In this paper, SVM intrusion detection classification is used to detect the intrusions in fog based mobile edge computing (MEC). However, chosen characteristics such as a mean, limit and median system and the suggested detection scheme can be used regardless of the type of propagation such that the traffic strength to the node is affected by an increase or decrease in intrusion or attack. The SVM is preferred for its efficient efficiency among other machine learning algorithms. The similarity of output in the experimental results section between the various algorithms supports our point. In addition, the experimental findings demonstrate the light weight of the algorithm. In addition, we provided a comparative comparison with other machine-driven classifiers of the SVM based classifier to demonstrate how accurate SVM is than other techniques. We have submitted a comparison of the algorithm suggested with another IDS in literature for further verification. The findings indicate that an SVM-based IDS can be used to detect attacks satisfactorily.
M Ramkumar
Gnanamani College of Technology, India
Intrusion Detection, Fog Computing, Support Vector Machine, Parameters | Published By :
ICTACT
Published In :
ICTACT Journal on Data Science and Machine Learning
( Volume: 2 , Issue: 2 )
Date of Publication :
March 2021
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