Predictive Threat Intelligence Using Machine Learning

The number and sophistication of cyberattacks have dramatically expanded due to the quick development of digital technology, cloud computing, and Internet of Things (IoT) devices. Conventional intrusion detection systems are ineffective against zero-day and dynamic threats since they primarily rely on predetermined signatures and criteria. By examining both previous and current data, predictive threat intelligence (PTI) aims to foresee cyberattacks before they happen. Intelligent methods for extracting hidden patterns from massive network traffic are offered by machine learning (ML). Using the UNSW NB15 dataset, this study suggests a predictive threat intelligence system based on machine learning. A number of supervised learning methods are used and assessed, such as Random Forest, Artificial Neural Networks, Support Vector Machine, Logistic Regression, and EnXGBoost. According to experimental results, EnXGBoost performs the best, achieving an accuracy of 93.5% for multi-class classification and 98.7% for binary classification. In contemporary networks, the suggested approach facilitates real-time deployment, lowers false alarms, and improves proactive security.

Predictive Threat Intelligence, Machine Learning

1. Int. J. Adv. Sci. Res. Eng., 2024, N. G. Anoh et al., "IoT Intrusion Detection System based on Machine Learning Algorithms utilizing the UNSW- NB15 dataset." [Google Scholar] [Crossref]

2. S. More et al., "UnSW-NB15 Data Analysis Improves Intrusion Detection Performance," Algorithms, 2024. System [Google Scholar] [Crossref]

3. A. Al-Obaidi et al., "Effectiveness of ML Techniques to Detect Nine Attacks using UNSW NB15," MMEP, 2023. [Google Scholar] [Crossref]

4. "Intrusion Detection: A Comparison Study of Machine Learning Models Using Unbalanced Dataset," SN Comput. Sci., 2024. [Google Scholar] [Crossref]

5. "IGRF-RFE: Hybrid Feature Selection for MLP- based IDS on UNSW-NB15," by Y. Yin et al. Big Data Journal, 2023. [Google Scholar] [Crossref]

6. ML-based network intrusion detection for large and unbalanced data using oversampling and stacking, M. A. Talukder et al., 2024. [Google Scholar] [Crossref]

7. M. Karthi and colleagues, "The UNSW-NB15 Dataset: Network Intrusion Detection Using Stacked Machine Learning Models," 2025. [Google Scholar] [Crossref]

8. UNSW-NB15 Dataset,” 2025. [8] A. K. Dasari et al., “Stacked Ensemble Learning Technique for NIDS,” Eng. Res. Express, 2025. [Google Scholar] [Crossref]

9. "Comparative Analysis of NSL-KDD and UNSW-NB15 Using Deep Learning Resampling Methods," N. Dangol, 2025. [Google Scholar] [Crossref]

10. M. Luqman et al., "Intelligent Parameter- Based In-Network IDS for IoT utilizing UNSW NB15," Journal of Franklin Institute, 2025. [Google Scholar] [Crossref]

11. J. Biradar and colleagues, "Attention Augme [Google Scholar] [Crossref]

12. F. S. Alrayes et al., “Adaptive IDS using Model-Agnostic Meta-Learning on UNSW NB15,” Sensors, 2025 [Google Scholar] [Crossref]

13. Z. P. Putra, “Evaluating Classification Algorithms on the UNSW-NB15 Dataset,” 2024. [Google Scholar] [Crossref]

14. I. H. Putro, “Performance Evaluation of ML Classifiers for Network Intrusion Detection,” 2025. [Google Scholar] [Crossref]

15. T. P. J. Swaroop et al., “Enhanced IDSSVM and Random Forest applied to the UNSW- NB15 dataset, 2025. [Google Scholar] [Crossref]

16. S. Fatima, P. Upadhyay, P. Sharma, S. K K. Khadagade, S. Bihari and R . Tiwari, “Hybrid Machine Learning Framework for Intelligent Attack Detection in VANET Environments “2025 International Conference on Data, Energy and Communication Networks (DECoN), Bhopal, India,2025. [Google Scholar] [Crossref]

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