ABSTRACT
The Internet of Things (IoT) became the basic axis in the information and network technology to create a smart
environment. To build such an environment; it needs to use some IoT simulators such as Cooja Simulator. Cooja
simulator creates an IoT environment and produces an IoT routing dataset that contains normal and malicious
motes. The IoT routing dataset may have redundant and noisy features. The feature selection can affect on the
performance metrics of the learning model. The feature selection can reduce complexity and over-fitting problem.
There are many approaches for feature selection especially meta-heuristic algorithms such as Cuckoo search (CS).
This paper presented a proposed model for feature selection that is built on using a standard cuckoo search
algorithm to select near-optimal or optimal features. A proposed model may modify the CS algorithm which has
implemented using Dagging with base learner Bayesian Logistic Regression (BLR). It increases the speed of the
CS algorithm and improves the performance of BLR. Support Vector Machine (SVM), Deep learning, and FURIA
algorithms are used as classification techniques used to evaluate the performance metrics. The results have
demonstrated that the algorithm proposed is more effective and competitive in terms of performance of
classification and dimensionality reduction. It achieved high accuracy that is near to 98 % and low error that is
about 1.5%.
Keywords: - IoT, Feature Selection, Meta-heuristic, Cuckoo Search Algorithm, Deep Learning.