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All Journal IJIIS: International Journal of Informatics and Information Systems Journal of Applied Data Sciences International Journal for Applied Information Management
El Emary, Ibrahiem M. M.
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Environmental Science Social Sciences

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Journal : Journal of Applied Data Sciences

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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

Abstract

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

Abstract

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.
Co-Authors Abdullah, Manal Alhebbi, Mohammed Ahmed Berlilana Berlilana Sanyour, Rawan Taqwa Hariguna Yaghi, Khalil A.