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Machine Learning Classifier Algorithms for Ransomware Lockbit Prediction El Emary, Ibrahiem M. M.; Yaghi, Khalil A.
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.161

Advanced virus known as ransomware has been spreading quickly in recent years, resulting in considerable financial losses for a variety of victims, including businesses, hospitals, and people. Modern host-based detection techniques need to first infect the host in order to spot abnormalities and find the malware. When the system is infected, it can already be too late because some of the assets have been exfiltrated or encrypted by the malware. On the other hand, as most ransomware families attempt to connect to command-and-control servers before to executing their damaging payloads, network-based methods can be helpful in detecting ransomware attacks. Therefore, one of the most important methods for early identification can be a detailed examination of ransomware network activity. This study presents a thorough behavioral analysis of the ransomware LockBit. In early 2022, ransomware, particularly targeting data on endpoints in Indonesia, was enough to horrify the news online. LockBit ransomware is one of the ransomwares that is particularly worrisome in Indonesia, so study is required to combat the ransomware. Static and dynamic analyses are used to study the ransomware; the former involves deciphering the portable executable (PE) file, while the latter involves actually running the ransomware. These analyses will reveal the impurity and resolve of the LockBit ransomware. Examine the running operations, the resources utilized, the network activities the ransomware performed, and the effect it had on the impacted operating system to try to build a scenario for preventative measures. The real effects of the ransomware-as-a-service (Raas) attacks conducted by the LockBit ransomware are demonstrated in this research. In this work, we describe an attribute selection-based system for identifying and avoiding ransomware that uses a variety of machine learning techniques, such as neural network-based frameworks, to classify the malware's security grade. We used a range of machine learning approaches, such as Decision Tree-DT, Random Forest-RF, Naive Bayes-NB, and Logical Regression-LR based classifiers, on a selected set of attributes for ransomware detection. The results of the study demonstrate that the Random-Forest predictor outperformed different classifiers by achieving the best accuracy, precision, recall, and F1-Score.

Enhancing Digital Marketing Strategies with Machine Learning for Analyzing Key Drivers of Online Advertising Performance Berlilana, Berlilana; Hariguna, Taqwa; El Emary, Ibrahiem M. M.
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.658

The rapid growth of digital advertising has underscored the need for data-driven strategies to optimize campaign performance. This study applies machine learning techniques to analyze online advertising data, aiming to identify key performance drivers and provide actionable insights for optimizing marketing strategies. The dataset includes metrics such as clicks, displays, costs, and revenue, which were preprocessed, analyzed, and modeled using ensemble methods, including Random Forest and Gradient Boosting. These ensemble methods were chosen for their ability to handle high-dimensional data, mitigate overfitting, and capture complex, nonlinear relationships between variables. Random Forest, with its bagging approach, enhances generalization by reducing variance, while Gradient Boosting incrementally corrects errors by focusing on hard-to-predict instances, improving overall predictive performance. Descriptive analysis revealed significant variability in campaign outcomes, with cost and user engagement emerging as primary predictors of revenue. Machine learning models demonstrated strong predictive accuracy, with Random Forest achieving 92% accuracy and an F1-score of 89%. Visualizations such as feature importance charts, correlation heatmaps, and learning curves validated the robustness of the models and highlighted key insights, including inefficiencies in cost allocation and the limited impact of certain categorical features like placement. The study emphasizes the potential of machine learning to optimize digital marketing strategies by identifying critical factors that influence campaign success. The findings provide a scalable framework for resource allocation, audience targeting, and strategic decision-making in online advertising. Future research could further enhance predictions by incorporating additional features, such as audience demographics and temporal trends, to provide deeper insights into campaign dynamics.

Assessing Sentiment in YouTube Video Content: A Title and Description Analysis Approach to Analyze User Reactions Sanyour, Rawan; Abdullah, Manal; El Emary, Ibrahiem M. M.
International Journal for Applied Information Management Vol. 4 No. 4 (2024): Regular Issue: December 2024
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijaim.v4i4.93

This study investigates the relationship between sentiment in YouTube video titles and descriptions and user engagement metrics, such as view count, like count, and comment count. The findings reveal that videos with positive sentiment generally attract higher levels of engagement, including more views, likes, and comments, while videos with negative sentiment typically receive lower interaction levels. The research emphasizes the importance of emotionally resonant content, suggesting that content creators should focus on producing videos with positive emotional tones to maximize audience interaction. Additionally, the study highlights the significance of well-crafted titles and descriptions as key drivers of engagement, as these textual elements influence viewers' initial expectations and emotional reactions. However, the study is limited to analyzing titles and descriptions, which may not fully capture the emotional tone of the video itself. Future research should incorporate the actual video content and explore additional engagement metrics, such as shares and watch time, for a more comprehensive understanding of viewer behavior. Despite these limitations, the study provides valuable insights that can guide content creators in tailoring their video content and metadata to foster greater viewer engagement and content success.

The Role of Media Literacy in Shaping Public Opinion and Political Participation in the Digital Era El Emary, Ibrahiem M. M.; Alhebbi, Mohammed Ahmed
International Journal of Informatics and Information Systems Vol 8, No 3: September 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i3.271

The rapid development of digital technology has transformed how people access, interpret, and respond to political information. This study explores the role of media literacy in shaping public opinion and political participation in the digital era. Using a descriptive qualitative approach, data were collected through in-depth interviews with ten respondents from diverse educational and social backgrounds. The findings reveal that individuals with higher media literacy demonstrate stronger critical thinking, higher awareness of information credibility, and greater involvement in political discourse and civic engagement. Conversely, those with lower media literacy are more susceptible to misinformation, hoaxes, and emotional manipulation, which can distort political perceptions and reduce participation. The study highlights that media literacy not only enhances citizens’ ability to filter and evaluate political content but also strengthens democratic quality by encouraging informed and responsible participation. It recommends that media literacy education be integrated into both formal and informal learning environments to foster critical, active, and digitally responsible citizens.

Predicting Campaign ROI Using Decision Trees and Random Forests in Digital Marketing Hayadi, B Herawan; El Emary, Ibrahiem M. M.
Journal of Digital Market and Digital Currency Vol. 1 No. 1 (2024): Regular Issue June 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v1i1.5

Digital marketing has become a cornerstone of modern business strategies, leveraging various channels and technologies to promote products and services. Measuring the Return on Investment (ROI) is crucial in evaluating the effectiveness of these marketing campaigns. This study aims to predict the ROI of digital marketing campaigns using two prominent machine learning algorithms: Decision Trees and Random Forests. The primary objective of this research is to compare the performance of Decision Trees and Random Forests in predicting the ROI of digital marketing campaigns. The study focuses on evaluating the accuracy, precision, and robustness of these models, and identifying the key features that influence ROI. The dataset used in this study comprises 200,000 rows and 16 columns, detailing various aspects of digital marketing campaigns, including campaign type, target audience, duration, and channels used. Initial Exploratory Data Analysis (EDA) identified no missing values or duplicates, ensuring a clean dataset for modeling. Data preprocessing involved feature engineering and encoding categorical variables. The models were trained and evaluated using an 80-20 split for training and testing, with cross-validation applied to ensure robustness. The Decision Tree model achieved a Mean Squared Error (MSE) of 1.0896, a Root Mean Squared Error (RMSE) of 1.0439, a Mean Absolute Error (MAE) of 0.8958, and an R2 value of -0.0781. In contrast, the Random Forest model showed superior performance with an MSE of 1.0143, an RMSE of 1.0071, an MAE of 0.8755, and an R2 value of -0.0035. Cross-validation for the Random Forest model yielded a CV MSE of 1.0035, a CV RMSE of 1.0018, and a CV R2 of -0.0039, reinforcing its robustness and accuracy. The Random Forest model's superior performance is attributed to its ability to handle complex interactions between features and its robustness against overfitting. Key predictors such as Conversion_Rate, Acquisition_Cost, and Engagement_Score were identified as significant factors influencing ROI. The study discusses the practical implications of these findings for optimizing digital marketing strategies, acknowledging the limitations of data quality and model assumptions, and suggesting directions for future research, including the integration of additional data sources and exploration of advanced machine learning techniques. This study highlights the potential of machine learning models, particularly Random Forests, in predicting the ROI of digital marketing campaigns. The findings provide valuable insights for marketers to enhance their strategies and optimize budget allocations, emphasizing the importance of predictive analytics in achieving marketing success. Future work should focus on improving model accuracy and exploring new techniques to further advance the field of marketing analytics.

Enhancing Security and Efficiency in Decentralized Smart Applications through Blockchain Machine Learning Integration Hayadi, B Herawan; El Emary, Ibrahiem M. M.
Journal of Current Research in Blockchain Vol. 1 No. 2 (2024): Regular Issue September
Publisher : Bright Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jcrb.v1i2.16

This study investigates the integration of machine learning (ML) into blockchain-based smart applications, aiming to enhance security, efficiency, and scalability. The research contributes a novel framework that combines blockchain's decentralized ledger with privacy-preserving ML techniques, addressing key challenges in data integrity and computational efficiency. The primary objective is to evaluate the performance of this integration in a simulated smart grid environment, focusing on security, processing time, energy consumption, and scalability. Our findings reveal that the integrated system significantly improves security, achieving a 98% success rate in mitigating data breaches and reducing the impact of adversarial attacks by 90%. Computational efficiency is also enhanced, with the optimized blockchain-ML configuration reducing processing time by 33% and energy consumption by 20% compared to standard blockchain setups. However, scalability remains a challenge; the system demonstrates effective scalability up to 100 nodes, beyond which transaction processing time increases by 50%, indicating the need for further optimization. The results suggest that while the integration of ML and blockchain offers substantial improvements in security and efficiency, addressing scalability and environmental impact are critical for broader application. The novelty of this research lies in its dual focus on enhancing both security and efficiency within blockchain-ML systems, providing a foundation for future advancements in decentralized intelligent applications across industries. This work contributes to the field by offering empirical data that supports the viability of blockchain-ML integration and by highlighting the areas where further research is needed to realize its full potential.

Assessing the Adoption of Metaverse Platforms: A Structural Equation Modeling Approach with Mediating Effects of Switching Costs El Emary, Ibrahiem M. M.
International Journal Research on Metaverse Vol. 1 No. 3 (2024): Regular Issue December
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijrm.v1i3.14

The adoption of Metaverse platforms, a burgeoning technological innovation, holds significant potential for transforming various sectors, yet its uptake in emerging markets like Indonesia remains underexplored. This study addresses this gap by investigating the key factors influencing the Intention to Use (IU) Metaverse platforms in Indonesia, focusing on the roles of Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Relative Advantage (RA), and the mediating effect of Switching Costs (SC). The primary objective of this research was to develop and validate a model that explains the relationships between these factors and how they collectively impact user adoption decisions. Specifically, the study aimed to understand how PE, EE, SI, and RA influence the intention to use Metaverse platforms, with SC acting as a mediator. A quantitative research design was employed, utilizing Structural Equation Modeling (SEM) with Partial Least Squares (PLS) to analyze data collected from 380 distributed questionnaires. Of these, 361 were valid and used in the analysis, providing a robust sample to examine the study’s hypotheses. Participants were surveyed on their perceptions and intentions regarding Metaverse platforms. The analysis focused on examining the direct effects of PE, EE, SI, and RA on the intention to use, as well as the indirect effects mediated by SC. The findings revealed that PE, EE, SI, and RA significantly influence the intention to adopt Metaverse platforms, with SC playing a crucial mediating role. The study underscores the importance of reducing perceived switching barriers to enhance adoption, especially in a culturally diverse market like Indonesia. These results contribute to the broader understanding of technology adoption in emerging markets and offer practical implications for developers and marketers aiming to promote Metaverse platforms. Future research should explore additional factors such as technological anxiety or perceived risk and consider longitudinal designs to capture changes in user perceptions over time. This study provides a foundational model that can guide further exploration and application of Metaverse technologies in similar contexts.

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