Jul 2026· International journal of computer information systems and industrial management applications· 1 citation
TL;DR
A Hybrid Deep Learning and Machine Learning (DL-ML) framework for intelligent cyber threat detection that fuses a Convolutional Neural Network combined with a Bidirectional Long Short-Term Memory branch with a gradient-boosted ensemble branch that produces a unified threat classification and severity score is proposed.
Abstract
The rapid expansion of smart networks, encompassing the Internet of Things (IoT), software-defined networking (SDN), and 5G-enabled edge infrastructure, has dramatically increased the attack surface available to malicious actors, while simultaneously producing high-velocity, heterogeneous traffic that traditional signature-based intrusion detection systems struggle to analyze in real time. This paper proposes a Hybrid Deep Learning and Machine Learning (DL-ML) framework for intelligent cyber threat detection that fuses a Convolutional Neural Network combined with a Bidirectional Long Short-Term Memory (CNN-BiLSTM) branch, which captures spatial and temporal traffic patterns, with a gradient-boosted ensemble branch (XGBoost/Random Forest), which captures statistical flow-level signatures. The outputs of both branches are combined through a weighted feature-fusion and ensemble layer that produces a unified threat classification and severity score. The framework was evaluated on a large-scale smart-network intrusion dataset comprising over 1.8 million labeled flow records spanning six traffic classes: normal, DDoS, botnet, port scanning, malware communication, and spoofing. Experimental results show that the proposed hybrid model achieves 98.8% accuracy, 96.4% precision, 95.6% recall, and a 96.0% F1-score, exceeding the strongest individual baseline (LSTM) by 3.7 percentage points in F1-score and achieving an AUC of 0.992.
An intelligent cyberattack detection system that applies machine learning and deep learning techniques to classify network traffic as either normal or malicious, and demonstrates the potential of machine learningbased intrusion detection systems in improving network security and supporting the protection of modern smart environments.
KADADHARAPU ANUPRIYA, Dr.S.SWATHI RAO· International Journal of Eng...· 0 citations
The rapid growth of digital communication technologies, cloud computing, and Internet of Things (IoT) devices has
increased both the frequency and sophistication of cyber-attacks, making effective intrusion detection an essential component of
modern cybersecurity systems. Traditional signature-based intrusion detection systems (IDS) are effective against known attacks
but fail to detect previously unseen or evolving threats. This study investigates the application of deep learning models for binary
network intrusion detection using the NSL-KDD benchmark dataset. Three standalone architectures, Convolutional Neural
Networks (CNN), Long Short-Term Memory (LSTM) networks, and Deep Neural Networks (DNN), are implemented and
evaluated, alongside a CNN-LSTM Hybrid model that integrates spatial and sequential learning, and a DNN-LSTM Ensemble
model that combines independently trained DNN and LSTM predictions through weighted averaging. Following data cleaning,
categorical encoding, normalization, and Random Forest-based feature selection (41 features reduced to 20), all models were
trained and evaluated under identical conditions using Accuracy, Precision, Recall, F1-Score, ROC-AUC, training time, and
inference time. The standalone DNN model achieved the best overall performance, with 80.98% accuracy, 97.08% precision,
68.66% recall, 80.43% F1-score, and 96.11% ROC-AUC, while also requiring the shortest training time (39.69 s). The CNNLSTM Hybrid model attained the highest precision (97.23%) but did not outperform the standalone architectures overall, and
the DNN-LSTM Ensemble produced balanced but not superior results. These findings indicate 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 computational cost. The study contributes a controlled, commonframework comparison of five deep learning architectures and provides practical guidance for selecting computationally
efficient models for anomaly-based intrusion detection.
Ketki Naik, Sanjeev Ghosh· International Journal for Re...· 0 citations
An intelligent cyber threat detection framework based on a hybrid deep learning architecture that combines Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) in order to get around restrictions on conventional security solutions.
Manjali Gupta, J. Kumar, Bharti· International Journal for Re...· 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.
An intelligent hybrid deep learning framework based on a combination of deep neural networks (DNNs) and random forests (RF) to ensure the security of 5G IIoT networks for use in critical areas such as smart factories, cyber-physical systems, power grids, and industrial automation.
Rohan Rajoriya, Shweta Chouksey· International journal of com...· 0 citations
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