CDDO-Leo-Based Feature Selection for IoT Intrusion Detection
DOI:
https://doi.org/10.71285/icpt.v3i2.34Keywords:
Feature Selection, Machine Learning, Intrusion Detection System (IDS), Cybersecurity, IoT SecurityAbstract
An Internet of Things (IoT) intrusion detection system (IDS) can be defined as one of the security systems designed for monitoring, analyzing, and detecting malicious cyberthreats or activity on IoT network. It helps to protect IoT devices as well as infrastructure from malware, unauthorized access, denial-of-service (DoS) attacks, and other security breaches. IoT networks produce high-dimensional, redundant and noisy data and thus intrusion detection is a difficult task. The current techniques like HHO-SSA, GWO, and WOA are limited in terms of balancing exploitation and exploration, which results in sub optimal feature selection and low classification performance. To enhance classification accuracy and computational efficiency, the proposed CDDO-LEO hybrid algorithm enhances the feature selection through integrating Child Drawing Development Optimization algorithm (CDDO) with Lagrange Elementary Optimization algorithm (LEO) for dataset refinement. It was used to test an IoT intrusion detection dataset where 17 optimal features were selected and Gradient Boosting got 98.60% accuracy.
It performed better than the conventional feature selection methods like HHO-SSA, GWO and WOA. The comparative analysis establishes the fact that CDDO-LEO has better and more consistent classification performance especially when using ensemble classifiers. The proposed method also consumes less processing time and is also has high accuracy thus useful in high dimensional datasets.
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