Features Extraction on IoT Intrusion Detection System Using Principal Components Analysis (PCA)

Sharipuddin Sharipuddin, Benni Purnama, Kurniabudi Kurniabudi, Eko Arip Winanto, Deris Stiawan, Darmawijoyo Hanapi, Mohd. Yazid Idris, Rahmat Budiarto


There are several ways to increase detection accuracy result on the intrusion detection systems (IDS), one way is feature extraction. The existing original features are filtered and then converted into features with lower dimension. 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.


Intrusion detection system; Features extraction; Principal component analysis; KNN

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