Hyper-Heuristic Firefly Algorithm Based Convolutional Neural Networks for Big Data Cyber Security

Abstract

Objectives: A highly accurate Intrusion detection model is developed that classifies both the network-based and host-based intrusions without any complexity issues. Method: An optimized Deep Learning (DL) algorithm of IDS model is presented in the form of a Hyper-Heuristic Firefly Algorithm based Convolutional Neural Networks (HHFA-CNN). This proposed HHFA-CNN reduces false values and improves accuracy without increasing the complexities. Findings: The proposed HHFA-CNN system is performed on two network traffic datasets: NSL-KDD and ISCX-IDS. The outcomes demonstrated that the proposed HHFA-CNN model gives predominant execution than the other existing models. Novelty: The proposed model has employed a novel Hyper-Heuristic Firefly Algorithm for optimizing the hyper-parameters of the CNN. This model maintains the standard guidelines of the firefly algorithm and applies the high-level technique for controlling the exploration and determination of low-level heuristics.