journal article·2025Open access

A Barrier-Based Machine Learning Approach for Intrusion Detection in Wireless Sensor Networks

Haydar Abdulameer Marhoon, Rafid Sagban, Atheer Y. Oudah, Saadaldeen Rashid Ahmed

Computers, Materials & Continua· Volume 82· Issue 3

Abstract

In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, hea... | Find, read and cite all the research you need on Tech Science Press
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Research context

OpenAlex topics: Network Security and Intrusion Detection · Security in Wireless Sensor Networks

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (46)

Show source-provided references
1. Participation of 5G with wireless sensor networks in the internet of things (IoT) application
2. Applications of wireless sensor networks and internet of things frameworks in the Industry Revolution 4.0: a systematic literature review10.3390/s22062087 ↗
3. Challenges, threats, security issues and new trends of underwater wireless sensor networks10.3390/s18113907 ↗
4. Securing wireless sensor networks using machine learning and blockchain: a review10.3390/fi15060200 ↗
5. A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network10.1109/ACCESS.2020.3034015 ↗
6. Ensemble-based deep learning models for enhancing IoT intrusion detection10.3390/app132111985 ↗
7. An explainable evolving fuzzy neural network to predict the k barriers for intrusion detection using a wireless sensor network10.3390/s22145446 ↗
8. SLGBM: an intrusion detection mechanism for wireless sensor networks in smart environments10.1109/ACCESS.2020.3024219 ↗
9. Comparison of machine learning techniques applied to traffic prediction of real wireless network10.1109/ACCESS.2021.3129850 ↗
10. Ensemble learning for intrusion detection systems: a systematic mapping study and cross-benchmark evaluation10.1016/j.cosrev.2020.100357 ↗
11. Boosting meta-learning with simulated data complexity measures10.3233/IDA-194803 ↗
12. Intrusion detection based on ensemble learning for big data classification10.1007/s10586-023-04168-7 ↗
13. XIDINTFL-VAE: XGBoost-based intrusion detection of imbalance network traffic via class-wise focal loss variational autoencoder10.1007/s11227-024-06552-5 ↗
14. Sequential Monte Carlo localization in mobile sensor networks10.1007/s11276-007-0064-3 ↗
15. Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection10.1038/s41598-023-50554-x ↗
16. Enhancing intrusion detection: a hybrid machine and deep learning approach10.1186/s13677-024-00685-x ↗
17. A novel hybrid deep learning framework for intrusion detection systems in WSN-IoT networks10.32604/iasc.2022.022259 ↗
18. RNNIDS: enhancing network intrusion detection systems through deep learning10.1016/j.cose.2020.102151 ↗
19. Enhancing intrusion detection in wireless sensor networks through machine learning techniques and context awareness integration10.59543/ijmscs.v2i.10377 ↗
20. Intrusion detection system in wireless sensor networks using modified recurrent neural network with long short-term memory
21. Data mining approach to analyzing intrusion detection of wireless sensor network
22. Supervised machine learning classification algorithmic approach for finding anomaly type of intrusion detection in wireless sensor network10.3103/S1060992X20030029 ↗
23. A genetic-based extreme gradient boosting model for detecting intrusions in wireless sensor networks10.3390/s19204383 ↗
24. CNN-LSTM based approach for dos attacks detection in wireless sensor networks
25. Enhancing intrusion detection in wireless sensor networks using deep learning-based K-barriers prediction
26. HIDM: hybrid intrusion detection model for Industry 4.0 networks using an optimized CNN-LSTM with transfer learning10.3390/s23187856 ↗
27. A novel optimal deep learning approach for designing intrusion detection system in wireless sensor networks10.1016/j.eij.2024.100522 ↗
28. A deep learning based feed forward artificial neural network to predict the K-barriers for intrusion detection using a wireless sensor network
29. A deep learning approach to predict the number of k-barriers for intrusion detection over a circular region using wireless sensor networks10.1016/j.eswa.2022.118588 ↗
30. Anomaly-based intrusion detection system in wireless sensor networks using machine learning algorithms
31. Intrusion detection system for wireless sensor networks: a machine learning based approach10.1109/ACCESS.2024.3380014 ↗
32. MuDeLA: multi-level deep learning approach for intrusion detection systems
33. Intrusion detection in machine learning based E-shaped structure with algorithms, strategies and applications in wireless sensor networks10.1016/j.heliyon.2024.e30675 ↗
34. Machine learning algorithms from wireless sensor network’s perspective
35. Use of random forest in the identification of important variables10.1016/j.microc.2018.12.028 ↗
36. Optimization by gradient boosting10.1007/978-3-030-73249-3_2 ↗
37. Learning in feedforward neural networks accelerated by transfer entropy10.3390/e22010102 ↗
38. An approach of feed-forward neural network throughput-optimized implementation in FPGA10.3390/electronics9122193 ↗
39. Machine learning econometrics: bayesian algorithms and methods10.1093/acrefore/9780190625979.013.588 ↗
40. Hyper-parameter tuning based on random search for DenseNet optimization
41. Anomaly detection for fault detection in wireless community networks using machine learning10.1016/j.comcom.2023.02.019 ↗
42. Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv:2010.16061. 2020.
43. Genetic algorithm with radial basis mapping network for the electricity consumption modeling10.3390/app10124239 ↗
44. Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network10.1186/s13638-020-01729-x ↗
45. Radial basis function artificial neural network able to accurately predict disinfection by-product levels in tap water: taking haloacetic acids as a case study10.1016/j.chemosphere.2020.125999 ↗
46. Wireless sensor network and deep learning for prediction greenhouse environments