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An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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LAM-CIoT: Lightweight authentication mechanism in cloud-based IoT environment
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Deep Learning with Dense Random Neural Network for Detecting Attacks against IoT-connected Home Environments
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Adaptive feature selection for denial of services (DoS) attack
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Deep Learning Models for Cyber Security in IoT Networks
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ML-IDS: A machine learning approach to detect wormhole attacks in Internet of Things
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Threat analysis of IoT networks using artificial neural network intrusion detection system
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Fog Computing for the Internet of Things: Security and Privacy Issues
inferredcites →https://doi.org/10.1109/milcis.2015.7348942openalex-snapshot
UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)
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IoT Malware Network Traffic Classification using Visual Representation and Deep Learning
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A survey: Attacks on RPL and 6LoWPAN in IoT
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Designing Secure Lightweight Blockchain-Enabled RFID-Based Authentication Protocol for Supply Chains in 5G Mobile Edge Computing Environment
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Representation Learning: A Review and New Perspectives
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A few-shot deep learning approach for improved intrusion detection
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IEEE 802.11 Network Anomaly Detection and Attack Classification: A Deep Learning Approach
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Collaborative Documentation Process Model
inferredcites →https://doi.org/10.3390/electronics9122152openalex-snapshot
An Efficient Deep-Learning-Based Detection and Classification System for Cyber-Attacks in IoT Communication Networks
inferredcites →https://doi.org/10.4108/eai.3-12-2015.2262516openalex-snapshot
A Deep Learning Approach for Network Intrusion Detection System
inferredcites →https://doi.org/10.48550/arxiv.1912.13204openalex-snapshot
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms
inferredcites →https://doi.org/10.7287/peerj.preprints.1954openalex-snapshot
A review of KDD99 dataset usage in intrusion detection and machine learning between 2010 and 2015

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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GraphCWGAN-GP: A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification
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Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection
inferred← citeshttps://doi.org/10.32604/csse.2023.036735openalex-snapshot
Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment
inferred← citeshttps://doi.org/10.3389/fdata.2021.782902openalex-snapshot
Top-Down Machine Learning-Based Architecture for Cyberattacks Identification and Classification in IoT Communication Networks
inferred← citeshttps://doi.org/10.3389/fenrg.2024.1491332openalex-snapshot
Enhancing unmanned aerial vehicle and smart grid communication security using a ConvLSTM model for intrusion detection
inferred← citeshttps://doi.org/10.3390/ai3010002openalex-snapshot
Cyberattack and Fraud Detection Using Ensemble Stacking
inferred← citeshttps://doi.org/10.3390/app12105015openalex-snapshot
IoT Intrusion Detection Using Machine Learning with a Novel High Performing Feature Selection Method
inferred← citeshttps://doi.org/10.3390/app122312336openalex-snapshot
An Intrusion Detection and Classification System for IoT Traffic with Improved Data Engineering
inferred← citeshttps://doi.org/10.3390/app13063857openalex-snapshot
A Fog Computing Framework for Intrusion Detection of Energy-Based Attacks on UAV-Assisted Smart Farming
inferred← citeshttps://doi.org/10.3390/app13084956openalex-snapshot
An Analysis of Artificial Intelligence Techniques in Surveillance Video Anomaly Detection: A Comprehensive Survey
inferred← citeshttps://doi.org/10.3390/app15063036openalex-snapshot
Comprehensive Study of IoT Vulnerabilities and Countermeasures
inferred← citeshttps://doi.org/10.3390/bdcc6040126openalex-snapshot
Explaining Intrusion Detection-Based Convolutional Neural Networks Using Shapley Additive Explanations (SHAP)
inferred← citeshttps://doi.org/10.3390/electronics10060650openalex-snapshot
Cyber Attacks and Faults Discrimination in Intelligent Electronic Device-Based Energy Management Systems
inferred← citeshttps://doi.org/10.3390/electronics10161876openalex-snapshot
IoT Botnet Anomaly Detection Using Unsupervised Deep Learning
inferred← citeshttps://doi.org/10.3390/electronics10172113openalex-snapshot
High Performance Classification Model to Identify Ransomware Payments for Heterogeneous Bitcoin Networks
inferred← citeshttps://doi.org/10.3390/electronics11020198openalex-snapshot
Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review
inferred← citeshttps://doi.org/10.3390/electronics11040556openalex-snapshot
Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
inferred← citeshttps://doi.org/10.3390/electronics13050837openalex-snapshot
Bridging the Cybersecurity Gap: A Comprehensive Analysis of Threats to Power Systems, Water Storage, and Gas Network Industrial Control and Automation Systems
inferred← citeshttps://doi.org/10.3390/en14216935openalex-snapshot
Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management
inferred← citeshttps://doi.org/10.3390/info14020102openalex-snapshot
Spyware Identification for Android Systems Using Fine Trees
inferred← citeshttps://doi.org/10.3390/iot6030048openalex-snapshot
A Two-Stage Hybrid Federated Learning Framework for Privacy-Preserving IoT Anomaly Detection and Classification
inferred← citeshttps://doi.org/10.3390/jsan11010018openalex-snapshot
ELBA-IoT: An Ensemble Learning Model for Botnet Attack Detection in IoT Networks
inferred← citeshttps://doi.org/10.3390/math11194080openalex-snapshot
Optimal Deep-Learning-Based Cyberattack Detection in a Blockchain-Assisted IoT Environment
inferred← citeshttps://doi.org/10.3390/math12111720openalex-snapshot
Reinforcing Network Security: Network Attack Detection Using Random Grove Blend in Weighted MLP Layers
inferred← citeshttps://doi.org/10.3390/s21217070openalex-snapshot
Intelligent Techniques for Detecting Network Attacks: Review and Research Directions
inferred← citeshttps://doi.org/10.3390/s22010241openalex-snapshot
Attack-Aware IoT Network Traffic Routing Leveraging Ensemble Learning
inferred← citeshttps://doi.org/10.3390/s22166164openalex-snapshot
Examining the Suitability of NetFlow Features in Detecting IoT Network Intrusions
inferred← citeshttps://doi.org/10.3390/s22218417openalex-snapshot
Intrusion Detection in IoT Using Deep Learning
inferred← citeshttps://doi.org/10.3390/s23073489openalex-snapshot
A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach
inferred← citeshttps://doi.org/10.3390/s23125562openalex-snapshot
Plant and Salamander Inspired Network Attack Detection and Data Recovery Model
inferred← citeshttps://doi.org/10.3390/s23146302openalex-snapshot
Unmasking Cybercrime with Artificial-Intelligence-Driven Cybersecurity Analytics
inferred← citeshttps://doi.org/10.3390/s23167135openalex-snapshot
Dynamic Adaptation Attack Detection Model for a Distributed Multi-Access Edge Computing Smart City
inferred← citeshttps://doi.org/10.3390/s23167207openalex-snapshot
Binary Hunter–Prey Optimization with Machine Learning—Based Cybersecurity Solution on Internet of Things Environment
inferred← citeshttps://doi.org/10.3390/s24175834openalex-snapshot
FL-DSFA: Securing RPL-Based IoT Networks against Selective Forwarding Attacks Using Federated Learning
inferred← citeshttps://doi.org/10.3390/s25020580openalex-snapshot
Intelligent Intrusion Detection System Against Various Attacks Based on a Hybrid Deep Learning Algorithm
inferred← citeshttps://doi.org/10.3390/sym14071450openalex-snapshot
Symmetrical Simulation Scheme for Anomaly Detection in Autonomous Vehicles Based on LSTM Model
inferred← citeshttps://doi.org/10.3934/math.2024331openalex-snapshot
Integrating Ebola optimization search algorithm for enhanced deep learning-based ransomware detection in Internet of Things security
inferred← citeshttps://doi.org/10.4018/978-1-6684-6275-1.ch006openalex-snapshot
Approaches for Detecting and Predicting Attacks Based on Deep and Reinforcement Learning to Improve Information Security
inferred← citeshttps://doi.org/10.4018/978-1-6684-8218-6.ch006openalex-snapshot
Introduction to Ransomware
inferred← citeshttps://doi.org/10.4018/978-1-7998-6975-7.ch007openalex-snapshot
Advancing Artificial Intelligence-Enabled Cybersecurity for the Internet of Things
inferred← citeshttps://doi.org/10.4018/979-8-3373-3326-7.ch008openalex-snapshot
Protecting Data of Power Stations in Smart Cities Using Quantum-Resistant and Zero-Knowledge Proof
inferred← citeshttps://doi.org/10.4018/979-8-3693-3451-5.ch010openalex-snapshot
Supply Chain Security, Technological Advancements, and Future Trends
inferred← citeshttps://doi.org/10.4018/ijswis.383062openalex-snapshot
Boosting Intrusion Detection Against DDoS Attacks Using a Feature Engineering-Based Fine-Tuned XGBoost Model
inferred← citeshttps://doi.org/10.46253/jnacs.v7i2.a2openalex-snapshot
Enhancing Network Availability using Firewall Clustering
inferred← citeshttps://doi.org/10.47533/2023.1606-146x.40openalex-snapshot
Classification of cyber threats for internet of things
inferred← citeshttps://doi.org/10.52167/1609-1817-2022-122-3-195-202openalex-snapshot
АКТУАЛЬНЫЕ КИБЕРУГРОЗЫ ДЛЯ IOT УСТРОЙСТВ УМНОГО ДОМА
inferred← citeshttps://doi.org/10.5220/0012210300003598openalex-snapshot
Explainable Intrusion Detection for Internet of Medical Things
inferred← citeshttps://doi.org/10.53730/ijhs.v6ns4.9457openalex-snapshot
DDoS attacks in cloud environment
inferred← citeshttps://doi.org/10.55041/ijsrem39264openalex-snapshot
Machine Learning Algorithm as a Firewall Decision and Reinforcement in Market Segmentation and Big Data
inferred← citeshttps://doi.org/10.56770/pst2025124openalex-snapshot
Effectiveness of predictive neural network models in improving cyberattack detection and vulnerability assessment in critical infrastructure systems
inferred← citeshttps://doi.org/10.5815/ijwmt.2023.04.01openalex-snapshot
Adversarial Deep Learning in Anomaly based Intrusion Detection Systems for IoT Environments
inferred← citeshttps://doi.org/10.7717/peerj-cs.1793openalex-snapshot
Evaluating deep learning variants for cyber-attacks detection and multi-class classification in IoT networks
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