Optimal Feature Subset Selection Using Cuckoo Search On IoT Network

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%.