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

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

202020 citationsDOI

Abstract

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.

Topics & Concepts

Principal component analysisFeature extractionTestbedIntrusion detection systemComputer scienceData miningArtificial intelligencePattern recognition (psychology)Internet of ThingsComputer networkComputer securityNetwork Security and Intrusion DetectionAnomaly Detection Techniques and ApplicationsAdvanced Malware Detection Techniques