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Detection IoT attacks using Lasso regression algorithm with ensemble classifier

K.V. Sheelavathy (Department of Computer Science and Engineering, REVA University, Bangalore, India)
V. Udaya Rani (School of Computer Science and Engineering, REVA University, Bangalore, India)

International Journal of Pervasive Computing and Communications

ISSN: 1742-7371

Article publication date: 29 December 2022




Internet of Things (IoT) is a network, which provides the connection with various physical objects such as smart machines, smart home appliance and so on. The physical objects are allocated with a unique internet address, namely, Internet Protocol, which is used to perform the data broadcasting with the external objects using the internet. The sudden increment in the number of attacks generated by intruders, causes security-related problems in IoT devices while performing the communication. The main purpose of this paper is to develop an effective attack detection to enhance the robustness against the attackers in IoT.


In this research, the lasso regression algorithm is proposed along with ensemble classifier for identifying the IoT attacks. The lasso algorithm is used for the process of feature selection that modeled fewer parameters for the sparse models. The type of regression is analyzed for showing higher levels when certain parts of model selection is needed for parameter elimination. The lasso regression obtains the subset for predictors to lower the prediction error with respect to the quantitative response variable. The lasso does not impose a constraint for modeling the parameters caused the coefficients with some variables shrink as zero. The selected features are classified by using an ensemble classifier, that is important for linear and nonlinear types of data in the dataset, and the models are combined for handling these data types.


The lasso regression with ensemble classifier–based attack classification comprises distributed denial-of-service and Mirai botnet attacks which achieved an improved accuracy of 99.981% than the conventional deep neural network (DNN) methods.


Here, an efficient lasso regression algorithm is developed for extracting the features to perform the network anomaly detection using ensemble classifier.



Sheelavathy, K.V. and Udaya Rani, V. (2022), "Detection IoT attacks using Lasso regression algorithm with ensemble classifier", International Journal of Pervasive Computing and Communications, Vol. ahead-of-print No. ahead-of-print.



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