A Malicious Botnet Traffic Detection Using Machine Learning

Abstract

Detection of incorrect and malign data transfers in the Internet of Things (IoT) network is important for IoT safety to observe an eye on and
prevent unwelcomed traffic flow to the network of IoT. For it, Machine Learning (ML) strategic methods are produced by several researchers
to prevent malign data flows through the network of IoT. Nonetheless, because of the wrong choice of feature, a few malign Machine Learning
models differentiate especially the movement of malign traffic. Still, what matters is the problem that needs to be deliberated in-depth to select
the best features for better malign traffic acquisition in the network of IoT. Dealing with the challenge, a new process was proposed. 1st, the
metric method of selecting a novel feature called the proposed CorrAUC, and hinged on CorrAUC, a new highlight for choosing the Corrauc
algorithm name is also being developed, designed hinged on the system folding filter features precisely and select the active features of the
choose ML method using AUC metric. After that, we apply a combined application Order of Preference by Similarity to Ideal Solution Using
Shannon Entropy (TOPSIS) built on a bijective set which is soft to verify selected features for identification of malign 1traffic in IoT network.
We test our method using data set of Bot-IoT and 4 dissimilar ML classifiers. Practical outcomeanalysis showed that our proposed approach
works as well and can achieve greater than 96% results on average.