Optimal intrusion detection for imbalanced data using Bagging method with deep neural network optimized by flower pollination algorithm.

ABSTRACT: As the number of connected devices and Internet of Things (IoT) devices grows, it is becoming more and more important to develop efficient security mechanisms to manage risks and vulnerabilities in IoT networks. Intrusion detection systems (IDSs) have been developed and implemented in IoT networks to discern between regular network traffic and potential malicious attacks. This article proposes a new IDS based on a hybrid method of metaheuristic and deep learning techniques, namely, the flower pollination algorithm (FPA) and deep neural network (DNN), with an ensemble learning paradigm. To handle the problem of imbalance class distribution in intrusion datasets, a roughly-balanced (RB) Bagging strategy is utilized, where DNN models trained by FPA on a cost-sensitive fitness function are use

SUBMITTER: Sayegh HR 

PROVIDER: S-EPMC11935772 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature