Aug 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
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.
Abstract
The rapid development of 5G-based Industrial IoT (IIoT) networks has increased their vulnerability to cyberattacks, especially intrusion attempts and botnets. To address the need for an efficient and accurate real-time intrusion detection system, this paper presents an intelligent hybrid deep learning framework based on a combination of deep neural networks (DNNs) and random forests (RF). For the purpose of analyzing high-dimensional characteristics of IIoT data, the suggested system makes use of sophisticated data preprocessing, feature selection via the use of SelectKBest, class imbalance correction through the use of SMOTE, and deep learning methods for hidden-layer expression. The model was evaluated on a state-of-the-art and realistic IIoT cybersecurity dataset, the Edge-IIoTset, which includes 61 highly correlated features spanning 14 attack types. Experimental results show that the proposed hybrid model is more effective than traditional machine learning techniques (DT, RF, SVM, KNN) and a single DNN model. This model meets the real-time requirements of IIoT applications with 99.20% accuracy, no false alarms, and extremely low inference latency of 0.03 to 0.05 milliseconds per sample. This approach makes 5G IIoT networks more secure, faster, and more adaptable. The results help ensure the security of 5G IIoT networks for use in critical areas such as smart factories, cyber-physical systems, power grids, and industrial automation.
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
The majority of assaults in heterogeneous networks are detected by intrusion detection systems (IDS). Cyberattack kinds that seriously harm networks are difficult for conventional IDSs to detect. The majority of existing solutions rely on deep learning models, which have a significant computational and energy overhead that limits their use in IoT environments with limited resources. A lightweight IDS based on ML is proposed in this research as a solution to this difficulty. Predicting the behavior of network traffic is achieved using ToN-IoT data and a tailored preprocessing pipeline. The voting-based ensemble classifier is built through the combination of models of RF and LightGBM to enhance the stability of the classification. The standard performance measures that are utilized to evaluate the proposed approach include accuracy, precision, recall, F1score, false alarm rates, and ROC analysis. The experimental findings indicate that RF achieve 99.81% accuracy, LGBM achieve 99.83%, and the ensemble model has a high accuracy of 99.99% with very low false alarms. Comparative evaluation with traditional ML and DL models demonstrates improved detection reliability with reduced computational overhead. These results prove that the suggested architecture is both computationally efficient and practically applicable to IoT settings with limited resources. However, direct hardware-level energy measurements are required to fully quantify the energy-saving characteristics of the proposed IDS.
Abhinay Kumar Reddy Seella, Rupesh Shirke, Vijay Kumar Kasuba et al.· International Conference on...· 0 citations
In this study, a hybrid deep learning technique is proposed as the foundation for a real-time network intrusion detection system (i.e., NIDS) for Internet of Things (i.e., IoT) networks. (i.e., NIDS) stands for network intrusion detection system. Learning spatial information is accomplished using convolutional neural networks (i.e., CNNs), while modeling temporal information in network traffic patterns with long short-term memory (i.e., LSTMs) is also used. It is possible to create the architecture in such a way that it is both lightweight and efficient, taking into consideration the limitations of Internet of Things devices, such as the restricted processing power and communication protocols that they must support. Using the NSL-KDD benchmark dataset, the proposed model achieves a detection accuracy of 98.7%, while using the UNSW-NB15 benchmark dataset, it achieves 97.3%, and using the CIC-IDS-2017 benchmark dataset, it achieves 98.1%. Furthermore, the inference time for each packet is less than 2.5 milliseconds. It has been demonstrated through the findings that the CNN-LSTM hybrid model possesses superior detection performance in comparison to conventional machine learning classifiers and single-architecture deep learning models when it comes to identifying both known and novel attacks for various Internet of Things network topologies.
Obaida M. Al-hazaimeh, A. Abu-Ein· WSEAS Transactions on Comput...· 0 citations
LSTM had good detection for frequent attacks and slow-changing patterns, which shows its capacity in learning long-lasting dependencies, which shows its capacity in learning long-lasting dependencies.
Jawad Hussain Awan, Misbah Safdar, Muhammad Ayaz Shirazi et al.· Italian National Conference...· 0 citations
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 intrusion detection.
Research Paper, Wong Zoey, Yu Watanabe et al.· International Journal of Eme...· 0 citations
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