IOT Network Attack Detection by Machine Learning with Genetic Algorithm Code Files

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IOT network increases the comfort of human life in various measures. This growing network attract many intruders to attack on the system hence security of IOT devices is major concern these days. This chapter has proposed a IOT network intrusion detection system that detect the session into attack and normal class. Moth flame optimization genetic algorithm was used in the work for selection of features for identifying the class representative sessions. Identification of class session was done by K-Nearest Neighbor.

Conclusion This model has developed a model that support such network for the intrusion detection. As input dataset has feature set that have vule set. These features are cluster into selected and rejected group by moth flame optimization algorithm. Selected features were used for the identification of feature values that act as cluster center of intrusion and non-intrusion class detection by KNN approach. Experiment was doe on IOT dataset and result shows that proposed model has improved the precision value of intrusion detection  as compared to other existing model.

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