Jul 2026· Journal of The Institution of Engineers (India) Series B· Vol 107, pp. 1985 - 1993· 0 citations· 16 references
TL;DR
The proposed BAT framework achieves superior classification accuracy of 96%, effectively combining feature-level attention with gradient-boosted ensemble learning to improve detection precision, reduce manual feature engineering, and handle diverse attack categories including DoS, Probe, R2L, and U2R.
The findings indicate that the RF–SVM hybrid model provides an effective and scalable solution for real-time intrusion detection in modern cybersecurity environments.
Esther J., Grace Phiri, Arockia Venice J.· International Journal of Dat...· 0 citations
An OFLN by addition of PSO to perform automated hyperparameter optimization and MAML to enable high-speed adaptation to new and previously seen attacks is proposed, which is more effective than baseline ensemble models in detection accuracy, resistance to class imbalance and training efficiency.
Results highlight that the ensemble tree based methods are still highly effective for the structured network traffic classification cases, and a Deep learning-based approach presents an even more competitive solution with the task of feature selection through raw data, which opens doors of opportunity to extract featur...
Dhilleswari Palli, Jyothi Musireddy, V. P· International Journal of Res...· 0 citations
Investigation of deep learning models for binary network intrusion detection using the NSL-KDD benchmark dataset indicates that carefully designed standalone architectures can match or exceed the performance of more complex hybrid and ensemble models for binary intrusion detection, while incurring substantially lower c...
Ketki Naik, Sanjeev Ghosh· International Journal for Re...· 0 citations
The findings indicate that hybrid deep learning techniques can improve network security by enhancing intrusion detection capability while reducing false alarms.
A. O. Jimoh-Mahmud, Abubakar Dayyabu, Abubakar Sadiq Idris et al.· FUDMA Journal of Sciences· 0 citations
A comprehensive overview of intrusion detection systems (IDS), including the following topics: fundamentals, kinds of IDS, methods for detecting intrusions in NIDS, the architecture of IDS, data pre-processing, and examples of ML techniques used in NIDS.
Madhav Sharma· International Journal of Cyb...· 1 citation
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
