ACO-Enhanced DCNN-BiLSTM framework for intrusion detection in smart Consumer Electronics network
- 1Department of AI and software, Gachon University, Seongnam, South Korea
- 2Department of Computer Science, University of Craiova, Craiova, Romania
- 3Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India
Abstract
Consumer Electronics (CE) devices, such as smartwatches, cameras, and smart home appliances, are becoming increasingly interconnected through Smart CE networks supported by Internet of Things (IoT) ecosystems and next-generation wireless networks. This ubiquitous connectivity enhances user convenience and enables intelligent services, but it also widens the attack surface by exposing resource-constrained devices to cyber threats. Although many CE devices constantly communicate with edge or cloud infrastructures, compromising a single vulnerable node can spread risk throughout the Smart CE network and jeopardize user privacy. Intrusion Detection Systems (IDS) are commonly used security systems for detecting threats and vulnerabilities in consumer devices. Although several IDS techniques have been developed in recent years, the Smart CE network environment still requires a real-time, highly accurate attack-detection solution to address its ever-changing, large-scale security concerns. In this paper, we propose a hybrid intrusion detection framework for securing smart CE network. The proposed model draws on the strengths and capabilities of multiple deep learning algorithms. Specifically, the proposed model combines a Deep Convolutional Neural Network (DCNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network to accurately recognize threats. In addition, we use the Ant Colony Optimization (ACO) approach to extract informative and uncorrelated attributes. An attention layer is added to improve discriminative learning by emphasizing the most important representations. The proposed framework was evaluated using the UNSW-NB15 dataset. The experimental results show that the proposed framework is more accurate than the existing techniques.
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BibTeX
@article{chandroth2026acoenhanced,
title = {{ACO-Enhanced DCNN-BiLSTM framework for intrusion detection in smart Consumer Electronics network}},
author = {Jisi Chandroth and Gabriel Stoian and D.Jude Hemanth},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0342949},
url = {https://doi.org/10.1371/journal.pone.0342949},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - ACO-Enhanced DCNN-BiLSTM framework for intrusion detection in smart Consumer Electronics network
AU - Jisi Chandroth
AU - Gabriel Stoian
AU - D.Jude Hemanth
PY - 2026
DA - 2026/09/30
JO - PLOS One
DO - 10.1371/journal.pone.0342949
UR - https://doi.org/10.1371/journal.pone.0342949
AB - Consumer Electronics (CE) devices, such as smartwatches, cameras, and smart home appliances, are becoming increasingly interconnected through Smart CE networks supported by Internet of Things (IoT) ecosystems and next-generation wireless networks. This ubiquitous connectivity enhances user convenience and enables intelligent services, but it also widens the attack surface by exposing resource-constrained devices to cyber threats. Although many CE devices constantly communicate with edge or cloud infrastructures, compromising a single vulnerable node can spread risk throughout the Smart CE network and jeopardize user privacy. Intrusion Detection Systems (IDS) are commonly used security systems for detecting threats and vulnerabilities in consumer devices. Although several IDS techniques have been developed in recent years, the Smart CE network environment still requires a real-time, highly accurate attack-detection solution to address its ever-changing, large-scale security concerns. In this paper, we propose a hybrid intrusion detection framework for securing smart CE network. The proposed model draws on the strengths and capabilities of multiple deep learning algorithms. Specifically, the proposed model combines a Deep Convolutional Neural Network (DCNN) and a Bidirectional Long Short-Term Memory (BiLSTM) network to accurately recognize threats. In addition, we use the Ant Colony Optimization (ACO) approach to extract informative and uncorrelated attributes. An attention layer is added to improve discriminative learning by emphasizing the most important representations. The proposed framework was evaluated using the UNSW-NB15 dataset. The experimental results show that the proposed framework is more accurate than the existing techniques.
SN - 1932-6203
ER -