## 6 X Security: ML/DL Solutions
### Detection of Vulnerabilities and Exploits on X  
Vulnerabilities and exploits are problematic security weaknesses. Vulnerabilities are typically found within software systems, while exploits arise because of these vulnerabilities. In other words, exploits are the actual manifestation of the vulnerabilities within software systems. To better understand these weaknesses, further research is necessary to identify the root cause of these security issues and develop effective mitigation strategies [131]. To prioritize the protection of systems, this section examines the marginal variance between various weaknesses concepts. The scope of this investigation is centered on utilizing X data as a source of information to identify any new vulnerabilities or to assess the presence of exploits targeting known vulnerabilities. Given that most security breaches are subject to temporal constraints, it is imperative to have effective mechanisms in place for detecting such incidents in a timely manner. By doing so, it becomes possible to prioritize the allocation of resources towards rectifying the vulnerability, thereby saving valuable time and effort [132]. Researchers utilize common vulnerability and exposure identifier (CVE-ID) as a feature to predict the likelihood of exploitation for known vulnerabilities. A novel approach was presented by [133] for the generation of early warnings for real-world exploits against known vulnerabilities. The prediction is grounded in an analysis of tweets that mention these vulnerabilities, along with their associated CVE-IDs, in the context of malicious intent. To achieve this, they utilized X's Streaming API to monitor occurrences of the keyword \"CVE\". Additionally, they employed the SVM algorithm, a supervised machine learning technique, to develop a classifier that leverages user and tweet-related features to identify emerging cyber attacks. The results of this approach demonstrated superiority over the commonly recommended vulnerability scoring system (CVSS), with a reduced rate of false positives. Furthermore, the method was capable of \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Year** & **Focus Area** & **Techniques/Models** & **Results** \\ \hline \multirow{4}{*}{[127]} & \multirow{4}{*}{2024} & \multirow{4}{*}{\begin{tabular}{c} Real-time public \\ opinion analysis \\ \end{tabular} } & \begin{tabular}{c} Naive Bayes, Decision Trees, \\ Random Forest, Logistic \\ Regression, RNN, LSTM, \\ GRU, ensemble of ML and \\ DL models \\ \end{tabular} & \begin{tabular}{c} Comparative analysis of ML \\ and DL models; novel \\ ensemble approach \\ combining ML and DL \\ \end{tabular} \\ \hline \multirow{4}{*}{[128]} & \multirow{4}{*}{2024} & \multirow{4}{*}{Aggressive tweet detection} & Stacking ensemble, Decision \\  & & Trees, Random Forest, Linear \\  & & SVC, Logistic Regression, \\  & & k-Nearest Neighbors \\ \hline \multirow{4}{*}{[129]} & \multirow{4}{*}{2024} & \multirow{4}{*}{\begin{tabular}{c} Cyberbullying detection \\ in tweets \\ \end{tabular} } & \begin{tabular}{c} 1D-CNN, Maximum Entropy, \\ Unigram, Bigram, Trigram, \\ N-gram \\ \end{tabular} & \begin{tabular}{c} 96.1\% accuracy, 93.6\% \\ precision, 73.7\% recall, \\ 83.8\% F1-score \\ \end{tabular} \\ \hline \multirow{4}{*}{[130]} & \multirow{4}{*}{2024} & \multirow{4}{*}{\begin{tabular}{c} Cyber attack detection in \\ IIoT environments \\ \end{tabular} } & \
\begin{tabular}{c} IRSO algorithm, Deep Belief \\ Network (DBN), BiGRU, \\ Autoencoder (AE), Modified \\ Gray Wolf Optimizer \\ (MGWO) \\ \end{tabular} &
\begin{tabular}{c} Superior performance in IIoT \\ cybersecurity, effective \\ feature selection and \\ hyperparameter tuning \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._detecting exploits with a median lead time of two days ahead of existing datasets. Ref. [134] proposed a method of utilizing social media analysis for software vulnerability monitoring in the HANA (SMASH) architecture.