ArticleSensors (Basel, Switzerland)2024
Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Explainable AI-Based Intrusion Detection Systems for IoT Environments: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2026Review
- A cross-dataset harmonized intrusion detection framework with statistically validated multi-model learning.PloS one · 2026Article
- Explainable Deep Reinforcement Learning for Anomaly Detection in IoT-Enabled Metaverse Healthcare: Toward Trustworthy Cyber Threat Intelligence.Research (Washington, D.C.) · 2026Article
- Optimized intrusion detection for IoT networks using Cauchy-Gaussian hybrid evolutionary feature selection.Scientific reports · 2025Article
- Robust IoT security using isolation forest and one class SVM algorithms.Scientific reports · 2025Article
- Emulation-Based Dataset EmuIoT-VT for NIDS in IoT Systems.Sensors (Basel, Switzerland) · 2025Article
- FD-IDS: Federated Learning with Knowledge Distillation for Intrusion Detection in Non-IID IoT Environments.Sensors (Basel, Switzerland) · 2025Article
- Securing IoT Communications via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method.Sensors (Basel, Switzerland) · 2025Article
- Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest.Scientific reports · 2025Article
- Machine Learning-Based Security Solutions for IoT Networks: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2025Review
- An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks.Sensors (Basel, Switzerland) · 2025Article
- LSTM-JSO framework for privacy preserving adaptive intrusion detection in federated IoT networks.Scientific reports · 2025Article
- Robust machine learning based Intrusion detection system using simple statistical techniques in feature selection.Scientific reports · 2025Article
- An Intrusion Detection System over the IoT Data Streams Using eXplainable Artificial Intelligence (XAI).Sensors (Basel, Switzerland) · 2025Article
- Harnessing advanced hybrid deep learning model for real-time detection and prevention of man-in-the-middle cyber attacks.Scientific reports · 2025Article
- Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection-Current Research Trends.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.
Indexed as
Identifiers
What OpenQuestion holds
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.