Jul 2026· International Conference Computing Methodologies and Communication· pp. 635-643· 0 citations· 20 references
Abstract
In rapid development of computer networks, anomaly detection is essential to securing modern communication networks against emerging cyber-attacks, zero-day vulnerabilities, and massive malicious traffic. But conventional intrusion detection systems are plagued by high false alarms, scalability issues and low adaptability to evolving network conditions. To overcome these challenges, this research suggests an adaptive deep learning and ensemble model for scalable real-time detection of network anomalies. This approach combines CNNs for spatial feature learning, RNNs for temporal pattern recognition, and dynamically weighted ensemble learning for making reliable decisions. The model uses normalization, feature selection and class-balancing to enhance generalization. The model is tested across benchmark datasets (CICIDS2017, NSL-KDD, UNSW-NB15) under various attack scenarios. The proposed framework achieves 97.83% accuracy, 97.8% precision, 97.6% recall, 97.7% F1-score and surpasses traditional machine learning and pure deep learning approaches. Further, the model exhibits low inference times and better adaptability to concept drift. This system can be deployed in enterprise, IoT, and edge-security systems for efficient, scalable, and intelligent threat detection.
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
This study examines a one-dimensional Convolutional Neural Network and a hybrid model, investigating how both architectures can detect network attacks in binary and multiclass classification settings, and provides actionable insights for practitioners choosing between deep learning and classical approaches under real-world NIDS deployment constraints.
The rapid growth of digital communication, cloud computing, Internet of Things (IoT), software-defined networking, and edge computing has significantly increased the complexity and volume of network traffic, creating new opportunities for sophisticated cyberattacks. Traditional signature-based intrusion detection systems are highly effective against previously identified threats but often fail to recognize emerging zero-day attacks whose behavioral characteristics have not been previously observed. Consequently, anomaly-based deep learning approaches have gained considerable attention because of their capability to automatically learn complex traffic patterns and identify deviations from legitimate network behavior. This study proposes an anomaly-based deep learning model for detecting both known and zero-day attacks in heterogeneous network environments. The proposed framework integrates advanced traffic preprocessing, automated feature extraction, deep neural representation learning, adaptive anomaly scoring, and intelligent attack classification to enhance detection accuracy while minimizing false alarms. The model is designed to capture nonlinear relationships among network traffic attributes, enabling effective identification of sophisticated intrusion attempts that evade conventional security mechanisms. Furthermore, the proposed architecture emphasizes scalability, robustness, and real-time applicability for modern enterprise networks. The anticipated outcomes demonstrate improved detection performance, reduced false positive rates, enhanced generalization capability for unseen attacks, and strengthened network resilience, thereby providing an effective intelligent cybersecurity solution for next-generation network intrusion detection systems.
Aswathy N. Rajan· Journal of Intelligent Decis...· 0 citations
ShieldDRLNet is a hybrid deep reinforcement learning framework for proactive cloud-network intrusion detection that employs a convolutional neural network and a long short-term memory encoder to obtain a spatiotemporal traffic representation and uses a Double Deep Q-Network agent for adaptive sequential decision-making.
S. Venkatramulu, Anitha Patil, K. R. Pradeep et al.· Discover Computing· 0 citations
The increasing sophistication of cyber threats demands intrusion detection systems that are
both accurate and robust. While deep learning models have shown promise in network
intrusion detection, individual architectures often fail to capture the full complexity of network
traffic patterns. This paper presents a hard-voting ensemble approach that combines three
distinct deep learning architectures—Dense Neural Network (DNN), Long Short-Term Memory
(LSTM), and Convolutional Neural Network (CNN)—to improve intrusion detection
performance on the UNSW-NB15 dataset. Each base model is designed to capture
complementary characteristics of network traffic: the DNN learns complex feature
interactions, the LSTM captures temporal dependencies, and the CNN extracts spatial patterns.
The ensemble combines their predictions through majority voting. Experimental results
demonstrate that the proposed ensemble achieves an accuracy of 85.01%, outperforming
individual models (DNN: 84.54%, CNN: 84.55%, LSTM: 82.95%). The ensemble also achieves
precision of 84.38%, recall of 85.01%, and an F1-score of 81.67%. A confusion matrix analysis
reveals balanced performance across nine attack categories. These findings establish that
ensemble methods, particularly hard voting, offer a practical pathway toward more reliable
network intrusion detection systems.
Godspower Oraye· International Journal of Com...· 0 citations
An Enhanced Multi-Model Ensemble Network Intrusion Detection System (EME-NIDS), a deep meta-learning system that combines five different heterogeneous learning paradigms, including Convolutional Neural Networks, Dense Neural Networks, Transformers, XGBoost, and Random Forests is introduced.
