## 5 AI-Based Cyber Threat Solutions in X
### Deep Learning (DL)
The model correctly identifies toxic content in more than 50% of cases but struggles with precision, detecting positive instances less than 50% of the time. Additionally, the recall rate is limited, capturing only 40% of positive cases. The F1-score, which balances precision and recall, is approximately 0.4, suggesting that further improvements are needed for enhanced effectiveness. Ref. [109] presented a graph-based approach for malware detection by constructing a program graph that captures the relationships within a program and developing two enhanced Graph Convolutional Network (GCN) architectures. The first model incorporates label propagation into the GCN to utilize label information, enabling neighborhood aggregation and propagating labels from labeled to unlabeled nodes. The second model introduces residual connections between the original node features and the node representations generated by the GCN layer, improving information flow and mitigating the over-smoothing problem. Experimental results demonstrate that the proposed models significantly outperform baseline GCN and traditional machine learning methods in malware detection, highlighting their effectiveness in program representation learning and malware detection using program graphs. Ref. [110] proposed a deep learning-based framework that analyzes social media across three key domains: users' profiles, the content they share, and the examination of users' unstructured ego-networks. This framework is built on an inductive learning-based graph neural network, enabling a 3D analysis of social media platforms. The proposed model can serve as a benchmark, providing a baseline for future research. Its performance is compared with existing approaches like SVM and LSTM, and experimental results demonstrate its superior performance using the real-world PHEME dataset. Furthermore, the framework can be leveraged as an OSINT (Open-Source Intelligence) tool, contingent on the availability of customized data. 3. Recurrent neural networks (RNNS) and Long Short-Term Memory Networks (LSTM)  
A recurrent neural network (RNN) is a recurrent structure where a directed graph along a chain is generated by node associations. This helps the RNN to view time dynamic behavior for a time series applied to natural language processing (NLP). RNNs can use their internal state to process input agreements and may do so only for a limited period of time, i.e., they cannot remember long-term information [110]. In other words, RNN is a neural network that simulates a complex system of discrete time that has an input xt, an output yt, and a hidden state ht. The subscript t represents time in our notation. RNN's have a very elegant way of dealing with sequential (time) data that embodies connections between data points similar to the sequence [111]. Ref. [112] proposed recurrent neural network (RNNs) for sequential data processing such as voice and text processing. The defining characteristic of RNNs, which is distinct from that of RNN, the general neural networks are the introduction of the hidden state vector. The secret state represents the description of the previous input data which are modified once the new input is reached. Finally, after processing all input results, the secret state is the summarization of all sequences, which is similar to the processing of a sequence performed by a human being. Of course, RNN has the benefit of reading sentences that are read by a human. However, as the layer deepens, gradient explosions and vanishing problems occur, which can degrade performance [113]. Ref. [114] proposed the Long-Term Memory (LSTM) technique to avoid this. In order to prevent the gradient from bursting and causing disappearing problems, LSTM adds the cell state to change the previous knowledge. LSTM has been commonly used for text classification because it can learn high-level representation using a deeper layer due to the cell status while maintaining the sequence of representations given by RNN. Ref.