## 5 AI-Based Cyber Threat Solutions in X
### Ensemble and Hybrid Learning
The ensemble approach yielded a detection accuracy of 97.4% on the X dataset and 90.97% on a combined X and Facebook dataset, emphasizing its effectiveness in combating cyberbullying across platforms. Ref. [125] employed an ensemble approach to accurately classify crime-related tweets. Data were collected using the Tweepy and Twint libraries and processed with TF-IDF vectorization. The ensemble combined Logistic Regression, Support Vector Machine, k-Nearest Neighbors, Decision Tree, and Random Forest classifiers (weighted 1, 2, 1, 1, and 1, respectively) using a soft weighted Voting classifier. This methodology achieved an impressive 96.2% accuracy on the test dataset, demonstrating the effectiveness of the ensemble for crime tweet classification. Ref. [126] identify and classify spam URLs on X developed multiple models using a combination of URL content, user profile information, and hybrid features. A large X dataset was analyzed to create comprehensive feature sets for training various ensemble learning models. Our models achieved high accuracy, often exceeding 90%, particularly when using k-Nearest Neighbors within bagging and Random Forest ensembles. Results indicate that combining user profile, content, and hybrid data significantly enhances spam detection accuracy. Ref. [127] research delves into real-time public opinion by analyzing tweets across a wide range of topics, including COVID-19, crime, spam, Flipkart, migraine, and airlines. The study harnessed the X API to collect a substantial dataset of tweets, which were then meticulously cleaned and preprocessed using natural language processing (NLP) techniques. To gauge public sentiment, a comparative analysis was conducted using both traditional machine learning (ML) algorithms (Naive Bayes, Decision Trees, Random Forest, Logistic Regression) and advanced deep learning (DL) models (recurrent neural networks, Long Short-Term Memory, Gated Recurrent Units). While these models were evaluated independently, the core contribution of the research lies in a novel ensemble approach that combines ML and DL models. Ref. [128] focused on automating the detection of binary labels in aggressive tweets, a novel system has been developed, demonstrating exceptional performance relative to previous studies conducted on the same dataset. The study employed a stacking ensemble machine learning approach, integrating four distinct feature extraction techniques to enhance performance within this framework. By combining five machine learning algorithms--Decision Trees, Random Forest, Linear Support Vector Classification, Logistic Regression, and k-Nearest Neighbors--into an ensemble model, the researchers were able to achieve significantly improved results over traditional machine learning classifiers. The stacking classifier attained an impressive accuracy rate of 94.00%, which not only surpassed the performance of conventional models but also outperformed the results of earlier experiments using the identical dataset. The findingshighlighted the system's effectiveness, achieving an accuracy rate of 94.00% in correctly classifying tweets as either aggressive or non-aggressive. Ref. [129] developed a sophisticated deep learning model tailored for cyberbullying detection in tweets. Leveraging the label X_parsed_dataset.csv, the model extracted keywords and entities using Maximum Entropy. A 1D-CNN architecture was then applied to classify tweets as truculent or non-truculent. The study compared four preprocessing methods (Unigram, Bigram, Trigram, and N-gram) and achieved impressive results: 96.1% accuracy, 93.6% precision, 73.7% recall, and an F1-score of 83.8% across different evaluations. Ref. [130] research introduces a novel cybersecurity approach, IRSO-EDLCS, to bolster cyber attack detection in Industrial Internet of Things (IIoT) environments. This technique leverages an Improved Reptile Search Optimization (IRSO) algorithm for feature selection, optimizing feature relevance for enhanced detection accuracy.