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Hybrid CNN–BiLSTM–DNN Approach for Detecting Cybersecurity Threats in IoT Networks

https://doi.org/10.3390/computers14020058

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What this work says

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A Review of the Applications of the Internet of Things (IoT) for Agricultural Automation

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A deep learning-based novel hybrid CNN-LSTM architecture for efficient detection of threats in the IoT ecosystem

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Internet of Things attack detection using hybrid Deep Learning Model

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Network anomaly detection methods in IoT environments via deep learning: A Fair comparison of performance and robustness

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IoT-PRIDS: Leveraging packet representations for intrusion detection in IoT networks

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Internet of things (IoT) applications to fight against COVID-19 pandemic

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Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset

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Enhancing IoT network security through deep learning-powered Intrusion Detection System

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A comparative evaluation of intrusion detection systems on the edge-IIoT-2022 dataset

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A lightweight SEL for attack detection in IoT/IIoT networks

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Machine Learning for DoS Attack Detection in IoT Systems

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Security Analysis of IoT Devices by Using Mobile Computing: A Systematic Literature Review

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An Empirical Study on System Level Aspects of Internet of Things (IoT)

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Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

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Security Threats and Artificial Intelligence Based Countermeasures for Internet of Things Networks: A Comprehensive Survey

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Generative Deep Learning to Detect Cyberattacks for the IoT-23 Dataset

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Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning

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CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System

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Advances in Pruning and Quantization for Natural Language Processing

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Binary and Multi-Class Classification on the IoT-23 Dataset

inferredcites →https://doi.org/10.1109/ccst.2019.8888419openalex-snapshot

Developing Realistic Distributed Denial of Service (DDoS) Attack Dataset and Taxonomy

inferredcites →https://doi.org/10.1109/cvpr.2018.00826openalex-snapshot

Towards Effective Low-Bitwidth Convolutional Neural Networks

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Generative Adversarial Network and Auto Encoder based Anomaly Detection in Distributed IoT Networks

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IoT Applications in Smart Agriculture: Issues and Challenges

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Comparative Analysis of Machine Learning Techniques for Handling Imbalance in IoT-23 Dataset for Intrusion Detection Systems

inferredcites →https://doi.org/10.1109/jiot.2019.2950469openalex-snapshot

Cost-Effective Video Summarization Using Deep CNN With Hierarchical Weighted Fusion for IoT Surveillance Networks

inferredcites →https://doi.org/10.14569/ijacsa.2021.0120670openalex-snapshot

Classification Model Evaluation Metrics

inferredcites →https://doi.org/10.32604/cmc.2023.042469openalex-snapshot

One Dimensional Conv-BiLSTM Network with Attention Mechanism for IoT Intrusion Detection

inferredcites →https://doi.org/10.32604/cmc.2024.053542openalex-snapshot

Detection of Real-Time Distributed Denial-of-Service (DDoS) Attacks on Internet of Things (IoT) Networks Using Machine Learning Algorithms

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IoT-Enabled Smart Agriculture: Architecture, Applications, and Challenges

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A Deep Learning-Based Framework for Strengthening Cybersecurity in Internet of Health Things (IoHT) Environments

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Distributed Deep Neural-Network-Based Middleware for Cyber-Attacks Detection in Smart IoT Ecosystem: A Novel Framework and Performance Evaluation Approach

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Networking Architectures and Protocols for IoT Applications in Smart Cities: Recent Developments and Perspectives

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IoT Technology Applications-Based Smart Cities: Research Analysis

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A Comprehensive Review on Internet of Things (IoT) and its Implications in the Mining Industry

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BinaryConnect: Training Deep Neural Networks with binary weights during propagations

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LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks

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Self-Evolving Middleware for Cyber Threat Detection in Smart IoT Ecosystems Using Cycle-GANs and Bio-Inspired Optimization

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DeepSenseTrack: A Predictive Threat Detection Model for IoT Ecosystems using Hybrid Data Learning

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A High-Performance Deep Learning-Based Intrusion Detection System for Accurate Identification of Sophisticated Cyberattacks in Modern Network Environments

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AI-Driven Threat Hunting in Enterprise Networks Using Hybrid CNN-LSTM Models for Anomaly Detection

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Trustworthy Adaptive AI for Real-Time Intrusion Detection in Industrial IoT Security

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A Survey of Advanced Intrusion Detection Systems Using Deep Learning in Cloud-Edge IoT Environments

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