## 5 AI-Based Cyber Threat Solutions in X
### Machine Learning (ML)  
Machine learning (ML) is a branch of artificial intelligence (AI) and computer science that focuses on the using data and algorithms to enable AI to imitate the way that humans learn, gradually improving its accuracy [50]. Machine learning can create an effective model automatically based on initial training data. The motivation for this approach is the availability of the appropriate training data, or it can be obtained at least more conveniently compared to the effort required to define the model manually [51]. The versatility of ML has led to its widespread application across diverse sectors, including healthcare, finance, natural language processing, and autonomous vehicles, as it can process large datasets and reveal insights that may be difficult for humans to discern. The scalability and adaptability of ML models make them highly valuable for addressing complex real-world problems, fueling innovation across industries [52]. Additionally, advancements in computational power and access to big data have accelerated the development of more advanced ML techniques, such as deep learning, which emulates neural networks in the human brain to analyze vast amounts of data with greater accuracy [53]. As ML continues to evolve, its potential to transform industries and solve pressing challenges expands rapidly. This section delves into the three primary methodologies in this field: supervised, semi-supervised, and unsupervised learning. This section provides a contextual background and a comprehensive analysis of key research within each category. 1. Supervised Learning  
Supervised learning is a machine learning system that can learn from the training data. Training data consist of pairs of input objects (usually vectors) and outputs. The output of the function can be assumed to be a continuous value (called regression) or a class mark of the input object (called classification). The task of the supervised learner is to predict the value of the function of any valid input object after observing several training examples (i.e., pairs of input and target output). In order to do so, the learner must \"reasonably\" generalize from the data given to unseen circumstances [54]. In other words, in terms of predictor characteristics, the goal of supervised learning is to create a concise model for the distribution of class labels [55]. Supervised learning is used, in particular, as a predictive mechanism in which a portion of the data is learned (or otherwise known as a training set), while another portion is used to test a trained model (Cross-validation) and the remainder will be used to determine the accuracy and effectiveness of the forecast [56]. Cyber threat analysis primarily relies on two types of features: behavioral and content. Behavioral features focus on user metadata, actions, and interactions, without deep content analysis. They examine factors like timestamps and basic text counts. The content features delve into the textual content itself to differentiate bots from real users. * **Behavior based** The well-known BotOrNot [57] is an off-the-shelf system that leverages more than one thousand features to discriminate bots. BotOrNot measures the 'bottness' of an X account. The authors expanded on their previous work [56] by retraining the model on a new dataset [58] and disclosing their feature engineering process [59]. They developed a feature set inspired by the DARPA competition [60] to distinguish between normal and bot accounts. Ref. [61] identified and evaluated the importance of features for Sybil detection on X, finding Random Forest to be the most effective classifier. Ref. [62] combined Support Vector Machines and neural networks (SVM-NN) to detect fake accounts and bots, reducing the feature set from to improve efficiency [63]. Ref. [64] developed a method to calculate a 'botScore' for X accounts, like the BotOrNot botness score. They identified ten user profile attributes and tweet patterns to feed into their BotClassifier, a supervised classification algorithm.