## 5 AI-Based Cyber Threat Solutions in X
### Machine Learning (ML)
Compared to Naive Bayes, their model demonstrated superior performance in distinguishing between human and bot accounts. CATS by [65] uses a clever approach to spot X spam bots. By analyzing just 5 tweets per user, they combine entropy, spammer behavior, and a blacklist of spammy URLs. This helps them accurately identify spam accounts. The CATS team also introduced 15 new features for better spam detection. They tested different machine learning methods and even grouped spammers to understand how they operate. Ref. [66] conducted an empirical study on the evasion tactics used by social bots. They identified key characteristics of social bots and common evasion techniques, and subsequently proposed a detection method that incorporated nine novel features alongside existing ones. Their approach was evaluated across multiple social media platforms. Ref. [67] introduced a hybrid approach combining human judgment and machine learning to identify X bots. This semi-automated method prioritizes precision, making it suitable for creating large, high-quality datasets for bot detection models. Ref. [68] developed a novel approach to classifying X accounts by stratifying users based on account popularity. This strategy, centered on user social status, allowed them to identify distinct feature sets effective for distinguishing between human and automated accounts within each popularity tier. While their study focused on general account classification rather than specifically detecting malicious bots, the methodology, features, and dataset generated could serve as valuable foundations for future bot detection research. Similarly, ref. [69] introduced a refined set of features focusing on user interaction levels and engagement. They combined these features with existing ones to detect X bots using deep learning. In a similar vein, ref. [70] identified bots in marketing campaigns by analyzing user interactions on X. They compared various classifiers and found back-propagation neural networks to be most effective with their feature set. Pattern recognition techniques have been applied to classify X accounts. Ref. [71] developed a model to categorize accounts as human, bot (spam bot), or cyborg. Their approach involved analyzing account behavior through entropy calculations for tweet timing patterns, machine learning for text-based spam detection, and statistical analysis of account properties. A decision-making component combined these analyses for final classification. In subsequent work [72], the model was refined with enhanced components and evaluated on a larger dataset. Ref. [73] developed a method to identify automated fake X profiles by analyzing multiple profile attributes, screen name patterns, and tweet posting times. While their model exhibited high precision in detecting fake accounts, its recall was relatively low. Nonetheless, due to its exceptional accuracy, the authors propose using it as a baseline or starting point for more complex graph-based detection methods. Ref. [74] developed an algorithmic approach to identify distinct behavioral patterns between real and fake X users. Their method focuses on extracting Relaxed Functional Dependencies to differentiate between human and bot accounts. The researchers posit that the complex patterns exhibited by humans are inherently difficult for bots to replicate. Ref. [75] introduced a proactive method to identify Sybil accounts during their creation. By comparing private user data and images, their framework can prevent these fraudulent accounts from being established. Additionally, ref. [76] developed a model that represents social media users based on their behavior and posting patterns. This model, utilizing a CNN-LSTM algorithm, was employed to distinguish between human and bot accounts on X. * **Content based** Numerous studies have focused on content analysis and textual information to detect X bots. For instance, ref. [77] employed deep learning to identify bots using a single tweet and six account features.