## 6 X Security: ML/DL Solutions
### Detection of Security Content
Researchers are trying to define the features of tweets and use suitable algorithms to solve the problems they are researching. Ref. [153] show a framework for classifying OSINT data into cybersecurity-related to be introduced and analyzed, and the accuracy of those data was subsequently improved using an unsupervised method. Ref. [154] conducted a study where they utilized supervised classification methods to identify spammers on X. Data were gathered from Tweepy API and consisted of 2798 accounts in the training group and 578 accounts in the testing group. Eighteen features were extracted from user profiles. Extreme Machine Learning (EML) showed the highest accuracy at 87.5%. Ref. [155] utilized ML to identify fraudulent X accounts. They employed several ML algorithms, including SVM, LR, RF, and KNN, and considered six account metadata features: likes, language code, sex code, status count, friends count, followers count, and favorites count. To enhance the accuracy of these algorithms, they applied two different normalization techniques: Z-score and Min-Max. Through their method, they achieved impressive accuracy levels of 98% for both the RF and KNN models. Ref. [156] suggested three effective techniques for identifying fraudulent accounts in their study. The classification algorithms  
Figure 9: DeepNN BiLSTM architecture, adapted from [118]. employed included Linear and Radial SVM, RF, and KNN. The dataset utilized contained 3964 entries. RF yielded more reliable predictions by resolving the overfitting issue. The RF's K-Fold Cross-Validation Scores showed a mean of 0.979812 and a standard deviation of 0.019682. By contrast, Radial SVM was unsuccessful and resulted in more false negatives. Nonetheless, using the Ensemble approach resulted in better accuracy. The study conducted by [157] aimed to distinguish live tweets as spam or ham and conduct a sentiment analysis on both live and stored tweets, categorizing them as positive, negative, or neutral. The methodology involved utilizing two datasets from Kaggle and extracting sentiment features using vectorizers like TF-IDF and BoW models. The extracted features were then input into various ML and DL classifiers. The best performing classifiers were LSTM in both spam detection with a 98.74% accuracy rate and sentiment analysis with a 73.81% accuracy rate. In a recent study by [158], a method for identifying X cyber threats was introduced. Their dataset consisted of 15.6 million tweets, with 3.2 million accounts sent during the US Elections, and they used the XGBoost algorithm to select 229 features from approximately 337 user-extracted features. The researchers trained and validated three ML models, including SVM, RF, and XGBoost, and found that XGBoost had the best performance. Their findings suggest that XGBoost is better at generalizing to new data than the other models. The study reported only a small decrease in performance, with a 2% decrease in F1-score from 0.916 to 0.896 and a 0.03% decrease in ROC-AUC from 0.98 to 0.977. Ref. [159] Identified OSN threats and recommended a double-factor authentication method using digital face-processing after entering the password using Matlab. They achieved a 95% accuracy rate by applying DL classification to a real dataset from the live webcam to train the model. They also mentioned the problem of fake accounts and its significant impact on executing spam campaigns and spreading malware and phishing attacks. The authors conducted a study on detecting fake and legitimate profiles on OSN, using two datasets from Facebook and Instagram. They applied ML algorithms such as Naive Bayes, Logistic Regression, Support Vector Machines, k-Nearest Neighbor, Boosted Tree, neural networks, SVM Kernel, and Logistic Regression Kernel, and found that SVM achieved the highest classification accuracy for the Fake Profiles detection datasets with 97.1%. Table 2 summarizes the comparisons of previous studies on the detection of cybersecurity threats in X.