This article presents an advanced IDS that uses deep learning, specifically stacked Long Short-Term Memory (LSTM) and the CatBoost algorithm, to detect anomalies in network traffic to monitor and detect the cyber threats in real-time.
A two-tier hybrid IDS that uses a Random Forest model for quick initial detection and a Neural Network for deeper analysis of suspicious traffic is proposed that provides a balanced and efficient solution that overcomes key limitations of existing IDS models and offers a pathway towards a more robust real-time IoT intr...
Research Paper, Wong Zoey, Yu Watanabe et al.· International Journal of Eme...· 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...· 0 citations
This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models, and discusses critical challenges affecting the deployment of ML-based IDS.
Ranobir Hasan, H. Jamal, Kamal Kamal et al.· The Eastasouth Journal of In...· 0 citations
A thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles, foreseeing an all-encompassing solution to modern network security issues.
Gaurav Kishor Saxena, Shambhu Dayal Sahu· International Journal of Cre...· 0 citations
This paper presents an AI-based Intrusion Detection System that integrates network simulation, machine learning, and real-time visualization into a unified three-layer framework and demonstrates that combining simulation, machine learning, and visualization can produce a scalable and effective solution for modern netwo...
T. Senthil, V. Shanmuganeethi· International Journal for Re...· 0 citations
Experimental results demonstrate that the proposed model achieves high detection accuracy, strong discriminative capability, and low false alarm rates across both datasets, confirming its effectiveness and scalability for next-generation cybersecurity applications.