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All Journal Journal of Computer Science and Engineering (JCSE) Scientific Journal of Computer Science
Hambali, Moshood Abiola
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Computer Science & IT

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Enhancing Credit Card Fraud Detection with Modified Binary Bat Algorithm: A Comparative Study with SVM, RF, and DT Olasupo, Yinusa Ademola; Malgwi, Musa Yusuf; Hambali, Moshood Abiola
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Numerous studies have revealed the problem of irrelevant features, noise, and dimensionality in a dataset, which can inhibit how the classification algorithm performs. In machine learning, feature selection approaches are critical, particularly in the context of credit card fraud detection, where relevant feature selection is critical. We use techniques such as machine learning algorithms, data mining techniques, and data science to stop and detect credit card fraud. These algorithms often classify genuine and fraudulent transactions in credit card datasets. However, the challenge of high dimensionality and irrelevant features persists, hindering improvements in classifier algorithms. This study centered on detecting credit card fraud (CCF) using a Modified Binary Bat Algorithm (MBBA) for feature selection. The MBBA selects the most informative features to improve the classifier algorithm's performance. The classifier algorithms used in this research are Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT). We conducted the experiment using the Python programming language, and the results indicate that RF achieves 99.945% accuracy, SVM 99.847%, and DT 99.909%. As a result, RF has the best accuracy. In summary, the optimal performance of a classification algorithm depends on the selection of relevant features for credit card fraud detection. The paper suggests improving the effectiveness of classifier algorithms for credit card fraud detection by employing the Modified Binary Bat algorithm, which outperforms the Genetic Algorithm (GA) in feature selection
Enhance Facial Biometric Template Security using Advance Encryption Standard with Least Significant Bit D, Gbolagade Morufat; Hambali, Moshood Abiola; Abdulganiyu, Oluwadamilare H.; Lawrence, E
Journal of Computer Science and Engineering (JCSE) Vol 3, No 2: August (2022)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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Abstract

Security of data has been a major issue for many years which has lead to several challenges; loss of data and also allure hackers to where data are been stored. Biometric system to some extend has helped in combating these security issues, the major issues with the biometric system is how to properly secure the template been generated from hackers and unauthorized users. In this paper we combine cryptography and steganography to enhance security of biometric templates using the advance encryption standard (AES) and the least significant bit (LSB) technique to encrypt these templates and secure it from hackers, Cryptography and Steganography are the two widely employed techniques for securing and hiding data. The center of attention of this paper is the potency of combining cryptography and stenography techniques to improve the security of biometric templates.
Heart Disease Prediction Using Principal Component Analysis and Decision Tree Algorithm Hambali, Moshood Abiola; Gbolagade, Morufat Damola; Olasupo, Yinusa Ademola
Journal of Computer Science and Engineering (JCSE) Vol 4, No 1: February (2023)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36596/jcse.v4i1.617

Abstract

Globally, cardiovascular disease is among the major diseases that lead to death. Early forecasts are crucial. Using the patient's medical record, the supervised learning algorithm for predicting heart disease at an early stage was proposed. The principal component analysis (PCA) classifier and decision tree algorithm were created to classify medical record data. To predict cardiovascular diseases, data mining was utilized. The proposed strategy improves the diagnostic efficiency of physicians. Using data received from the UCI repository, the classifier's efficacy was confirmed. PCA offers 98% precision, 100% sensitivity, and 98% accuracy. In terms of accuracy, sensitivity, and precision, the results showed that the PCA outperformed the decision tree. 
Breaking Class Imbalance Barriers in Intrusion Detection Systems: A Clustering-Based Hybrid Framework Hambali, Moshood Abiola; Bako, Nahum Zhema; Dalhatu, Mu’awuya; Ishaq, Ashraf
Scientific Journal of Computer Science Vol. 2 No. 1 (2026): June Article in Process
Publisher : PT. Teknologi Futuristik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64539/sjcs.v2i1.2026.378

Abstract

Intrusion Detection Systems (IDS) deal with issues concerning the ever-escalating level of sophistication observed within cyber threats. Nonetheless, IDS performance is deteriorated by class imbalance and excessively high-dimensional features, which cause biased classifier training towards major traffic patterns. Thus, this research introduces an innovative hybrid clustering IDS approach that utilizes MiniBatchKMeans clustering and ensemble machine learning strategies to mitigate these challenges. The suggested IDS approach utilizes the Synthetic Minority Over-sampling Technique for addressing class imbalance problems, Fast Correlation-Based Filter for reducing high-dimensional features, and Hyperopt Tree-structured Parzen Estimator for optimizing clustering and machine classifiers' parameters. Four supervised machine classifiers — Decision Tree classifier, Random Forest classifier, Extra Trees classifier, and XGBoost classifier— were trained and validated on the NSL-KDD IDS dataset. Additionally, experimental analysis indicated a superior detection accuracy for all classifiers, for which the best-optimized XGBoost classifier and best-optimized Random Forest classifier provided 99.57% and 99.51% accuracy, respectively. The proposed clustering-optimized machine IDS approach provided substantial improvements for identifying minority class attacks, along with sustainability and high generalization capabilities. The obtained outcomes support the research premise concerning the efficacy of cluster-aware sampling and ensemble optimizations for designing more balanced, accurate, and adaptive IDS systems for effectively protecting against ever-escalating real-life threats within the cyberworld.
Co-Authors Abdulganiyu, Oluwadamilare H. Bako, Nahum Zhema D, Gbolagade Morufat Dalhatu, Mu’awuya Gbolagade, Morufat Damola Ishaq, Ashraf Lawrence, E Malgwi, Musa Yusuf Olasupo, Yinusa Ademola