An Improved Binary Whale Optimization Algorithm for Feature Selection of Network Intrusion Detection

Xu, Hui; Fu, Yingchun; Fang, Ce; Cao, Qi; Su, Jun; Wei, Siwei

doi:10.1109/idaacs-sws.2018.8525539

Cited by 25 publications

(15 citation statements)

References 6 publications

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“…Parameter/operator Name Description [49] linear convergence factor a sine, cosine, tangent, log, and square functions used to update value of a in a non-linear fashion. [50], [51] adaptive inertia weights Parameter a adaptive inertia weight is added to update whale position. Parameter a is changed into a nonlinear convergence factor.…”

Section: Referencementioning

confidence: 99%

“…Zhong and Long [49] have proposed five non-linear strategies namely sine, cosine, tangent, log and square, to update the value of control parameter a and cosine method outperformed other four techniques and original WOA. Similarly [39], [50], [51] proposed nonlinear value adjustment of the same control parameter a. adaptive inertia weight is also added in [50] to update whales position.…”

Section: Referencementioning

confidence: 99%

“…Considering feature selection as an optimization problem, several swarm-intelligence based optimization algorithms have been employed to solve this problem. Xu et al created binary variants of WOA and have applied that binary WOA to feature selection problems in different application areas [51], [71]- [77]. Table 3 summarizes all binary variants of WOA discussed in this section along with the application areas.…”

Section: Discrete or Binary Variantsmentioning

confidence: 99%

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A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

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Whale optimization algorithm (WOA) is an emerging nature-inspired, swarm-intelligence based algorithm to solve optimization problems more efficiently. This algorithm is based on the bubble-net hunting strategy of the humpback whales. It has gained immense popularity among researchers, typically, due to its simple nature, fast convergence, and having minimum parameters. In the recent past, it has been widely adopted in various fields including data mining, machine learning, wireless sensor networks, cloud computing, civil engineering, and power systems due to its optimal performance. The WOA has given competitive results in comparison to the state-of-the-art optimization algorithms. In this study, we aim to present a comprehensive survey of WOA consisting of more than eighty existing variants of WOA. More specifically, we intend to put forward key aspects of WOA variants with reference to modifications and applications. Further, we classify the most dominant variants of WOA in distinct categories based on modification area such as equation modification, parameter tuning or the problem space for which an algorithm has been specifically altered. We believe that this study will be beneficial for the community working on optimization problems and it can serve as a basis for understanding the modification and improvement process of an optimization algorithm.

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Section: Referencementioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Discrete or Binary Variantsmentioning

confidence: 99%

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A comprehensive survey of whale optimization algorithm: modifications and classification

Mahmood

Bawany

Tanweer

2023

IJEECS

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“…Xu ve ark. [10] Ağ saldırısı tespiti için geliştirilmiş bir ikili balina optimizasyon algoritmasına (Whale Optimization Algorithm -WOA) dayalı bir özellik seçme yöntemi sunmuşlardır. Önerdikleri yöntemi GA ile karşılaştırmışlar ve sınıflandırma başarısını KDD Cup veri seti üzerinde ölçmüşlerdir.…”

Section: Literatürdeki İlgili çAlışmalarunclassified

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

Ahmetoglu

Daş

2021

Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi

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Applications on the Internet have some coding-related security concerns. Weaknesses or vulnerabilities allow criminals to gain direct and public access to databases to steal sensitive data. This study proposes an approach based on heuristic feature selection and machine learning for easier and more accurate detection of web application attacks with hybrid intrusion detection systems. Web application attacks and benign flow examples in CIC-IDS2017 and CSE-CIC-IDS2018 datasets were combined after a series of data preprocessing stages, and a new dataset was created. Using Genetic Algorithm and LogisticRegression, mean square error and feature count optimization were performed, and the results were tested with five different machine learning algorithms. When the results obtained were examined, it was observed that the success rate in classification remained at the level of 99%, although the number of features was reduced by 85%

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“…Also, the dolphin swarm behaviors such as hunting and spraying are demonstrated the feasibility for detection and prevention of malicious nodes, e.g., malicious vehicles, and data attacks from the VANET. The SI-enhanced IDS is also considered in [168] for wireless networks. Different from [167], this study relies on a WOA scheme that can achieve better classification accuracy by using fewer feature subsets.…”

Section: Attack Detectionmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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An Improved Binary Whale Optimization Algorithm for Feature Selection of Network Intrusion Detection

Cited by 25 publications

References 6 publications

A comprehensive survey of whale optimization algorithm: modifications and classification

A comprehensive survey of whale optimization algorithm: modifications and classification

Genetic Feature Selection Approach in Detection of Web Application Attacks Using Machine Learning Methods

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

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