to load and test various algorithms for tests. Algorithms for pattern recognition enabled us to find regular patterns in data. Through these regularities in the data, models of the patterns seen in the domain can be built and used to classify terminal behaviour based on the value of their attributes. Examples of such algorithms used in this work include decision trees and k-means. The derived models of the domain were then used to predict which class (disrupted or not disrupted) a terminal might fall into depending on the value of its attributes. The prediction algorithms used in this work attempted to fit curves to data during training. These curves are mostly mathematical models that have a dependent target that rely on the values of attributes of the terminals. During training, the prediction algorithms work by using a performance measure (cost function) to minimise the error of the curve in relation to the modelled dataset. The output of the training is a mathematical model that can then be used to either extrapolate or interpolate for those data points that are not present in the data set. Examples of prediction algorithms used in this work are linear regression and SVM regression. The advantage of using the framework shown in Figure 3 is that it gives the capability to compare different algorithm performances for further application. Both classes of algorithms noted above were used to build prediction and pattern recognition models. In this work, the algorithms that produced the best models were carried forward. In determining which model to carry forward, the working memory was used. **Working memory** (5): In our architecture, the working memory was used to store models generated by the machine learning algorithms and aggregated data bundles (ADBs) derived from the monitored network. The data were saved in a way of first-in-first-out (FIFO). The data were used by the architecture to generate models via its prediction and pattern recognition algorithms. The models generated were assessed by the evaluator in the working memory.