The current era extensively utilizes the Internet, which uses data. Due to the apparent open-access Internet service, this data is highly vulnerable to attacks. Data privacy is affected by Web-based attacks. This Systematic Literature Review (SLR) focuses on two Web-based attacks: Man-In-The-Middle and session hijacking. It reviews about 30 studies from the years 2016–2023 that have been selected utilizing a proper study selection procedure. This SLR comprises three research questions. The first describes the overall trends in Man-In-The-Middle attacks and session hijacking studies. It shows that 7 articles were published in 2018, and the trend is decreasing to 4 articles by 2021. Moreover, 73% articles are published in conference venues, and India is the top contributor in this domain. Lastly, this question elaborated that IEEE is the top contributor as a publisher. The second addresses the sorts of attacks used by Man-In-The-Middle attacks and session hijacking on Transmission Control Protocol / Internet Protocol (TCP/IP). This demonstrates that Man-In-The-Middle attacks invade all layers and session hijacking attacks on only two, that is, the application and network layer. The third research question discusses the solutions provided by different studies to deal with Man-In-The-Middle attacks and session hijacking. In conclusion, this analysis highlights the need for stronger cybersecurity measures against Man-in-the-Middle and session hijacking assaults in the Internet era by revealing evolving trends, contributors, and solutions in data privacy.

Depression is a common and severe mental disorder that frequently goes undiagnosed and untreated, particularly during its initial phase. However, with the increasing number of people sharing their thoughts and feelings online, social media has become a valuable resource to identify symptoms of mental health problems such as depression. As a result, research on social media-based depression diagnosis has received significant interest but it mainly focuses on the semantics of posts. It cannot detect the implicit ambiguities that lies in the language of the user in an online posting. To address this limitation, a syntactical analysis of each user post is required. The goal of this research is to enhance the identification of individuals at risk for depression by analyzing their language and rhetorical relations using rhetorical structure theory. The rhetorical scores are incorporated into an ensemble machine learning model to classify social media posts as depressive or non-depressive. The model employs a combining Multilayer Perceptron, Extreme Gradient Boosting, and Support Vector Machine algorithm. Therefore we proposed a model named RSTFusionX by linking depression to a social media dataset and using a combination of the rhetorical structure theory framework along with an ensemble machine learning algorithm. To attain more precise prediction models, the research aims to better understand the association between social media language trends and depression. The results of this study are significant, as RSTFusionX achieves 97.1% accuracy, 97.4% precision, 96.7% recall, and 96.9% F1-score, exhibiting better results as compared to baseline algorithms. As a result of RSTFusionX, true positive and true negative values are increased while false positive and false negative predictions are reduced. The proposed research findings suggest that the integration of characteristics from the depressive vocabulary improves classification accuracy. The article contributes to the resources for depression detection and provides a technique for future research to further improve depression detection.

Early identification of depression risk from social media text can support large-scale screening and timely follow-up. However, posts are often emotionally complex and linguistically ambiguous, which makes robust detection challenging. This paper proposes RST-DS (Rhetorical Structure Theory-based Depression Scanning), a discourse-aware framework that integrates Rhetorical Structure Theory (RST) signals with lexical evidence for classifying posts as Depressed/Non-Depressed (D/ND). Using Reddit posts, we compute an RST-derived score capturing rhetorical relationships and coherence and fuse it with lexical features represented via Term Frequency–Inverse Document Frequency using two strategies: feature addition (+) and feature concatenation ($\left|\right|$). We evaluate K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Multilayer Perceptron (MLP), along with a soft-voting ensemble model named MLPBoostReg (an ensemble of LR, XGBoost, and MLP). Using a 5-fold cross validation, the concatenation strategy consistently outperforms the addition strategy across accuracy, precision, recall, and F1-score. The best-performing configuration, MLPBoostReg$\left|\right|$ achieves the highest accuracy, precision, recall, and F1-score of 0.970, indicating a strong balance between identifying depression-related content and limiting false alarms. These findings suggest that combining rhetorical structure with lexical evidence improves depression detection from social media text, while noting that social-media labels do not substitute for clinical diagnosis.