Research Article
Improving Anomaly Detection Accuracy Using Fuzzy Cuckoo-Inspired Clustering and Optimization Techniques
Issue:
Volume 11, Issue 2, December 2026
Pages:
63-75
Received:
16 February 2026
Accepted:
1 June 2026
Published:
22 July 2026
Abstract: Anomaly detection is a critical task for identifying unusual patterns that may indicate security breaches, fraudulent transactions, or system failures in various application domains. Conventional anomaly detection techniques often experience high false positive rates and limited adaptability when handling complex, uncertain, or evolving datasets. To overcome these limitations, this study proposes a novel Fuzzy Cuckoo-Based Clustering Technique (F-CBCT) that integrates fuzzy logic with cuckoo search-based clustering and optimization. The proposed framework employs a decision tree classifier enhanced with fuzzy membership functions, enabling effective management of uncertainty during classification. Model parameters are optimized using a hybrid strategy based on Mean Square Error (MSE) and the Silhouette Index, improving clustering quality and classification accuracy. Experimental evaluations conducted on benchmark datasets demonstrate the effectiveness of the proposed approach, achieving a 96.86% detection rate, 97.77% accuracy, a 1.297% false positive rate, and an F-measure of 98.30%. Comparative analysis with existing state-of-the-art anomaly detection methods confirms that F-CBCT consistently outperforms conventional approaches in terms of detection capability, robustness, and reliability. The proposed technique effectively reduces false alarms while maintaining high detection performance, making it a promising solution for real-world anomaly detection applications across diverse and dynamic environments.
Abstract: Anomaly detection is a critical task for identifying unusual patterns that may indicate security breaches, fraudulent transactions, or system failures in various application domains. Conventional anomaly detection techniques often experience high false positive rates and limited adaptability when handling complex, uncertain, or evolving datasets. T...
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Research Article
Investigation on Machine Learning Models for Predicting Diabetes Risk in Indian Populations
Gajendra Singh*
Issue:
Volume 11, Issue 2, December 2026
Pages:
76-84
Received:
8 July 2026
Accepted:
6 August 2026
Published:
2 September 2026
Abstract: Background: Diabetes mellitus is a major public health concern in India, with increasing prevalence driven by demographic, metabolic, behavioral, and lifestyle-related factors. Early identification of individuals at high risk of diabetes can support timely prevention and improve health outcomes. Machine learning (ML) approaches offer opportunities to identify complex and nonlinear relationships among multiple risk factors and may complement conventional statistical approaches. Objective: This study aimed to investigate and compare the performance of different ML models for predicting diabetes risk, with particular emphasis on identifying the most influential predictors and assessing the potential applicability of ML-based approaches for early risk stratification in Indian populations. Methods: A cross-sectional analytical approach was used to evaluate demographic, physiological, and lifestyle-related variables associated with diabetes risk. The study considered variables including age, body mass index (BMI), blood glucose, blood pressure, insulin, family history of diabetes, and physical activity. Data preprocessing included missing-value management, outlier identification, categorical encoding, and feature standardization. The dataset was divided into training (70%) and testing (30%) subsets using stratified sampling. Seven supervised ML algorithms—Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network—were compared. Model performance was assessed using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). Pearson correlation, one-way ANOVA, and multivariate logistic regression were additionally used to examine associations between predictors and diabetes risk. Results: XGBoost demonstrated the strongest overall predictive performance, achieving an accuracy of 89.2%, precision of 0.88, recall of 0.87, F1-score of 0.87, and AUC of 0.93. Random Forest and the neural network also demonstrated strong performance, with accuracy of 87.6% and 88.5% and AUCs of 0.91 and 0.92, respectively. Glucose level was the most influential predictor, followed by BMI and age. Statistical analyses further supported the importance of these metabolic and demographic factors in diabetes risk prediction. Conclusion: Ensemble and advanced ML approaches, particularly XGBoost and Random Forest, demonstrated promising performance for diabetes risk prediction. Integrating ML with statistical analysis and interpretable AI approaches may strengthen early risk identification and support evidence-based diabetes prevention and personalized healthcare strategies in Indian populations. Further validation using larger, diverse, and multi center datasets is required before clinical implementation.
Abstract: Background: Diabetes mellitus is a major public health concern in India, with increasing prevalence driven by demographic, metabolic, behavioral, and lifestyle-related factors. Early identification of individuals at high risk of diabetes can support timely prevention and improve health outcomes. Machine learning (ML) approaches offer opportunities ...
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Research Article
Explainable Sequence-Aware Deep Learning Framework for Potential Zero-Day Attack Detection and Cross-Domain Generalization in Enterprise Network Intrusion Detection
Issue:
Volume 11, Issue 2, December 2026
Pages:
85-111
Received:
9 September 2026
Accepted:
21 September 2026
Published:
30 September 2026
DOI:
10.11648/j.mlr.20261102.13
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Abstract: Zero-day attacks remain a significant challenge in enterprise network security because their previously unseen characteristics can reduce the effectiveness of conventional signature-based intrusion detection systems. Although machine learning and deep learning have improved intrusion detection, many existing approaches are evaluated within a single dataset and often treat network traffic records as independent observations, providing limited evidence of temporal behavior and cross-domain generalization. This study proposes an Explainable Sequence-Aware Deep Learning Framework for Potential Zero-Day Attack Detection and Cross-Domain Generalization in Enterprise Network Intrusion Detection. The framework represents network traffic as overlapping sequences of 20 consecutive network-flow records and combines a one-dimensional convolutional neural network (1D-CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and four-head Multi-Head Self-Attention to learn local traffic characteristics, temporal dependencies, and informative relationships within network behavior. A shared representation supports both binary intrusion detection and multiclass attack classification, while SHAP and LIME provide global and local explanations of model decisions. CICIDS2017 serves as the source domain for model development, whereas UNSW-NB15 is maintained as an independent target domain for cross-domain evaluation. A stratified sample of 50,000 records is independently selected from each dataset, with SMOTE applied only to the CICIDS2017 training data. On the CICIDS2017 internal test set, the framework achieved 96.89% accuracy, 97.45% precision, 89.65% recall, 93.38% F1-score, 0.9961 ROC-AUC, and 0.9379 MCC for binary detection, while multiclass classification achieved 97.0% accuracy and 96.8% F1-score. On the independent UNSW-NB15 test set, binary detection achieved 87.70% accuracy and 90.56% F1-score, while multiclass detection achieved 80.35% accuracy and 59.57% F1-score. The findings demonstrate strong in-domain learning and useful cross-domain detection capability without retraining or fine-tuning. The cross-domain results also revealed the difficulty of transferring learned representations across different network environments. In this study, cross-domain evaluation is used to assess potential zero-day detection capability rather than to claim detection of a specifically verified zero-day attack.
Abstract: Zero-day attacks remain a significant challenge in enterprise network security because their previously unseen characteristics can reduce the effectiveness of conventional signature-based intrusion detection systems. Although machine learning and deep learning have improved intrusion detection, many existing approaches are evaluated within a single...
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