Aug 2026· Cluster Computing· Vol 29· 0 citations· 89 references
Computer Science
TL;DR
An innovative hybrid methodology that tackles three enduring issues in network intrusion detection: feature redundancy, class imbalance, and computational efficiency is presented, which offers a promising solution for real-time threat detection in dynamic network environments while maintaining adaptability to evolving attack vectors.
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
The proposed hybrid framework provides a robust, scalable, and reliable solution for real-time botnet attack detection, enhancing the security and resilience of modern IoT networks against evolving cyber threats.
Munagala Kusuma, M.ramesh· International Journal of Eng...· 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
This study proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (BiGRU) for efficient DDoS attack detection and demonstrates that the hybrid CNN–BiGRU architecture effectively improves detection accuracy and provides a reliable approach for in...
S. V, D. S., N. Deepti et al.· International journal of com...· 0 citations
The growth of Internet of Things (IoT) networks has drastically improved attack surface, requiring intrusion detection systems (IDS) to ensure accuracy and privacy protection. To overcome these obstacles, we introduce a federated learning (FL) based IDSW that incorporates state-of-the-art preprocessing, smart feature o...
S. Prakash, M. S. Kumar· International Journal of Inf...· 0 citations
A Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model for effective IoT malware detection is proposed, which enhances detection capability for both known and zero-day attacks.
