ReviewBrain sciences2023
Analysis of IoT Security Challenges and Its Solutions Using Artificial Intelligence.
Review in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- A blockchain-enabled IoT framework for smart electro-medical waste management.Scientific reports · 2026Article
- A Comprehensive Survey on Online AutoML and Adversarial Robustness for IoT and EV Charging Network Security.Sensors (Basel, Switzerland) · 2026Review
- An IoT-based smart emotion recognition system by using internal body parameters.Scientific reports · 2026Article
- Nursing Educators' Perceptions of AI in Research: Risks and Benefits.Nursing research and practice · 2026Article
- AI-optimized GRU-based self-attention model for predictive diabetes staging in IoT healthcare 5.0.Scientific reports · 2025Article
- Artificial Intelligence-based fine-tuning model for fall activity recognition in disabled persons within an IoT environment.Scientific reports · 2025Article
- Evaluation of IoT based smart safety systems for women and children using machine learning techniques.Scientific reports · 2025Article
- Comparative Performance Analysis of Lightweight Cryptographic Algorithms on Resource-Constrained IoT Platforms.Sensors (Basel, Switzerland) · 2025Article
- A Study on IoT Device Authentication Using Artificial Intelligence.Sensors (Basel, Switzerland) · 2025Article
- Innovative Technologies to Improve Occupational Safety in Mining and Construction Industries-Part I.Sensors (Basel, Switzerland) · 2025Review
- Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum.Scientific reports · 2025Article
- An effectiveness of deep learning with fox optimizer-based feature selection model for securing cyberattack detection in IoT environments.Scientific reports · 2025Article
- Ensemble of deep learning and IoT technologies for improved safety in smart indoor activity monitoring for visually impaired individuals.Scientific reports · 2025Article
- (IoT) Network intrusion detection system using optimization algorithms.Scientific reports · 2025Article
- A hybrid AI-Blockchain security framework for smart grids.Scientific reports · 2025Article
- Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2025Review
- Machine Learning-Enhanced Attribute-Based Authentication for Secure IoT Access Control.Sensors (Basel, Switzerland) · 2025Article
- Cognitive intelligence routing protocol for disaster management and underwater communication system in underwater acoustic network.Scientific reports · 2025Article
- An Efficient Flow-Based Anomaly Detection System for Enhanced Security in IoT Networks.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Internet of Things (IoT) is a well-known technology that has a significant impact on many areas, including connections, work, healthcare, and the economy. IoT has the potential to improve life in a variety of contexts, from smart cities to classrooms, by automating tasks, increasing output, and decreasing anxiety. Cyberattacks and threats, on the other hand, have a significant impact on intelligent IoT applications. Many traditional techniques for protecting the IoT are now ineffective due to new dangers and vulnerabilities. To keep their security procedures, IoT systems of the future will need AI-efficient machine learning and deep learning. The capabilities of artificial intelligence, particularly machine and deep learning solutions, must be used if the next-generation IoT system is to have a continuously changing and up-to-date security system. IoT security intelligence is examined in this paper from every angle available. An innovative method for protecting IoT devices against a variety of cyberattacks is to use machine learning and deep learning to gain information from raw data. Finally, we discuss relevant research issues and potential next steps considering our findings. This article examines how machine learning and deep learning can be used to detect attack patterns in unstructured data and safeguard IoT devices. We discuss the challenges that researchers face, as well as potential future directions for this research area, considering these findings. Anyone with an interest in the IoT or cybersecurity can use this website's content as a technical resource and reference.
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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.