Machine Learning based anomaly detection for IoT Network: (Anomaly detection in IoT Network)
Nilesh Kumar Sahu, Indrajit Mukherjee
Abstract
As the world is leading towards having everything smart, like smart home, smart grid smart irrigation, there is the major concern of attack and anomaly detection in the Internet of Things (IoT) domain. There is an exponential increase in the use of IoT infrastructure in every field leads to an increase in threats and attacks too. There can be many types of possible attacks and anomaly that can affect the IoT system which can lead to failure of the IoT system. In this paper, different anomalies are predicted based on a different feature in the data set. Two machine learning classification models are used and comparisons between the performance of these used models are shown. Logistic regression and artificial neural network classification algorithms are applied. Since there are more than 3.5 lakh data set, two different approaches are experimented. In the first case, the classification algorithm stated above is applied on the whole 3.5 lakh dataset, and in the second case, all the classification algorithms are applied after omitting the feature “value” having data as 0 and 1. Data is divided into two sets, training and test set where the training set is 75% of total data available and the rest are test set, 99.4% accuracy is obtained for ANN for the first case while 99.99% accuracy is obtained for the algorithm stated above for the second case. This work can be used for identifying threats and anomaly occurring in a smart device and IoT solutions and prevent attacks.