Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)
| Autor: | Darmawiiovo Hanapi, Benni Purnama, Sharipuddin, Deris Stiawan, Mohd. Yazid Idris, Rahmat Budiarto, Kurniabudi, Eko Arip Winanto |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
business.industry Computer science Feature extraction Testbed Pattern recognition 02 engineering and technology Intrusion detection system Network topology Set (abstract data type) 03 medical and health sciences Disk formatting 030104 developmental biology Component (UML) Principal component analysis 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | 2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI). |
| DOI: | 10.23919/eecsi50503.2020.9251292 |
| Popis: | Feature extraction solves the problem of finding the most efficient and comprehensive set of features. A Principle Component Analysis (PCA) feature extraction algorithm is applied to optimize the effectiveness of feature extraction to build an effective intrusion detection method. This paper uses the Principal Components Analysis (PCA) for features extraction on intrusion detection system with the aim to improve the accuracy and precision of the detection. The impact of features extraction to attack detection was examined. Experiments on a network traffic dataset created from an Internet of Thing (IoT) testbed network topology were conducted and the results show that the accuracy of the detection reaches 100 percent. |
| Databáze: | OpenAIRE |
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