A Novel Feature Selection Method Using Whale Optimization Algorithm and Genetic Operators for Intrusion Detection System in Wireless Mesh Network
R. Vijayanand,D. Devaraj +1 more
TLDR
A wrapper-based approach using the modified whale optimization algorithm, in which the genetic algorithm operators were combined with the WOA to improve the search space of whales, and the mutation operator helped to avoid being stuck in the local optimum.Abstract:
Machine learning–based intrusion detection system (IDS) is an important requirement for securing data traffic in wireless mesh networks. The noisy and redundant features of network data tend to degrade the performance of the attack detection classifiers. Therefore , the selection of informative features plays a vital role in the enhancement to the IDS. In this paper, we propose a wrapper-based approach using the modified whale optimization algorithm (WOA). One drawback of WOA is that premature convergence results in a local optimal solution. To overcome this limitation, we proposed a method in which the genetic algorithm operators were combined with the WOA. The crossover operator was used to further improve the search space of whales, and the mutation operator helped to avoid being stuck in the local optimum. The proposed method selects the informative features in the network data, which helps to accurately detect intrusions. Using a support vector machine (SVM), we identified the types of intrusions based on the selected features. The performance of the improved method was analyzed by using the CICIDS2017 and ADFA-LD standard datasets. Our proposed method had better attack detection rate than the standard WOA and other evolutionary algorithms; it also had good accuracy and was suitable for IDS in the wireless mesh networks. The performance of the IDS was increased by selecting the informative features with the improved whale optimization algorithm. The attack detection ratio was higher than that of the standard WOA.read more
Citations
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Journal ArticleDOI
Intrusion detection systems using long short-term memory (LSTM)
TL;DR: In this article, the authors implemented deep learning solutions for detecting attacks based on Long Short-Term Memory (LSTM) and used PCA (principal component analysis) and Mutual Information (MI) techniques for dimensionality reduction and feature selection techniques.
Journal ArticleDOI
A review of recent approaches on wrapper feature selection for intrusion detection
TL;DR: In this paper , a review of recent advances in wrapper feature selection techniques for attack detection and classification, applied in intrusion detection area is presented, considering design, rationale, technical characteristics and common evaluation metrics.
Journal ArticleDOI
Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM
TL;DR: A novel fault diagnosis method for rolling bearings combining wavelet threshold de-noising, genetic algorithm optimization variational mode decomposition, and the whale optimization algorithm based on the von Neumann topology optimization least squares support vector machine (VNWOA-LSSVM) is proposed.
Journal ArticleDOI
A novel intrusion detection system using hybrid clustering-optimization approach in cloud computing
TL;DR: In this article, a new hybridization approach for the intrusion detection system is proposed to improve the overall security of cloud based computing environment and also helps to handle various type of security hurdles on the cloud for e.g., fake identity detection, Data leakage and Phishing attacks etc.
References
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