Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

Abstract

Applications of internet-of-things (IoT) are increasingly being used in many facets of our daily life, which results in an enormous volume of data. Cloud computing and fog computing, two of the most common technologies used in IoT applications, have led to major security concerns. Cyberattacks are on the rise as a result of the usage of these technologies since present security measures are insufficient. Several artificial intelligence (AI) based security solutions, such as intrusion detection systems (IDS), have been proposed in recent years. Intelligent technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection (FS) techniques to increase classification accuracy by minimizing the number of features selected. On the other hand, metaheuristic optimization algorithms have been widely used in feature selection in recent decades. In this paper, we proposed a hybrid optimization algorithm for feature selection in IDS. The proposed algorithm is based on grey wolf (GW), and dipper throated optimization (DTO) algorithms and is referred to as GWDTO. The proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better performance. On the employed IoT-IDS dataset, the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC, 2023, vol.74, no.2 the literature to validate its superiority. In addition, a statistical analysis is performed to assess the stability and effectiveness of the proposed approach. Experimental results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

A Novel CNN Model with Dimensionality Reduction for WSN Intrusion Detection

“…Feature selection in IDS using a hybrid optimization approach was proposed by Alkanhel et al [26]. The suggested approach, known as GWDTO, is inspired by the grey wolf (GW) and dipper throated optimization (DTO) algorithms.…”

“…Following training, the network is tested on an independent dataset to gauge its ability to detect outliers. Improve the CNN's ability to spot network abnormalities by expanding the size and diversity of the training dataset, tweaking hyperparameters like learning rate and regularization, and using data augmentation techniques to add variety to the training data [26,27].…”

An Intrusion Detection Method Based on a Universal Gravitation Clustering Algorithm

“…Considering these threats, intrusion detection has emerged as a critical task in network security. Traditional intrusion detection techniques primarily relies on rules and feature engineering [4][5][6][7][8]. These methods can be effective in certain scenarios, while exhibiting certain limitations.…”

Leveraging Metaheuristics for Feature Selection With Machine Learning Classification for Malicious Packet Detection in Computer Networks

“…These algorithms are search-based optimization techniques inspired by natural processes such as evolution, swarm behavior, and genetics [3]. [4] have proposed the use of grey wolf and dipper throat optimization for feature selection for IDS. Their results show an increase in classification accuracy between the different types of attacks, which would be beneficial for IoT systems.…”