A hybrid intelligently initialized particle swarm optimizer with weight factored binary gray wolf optimizer for mitigation of security issues in Internet of Things and sensor nodes

With expanding nature of Internet of Things (IoT) based solutions in various application areas, the threat of cyber attacks is looming large. Often for resource constrained IoT nodes, implementation of machine learning based intrusion detection system is not practical, as the feature set to be processed is large in size. In this article, random forest based intelligently initialized hybrid binary particle swarm optimizer (PSO)‐gray wolf optimizer (GWO) is proposed to reduce the dimensions of dataset. The proposed algorithm exploits the concept of relative weights of the leader wolves of GWO to update the positions of particles in PSO. A novel fitness function is also introduced, that includes the key performance metrics for measuring the classification efficiency. Also, a new performance metric is proposed here for commensurate comparison with other related works, which renders the task of comparison effective. For experimental evaluation, the historical dataset NSL‐KDD and the more contemporary DS2OS dataset are both taken into consideration. The proposed work attains accuracy up to 99.61% for NSL‐KDD dataset and 99.79% for DS2OS dataset. The outcomes are better than majority of algorithms and recent related works. Most notably, for increasing the accuracy, the number of features are not compromised and are even reduced to 8 and 5 features, respectively, for NSL‐KDD and DS2OS.

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