Irfan Syamsuddin

A peer-reviewed journal of applied informatics that covers a broad range of topics, especially dealing with the social and business aspects of informatics.

The financial industry plays an important role in national economic growth. Because of their critical function, banks have become prime targets for numerous financial crimes. Among these, fraudulent financial transactions are regarded as a severe issue in the financial industry. Conventional approaches are frequently criticized for being ineffective in dealing with fraud in finance; therefore, machine learning approaches have a potential answer to deal with this problem. The goal of this research is to introduce a novel SMOTENN model to establish early detection of cyber fraudulent activities in financial transactions accurately. Two methods are used in this study: first, the Neural Network algorithm is applied to a dataset that contains unbalanced classes; second, the dataset is balanced using the SMOTE (Synthetic Minority Over-sampling Technique) algorithm first, followed by the Neural Network algorithm which we refer to as SMOTENN. The both models are assessed using evaluation metrics of Area Under the Curve, F1-score, precision, recall, specificity, accuracy, and processing time. The comparative analysis shows that the performance of the new SMOTENN model with a balanced dataset is significantly better than that of the neural network approach with an imbalanced dataset, implying that the new SMOTENN model is effective in detecting fraud activities in financial transactions.

This study presents COMPASS, a comparative evaluation of ten Machine Learning algorithms for DDoS attack detection using the AISED Dataset on Cloud DDoS Attacks. Feature selection was performed using SelectKBest with ANOVA F-Value, evaluating model performance across varying feature dimensions (K = 10, 15, 20, 25). Experimental results demonstrate that ensemble-based methods, particularly Random Forest, Gradient Boosting, and AdaBoost, achieve near-theoretical maximum AUC scores (>0.998) while maintaining fast training times ( 0.98) with minimal computational cost. In contrast, Support Vector Machine (SVM) and Quadratic Discriminant Analysis (QDA) show relatively lower accuracy (AUC > 0.85) and suffer from high computational complexity, with SVM requiring up to 572 seconds to train at K=25. These findings highlight the critical trade-off between classification accuracy and computational efficiency in selecting optimal models for real-time DDoS detection systems. As future work, we propose deploying a lightweight version of COMPASS on edge computing devices and integrating it into federated learning frameworks to enable collaborative, privacy preserving model training.

Purpose. This study is aimed at introducing a new hybrid machine learning model to enhance the accuracy and speed in detecting botnet attacks in Internet of Things networks. The new model is derived from an integration of decision tree algorithm and feature selection algorithms to produce a novel hybrid machine learning for better performance in IoT botnet detection. Methodology. The study adopts a six steps research methodology. It consists of dataset collection, dataset preprocessing, applying machine learning, comparing feature selection algorithms, combining both machine learning and feature selection algorithms, and finally comparing the results. Findings. A novel hybrid machine learning (ML) model called FoSDeT has been obtained as a result of combination of decision tree algorithm and feature selection algorithm called Forward Selection which shows a significant improvement in IoT botnet detection in comparison to standard decision tree model. Originality. The paper proposes a simple yet powerful hybrid approach which integrates Decision Tree algorithm with two pre-defined feature selection algorithms namely, Forward Selection and Backward Elimination. The new hybrid model called FoSDeT shows a significant enhancement in terms of IoT botnet detection. Practical value. The hybrid model obtained from this study might be used by IT security practitioners in developing real intrusion detection system for defending IoT networks from botnet attacks.

Purpose. This study aims to develop a new teaching module to illustrate the arithmetic systems of Elliptic Curve Cryptography, a powerful yet simple algorithm for information security, by exploring the capability of the Visual Basic Applications of Microsoft Excel in user friendly way. Methodology. The research is performed using research and development approach, which is divided into five steps utilizing VBA features of Microsoft Excel. It starts with modeling arithmetic in Microsoft Excel spreadsheet, then testing the validity through calculation and setup of the actual arithmetic of Elliptic Curve Cryptography using VBA Excel, before performing the test of the VBA application and finally visualizes the results in graphical mode. Findings. Novel teaching software based on of Microsoft Excel Visual Basic Applications is produced that is able to simulate arithmetic system behind Elliptic Curve Cryptography in an easy way for students. Originality. To the best of the authors’ knowledge, this is the first simulation based on Excel VBA to illustrate the arithmetic systems of Elliptic Curve Cryptography for teaching purposes. Practical value. In general, mastering cryptography will need a steep learning curve; however, using Microsoft Excel as a simulation platform will accelerate learning. The main practical value is the ease of Microsoft Excel, which will turn cryptography learning which was commonly very difficult for student to become easier and user friendly.

This paper presents our recent effort to reproduce several combination of multiboot Linux operating systems in the form of USB flash drive. The motivation behin This paper presents our recent effort to reproduce several combination of multiboot Linux operating systems in the form of USB flash drive. The motivation behin

This study presents progress in revising curriculum of the Cyber Security course by integrating international security certifications from Cisco, ECCouncil and ISACA in a vocational education. The main objective of establishing a new Cyber Security curriculum is to improve the quality of learning materials and updating its contents in order to enhance student competitive advantages at international levels. Considering the issue involves multi criteria perspectives, the study proposes the application of Ternary Analytic Hierarchy Process (Ternary AHP) technique as a novel approach in dealing with the problem. Final result suggests selecting 14 topics from Cisco, ECCouncil and ISACA for the formation of the new curriculum of Cyber Security course.