Research Article
Detection of Middlebox-Based Attacks in Healthcare Internet of Things Using Multiple Machine Learning Models
| Reference | Dataset | IoMT | Technique | Internal attacks | External attacks | Packets flow anomaly | Outcomes | Accuracy (%) | Limitations |
| Fujita et al. [40] | Real time data | ✔ | Machine learning | No | ✔ | ✔ | Anomaly detection and attacks protection | 89 | No detection using features | Manimurugan et al. [15] | Sensors data | ✔ | Machine learning | No | ✔ | ✔ | Early attack detection | 88.67 | No feature scoring | Saheed and Arowolo [6] | Cloud based data | ✔ | Machine learning | No | ✔ | ✔ | Anomaly detection | 90 | No real-time system | Manimurugan et al. [15] | Sensors data | ✔ | Machine learning | No | ✔ | ✔ | Anomaly detection | 91 | No detection using features | Aljumaie et al. [41] | Real time data | ✔ | Machine learning | No | ✔ | No | Anomaly detection | 92 | No feature scoring | Meng et al. [3] | Real time data | ✔ | Deep learning | No | ✔ | ✔ | Anomaly detection | 91 | No real-time system | Ali and Mahmoud [42] | Real time data | ✔ | Effective NN | No | ✔ | ✔ | Anomaly from real-time | 89.5 | No detection using features | Salem et al. [43] | Sensors data | ✔ | Efficient NN | No | ✔ | No | Anomaly from sensors data | 92.06 | No feature scoring | Sehatbakhsh et al. [16] | Sensors data | ✔ | Deep learning | No | ✔ | No | Jamming attacks in WBANS | 90 | No real-time system |
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