## 5 AI-Based Cyber Threat Solutions in X
### Deep Learning (DL)  
DL is a type of ML technique that allows machines to learn from their mistakes and comprehend the world as a hierarchy of concepts [103]. DL enables computational models consisting of several layers of processing to learn data representation at multiple abstraction levels. These methods have greatly improved state-of-the-art speech recognition, visual object recognition, object detection, and many other domains such as drug discovery and genomics [52]. The use of DL technology for cybersecurity research and intrusion detection is highly important since most attacks use invasive software families that can be detected and classified [55]. DL is commonly used in pattern recognition. Furthermore the issue of classification, such as text classification and image classification, has also shown efficiency when DL is used [13]. 1. Convolutional neural networks (CNNS)  
ConvNets is designed to process data that come in the form of multiple arrays, such as a color image consisting of three 2D pixel-intensity arrays in three color channels. There are many data modalities in the form of multiple arrays and 1D for signals. Sequences, like language; 2D images or audio spectrograms; and 3D images, either video or volumetric. The four key ideas behind ConvNets that take advantage of the characteristics of natural signals are: local connections, shared weights, pooling, and the use of multiple layers [52]. Convolutional networks integrate three architectural ideas to ensure a certain degree of transition, size, and distortion invariance: (1) local receptive fields, (2) shared weights (or duplication of weights), and (3) spatial or temporal subsampling [104]. Ref. [105] also suggested CNN to strive for image recognition. The basic idea of CNN is to capture a data function by transferring the kernel, a convolution matrix, to a region in the image. Generally, while neural networks cannot retain spatial information in the image, CNN can maintain it by adding the kernel to each area of the image. In the case of natural language processing (NLP), we can also add the convolutional layer of CNN to the vector space translated from the text corpus. Since each kernel can learn how to insert in a region, i.e., one sentence in the NLP, and capture the semantic and structural features of the sentence, CNN performs well in the text classification. Ref. [14] proposed a multitask learning approach based on the natural language processing technology and ML algorithm of the iterated dilated convolutional neural network (IDCNN) and Bidirectional Long Short-Term Memory (BiLSTM) to establish a highly accurate network model. Their results show that the proposed model operates well to predict cyber hazard incidents from tweets and greatly outperforms a variety of baselines. 2. Graph Convolutional Networks (GCNs)  
A Graph Convolutional Network (GCN) is a specialized neural network for processing and analyzing graph-structured data. In graphs, nodes represent entities, and edges represent the relationships between those entities [106]. GCNs have garnered significant attention and popularity due to their effectiveness across various domains where data can be naturally represented as graphs. Their strengths include the ability to excellently represent nodes within a graph through their iterative structure, handle irregular and complex data structures, perform node classification and prediction, and adapt to new graph contexts and scale efficiently [107]. Ref. [108] proposed a deep learning-based approach for identifying trolls and toxic content on social media. The developed machine learning model detects toxic images by analyzing their embedded text content. The model employs GloVe word embeddings to improve predictive accuracy and incorporates Graph Convolutional Networks (GCNs) to analyze the complex relationships in social media data. While the model demonstrates potential, it faces challenges in precision and recall.