Harris hawk optimization trained artificial neural network for anomaly based intrusion detection system

Abstract

SummaryAn integral part of security infrastructure is detecting and identifying malicious attacks commonly found in network environments. Despite its effectiveness at identifying anomalous network behaviors, an intrusion detection system (IDS) still has a low detection rate and a high rate of false alarms. This study proposes a novel effective anomaly IDS by integrating bio‐inspired optimization techniques, Harris hawk optimization (HHO), and an artificial neural network (ANN), called HHO‐ANN. Several experiments were conducted with other methods to verify the performance and capabilities of the proposed technique. The AWID, CIDDS001, NSL‐KDD, and NSL‐KDD datasets were used to benchmark the performance of the proposed method. Popular evolutionary algorithms, such as genetic algorithm, particle swarm optimization, moth‐flame optimization, and locust swarm optimization based on ANN trainer, were implemented to validate the result. Comparison with existing methods in the literature reveals that the proposed method offers more accuracy. Simulation results confirm that the proposed method has excellent speed convergence and a high‐reliability level due to a lower risk of getting stuck in the local minima region. The proposed method achieved accuracy of 96.71%, 98.03%, 98.25%, and 97.95% for AWID, CIDDS‐001, NSL‐KDD, and UNSW‐NB15, respectively.

A Dynamic BPN-MLP Neural Network DDoS Detection Model Using Hybrid Swarm Intelligent Framework

“…In this proposed architecture, deep learning algorithm has been employed and feature extraction is made by variance fractal dimension trajectory (VFDTv2) as a pre-processing step. The proposed model demonstrated 87.35% (2) 0.9212 0.9618 0.9056 0.9450 0.9328 PSO-BPN Liu et al (3) 0.9096 0.9618 0.8886 0.9434 0.9237 BR-BPN Ali et al (4) 0 (9) presented an ANN-based DDoS intrusion detection model based on open CICIDS2017 dataset that requires higher computational power which is greatly reduced in the hybrid BPN-MLP IDS model. Doriguzzi-Corin et al (2020) (10) developed a CNN model for DDoS attack detection.…”

“…Nagarajan et.al (2023) (2) proposed an Intrusion Detection System (IDS) system using Back Propagation Network (BPN) optimized by Particle Swarm Optimization (PSO) which detects intrusions based on system calls collected from KDD cup 99 dataset. Narengbam et.al (2023) (3) proposed an anomaly IDS by using hybrid Harris Hawk Optimization (HHO)-Artificial Neural Network (ANN) algorithm on AWID, CIDDS001 and NSL-KDD datasets. This hybrid IDS achieved better convergence with high reliability.…”

A method for managing scientific research project resource conflicts and predicting risks using BP neural networks

“…Representing the weight of the connection between the i -th neuron in the input layer and the j -th neuron in the hidden layer as W ij . The notation follows Narkhede et al’s study 36 , which offers a comprehensive explanation of neural network fundamentals and operational principles. The information received by the hidden layer is expressed in Eq.…”