ArticleSensors (Basel, Switzerland)2022
An Efficient Multilevel Probabilistic Model for Abnormal Traffic Detection in Wireless Sensor Networks.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks.Sensors (Basel, Switzerland) · 2022Pooled it
- A hybrid metaheuristic algorithm with machine learning for detecting denial-of-service attacks in wireless sensor networks.Frontiers in artificial intelligence · 2026Article
- Research on rechargeable agricultural wireless sensor network based on ZigBee immune routing repair algorithm.Scientific reports · 2025Article
- A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks.Sensors (Basel, Switzerland) · 2024Article
- Privacy-Preserving Indoor Trajectory Matching with IoT Devices.Sensors (Basel, Switzerland) · 2023Article
- Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wireless sensor networks (WSNs) are low-cost, special-purpose networks introduced to resolve various daily life domestic, industrial, and strategic problems. These networks are deployed in such places where the repairments, in most cases, become difficult. The nodes in WSNs, due to their vulnerable nature, are always prone to various potential threats. The deployed environment of WSNs is noncentral, unattended, and administrativeless; therefore, malicious attacks such as distributed denial of service (DDoS) attacks can easily be commenced by the attackers. Most of the DDoS detection systems rely on the analysis of the flow of traffic, ultimately with a conclusion that high traffic may be due to the DDoS attack. On the other hand, legitimate users may produce a larger amount of traffic known, as the flash crowd (FC). Both DDOS and FC are considered abnormal traffic in communication networks. The detection of such abnormal traffic and then separation of DDoS attacks from FC is also a focused challenge. This paper introduces a novel mechanism based on a Bayesian model to detect abnormal data traffic and discriminate DDoS attacks from FC in it. The simulation results prove the effectiveness of the proposed mechanism, compared with the existing systems.
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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.