Detecting Multilayer IoT Attacks with Machine Learning
Author Information
Author(s): Sukhni Badeea Al, Manna Soumya K., Dave Jugal M., Zhang Leishi
Primary Institution: Canterbury Christ Church University
Hypothesis
This research aims to develop a Semi-Automated Intrusion Detection System (SAIDS) that integrates efficient feature selection and human expertise to detect multilayer attacks in IoT systems.
Conclusion
The proposed SAIDS framework effectively identifies multilayer attacks using an optimal set of 13 significant features, achieving over 94% accuracy with the KNN model.
Supporting Evidence
- The KNN algorithm demonstrated an average accuracy exceeding 94% in detecting multilayer attacks.
- The proposed framework extracted an optimal set of 13 significant features out of 64 in the Edge-IIoT dataset.
- Existing research on multilayer IoT attacks exhibits gaps in real-world applicability.
Takeaway
This study created a smart system that helps find bad guys trying to attack our internet-connected devices by picking out the most important clues.
Methodology
The study used a semi-automated approach combining feature selection, feature weighting, and human expertise to improve detection accuracy.
Potential Biases
The reliance on automated processes without human input in feature selection may affect the reliability of detection models.
Limitations
Some models, like Naive Bayes, struggled with complex attack types such as XSS, indicating a need for more advanced methodologies.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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