ReviewSensors (Basel, Switzerland)2021
IoT Intrusion Detection Taxonomy, Reference Architecture, and Analyses.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Short-Window Micro-Behavioral Triage for High-Throughput Firewall Telemetry: Limits, Features, and Operational Feasibility.Sensors (Basel, Switzerland) · 2026Article
- Enabling Design of Secure IoT Systems with Trade-Off-Aware Architectural Tactics.Sensors (Basel, Switzerland) · 2024Article
- The intelligent evaluation in ice and snow tourism based on LSTM network.Scientific reports · 2024Article
- Tides of Blockchain in IoT Cybersecurity.Sensors (Basel, Switzerland) · 2024Article
- Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection-Current Research Trends.Sensors (Basel, Switzerland) · 2024Review
- An efficient intrusion detection system for IoT security using CNN decision forest.PeerJ. Computer science · 2024Article
- A Lightweight Double-Stage Scheme to Identify Malicious DNS over HTTPS Traffic Using a Hybrid Learning Approach.Sensors (Basel, Switzerland) · 2023Article
- A lightweight intrusion detection method for IoT based on deep learning and dynamic quantization.PeerJ. Computer science · 2023Article
- Multi-Aspect Based Approach to Attack Detection in IoT Clouds.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper surveys the deep learning (DL) approaches for intrusion-detection systems (IDSs) in Internet of Things (IoT) and the associated datasets toward identifying gaps, weaknesses, and a neutral reference architecture. A comparative study of IDSs is provided, with a review of anomaly-based IDSs on DL approaches, which include supervised, unsupervised, and hybrid methods. All techniques in these three categories have essentially been used in IoT environments. To date, only a few have been used in the anomaly-based IDS for IoT. For each of these anomaly-based IDSs, the implementation of the four categories of feature(s) extraction, classification, prediction, and regression were evaluated. We studied important performance metrics and benchmark detection rates, including the requisite efficiency of the various methods. Four machine learning algorithms were evaluated for classification purposes: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and an Artificial Neural Network (ANN). Therefore, we compared each via the Receiver Operating Characteristic (ROC) curve. The study model exhibits promising outcomes for all classes of attacks. The scope of our analysis examines attacks targeting the IoT ecosystem using empirically based, simulation-generated datasets (namely the Bot-IoT and the IoTID20 datasets).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.