Modified Firefly Optimization Algorithm-Based IDS for Nature-Inspired Cybersecurity

Abstract

The new paradigm of nature-inspired cybersecurity can establish a robust defense by utilizing well-established nature-inspired computing algorithms to analyze networks and act quickly. The existing research focuses primarily on the efficient selection of features for quick and optimized detection rates using firefly and other nature-inspired optimization techniques. However, selecting the most appropriate features may be specific to the network, and a different set of features may work better than the selected one. Therefore, there is a need for a generalized pre-processing step based on the standard network monitoring parameters for the early detection of suspicious nodes before applying feature-based or any other type of monitoring. This paper proposes a modified version of the firefly optimization algorithm to effectively monitor the network by introducing a novel health function for the early detection of suspicious nodes. We implement event management schemes based on the proposed algorithm and optimize the observation priority list based on a genetic evolution algorithm for real-time events in the network. The obtained simulation results demonstrate the effectiveness of the proposed algorithm under various attack scenarios. In addition, the results indicate that the proposed method reduces approximately 60–80% of the number of suspicious nodes while increasing the turnaround time by only approximately 1–2%. The proposed method also focuses specifically on accurate network health monitoring to protect the network proactively.

Improved Crow Search-Based Feature Selection and Ensemble Learning for IoT Intrusion Detection

“…T r +ve T r +ve + F l −ve (12) Accuracy (ACC) is a significant metric widely used in classification tasks to measure the concert of a classifier. It is computed by dividing the number of accurate estimates by the overall number of estimates by the classifier.…”

“…Recently, researchers from various domains applied meta-heuristic algorithms to FS. Some of the recent works that used metaheuristic algorithms for FS in IDS are: Genetic Algorithm (GA) [7], Bat Optimization algorithm (BOA) [10], Particle Swarm Optimization (PSO) [8], Dragonfly Algorithm (DA) [11], Grey Wolf Optimization (GWO) [9], Firefly optimization (FFO) [12], whale optimization algorithm (WOA) [13] and Pigeon Inspired Optimizer (PIO) [14]. Among them, Alazzam et al [14] proposed a PIO to select the prominent features by eradicating the redundant features.…”

Plant and Salamander Inspired Network Attack Detection and Data Recovery Model

“…Almomani et al [ 42 ] have used the Firefly Algorithm for feature selection and SVM together with J48 classifier on the UNSW-NB15 dataset. Shandilya et al [ 43 ] have used the Firefly Algorithm for the selection of nodes that could be monitored in a network. The authors have used SVM classifier on the NSL-KDD dataset.…”

Modified Firefly Algorithm using Iterated Descent Method to Solve Machine Scheduling Problems

“…In [14] the authors combined the FFA with VNS for Data Clustering (FA-VNS), the results showed that the proposed algorithm performance better than other well-known clustering algorithms in literature. In [15] the authors propose a modified FFA to effectively observe the network by introducing a new health function for early detection of suspicious nodes, the results showed that the proposed algorithm reduces the number of suspicious nodes. In this paper, we proposed three modifications to firefly algorithm FFA and used IDM algorithm to improve the performance of the original algorithm.…”