Sep 2026· International Journal of Informatics and Communication Technology (IJ-ICT)· 0 citations· 26 references
TL;DR
A hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity that achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
Abstract
The fast development of internet of things (IoT) networks has led to an increased probability of cyberattacks. Intrusion detection systems (IDS) are needed for identifying unauthorised access and malicious activities in such dynamic environments. However, existing machine learning (ML) models failed to handle the complexity and variability of modern cyber threats. In this work, a hybrid deep learning (DL)-based anomaly detection model is presented for IoT cybersecurity. The model combines three types of features: (i) supervised feature extraction using linear discriminant analysis (LDA) to extract the most discriminative features, (ii) unsupervised feature learning through autoencoders to capture latent representations of the input data, and (iii) statistical features such as mean, variance, skewness, and kurtosis to learn input characteristics. The fused feature matrix is fed into a learning based echo state network (LBESN) for final detection. The parameters of the LBESN model are tuned using black eagle optimizer (BEO). Experimental results on standard intrusion detection datasets such as UNSW-NB15, KDD99, and InSDN show that the proposed model achieves superior performance in terms of accuracy, precision, recall, and F1-score compared to conventional DL techniques.
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 Internet of Things (IoT) is rapidly being integrated into critical infrastructure sectors, such as energy, transportation, healthcare, and industry. This surge of interconnected devices dramatically expands the attack surface and increases the risk of cascading system failures and data breaches. To address these emerging threats, this work proposes an intrusion detection system (IDS) for IoT networks that incorporates machine learning techniques, considering side-channel (power) and network traffic features. We collected power consumption traces and network metrics from IoT devices during normal operation and under diverse cyberattacks (e.g., cryptomining, flooding, port scanning). Time-series machine learning classifiers are trained on this hybrid dataset to differentiate benign versus malicious behavior. The experimental results show that the combined-feature model significantly outperforms models using only one data type, achieving high detection accuracy (F1≈0.89) and correctly identifying the attack type. The resulting IDS generalizes to previously unseen attacks, demonstrating robust, adaptive defense capabilities. The novelty of our approach lies in integrating physical side-channel signals into an automated ML framework, enhancing robustness and resilience. This smart, data-driven solution operates in near real time and helps build autonomous, constantly evolving defenses against cyber threats. Overall, our study delivers a state-of-the-art ML-based tool that learns and evolves to counter modern IoT cyberattacks.
Felipe Lemus-Prieto, Alejandro Domínguez Campos, José-Luis González-Sánchez et al.· Electronics· 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
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
The Hybrid Autoencoder–TabTransformer framework provides an effective intrusion detection solution that demonstrates strong performance under the evaluated experimental conditions and comparative analysis with existing deep learning‐based intrusion detection approaches confirms the superior and balanced performance of the proposed method.
Rui Guo, Guangjun Wen· Transactions on Emerging Tel...· 0 citations
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.
Rajesh Yadav, Dinesh Kumar, Sanjeev Kumar et al.· International journal of com...· 1 citation
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