Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection

Khurma, Ruba Abu; Sabri, Khair Eddin; Castillo, Pedro; Aljarah, Ibrahim

doi:10.1007/978-3-030-72699-7_10

Cited by 13 publications

(12 citation statements)

References 27 publications

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“…It is open-source and available at (www.evo-ml.com accessed on 28 May 2021). The population size and the maximum iterations were set to 10 and 100, respectively [52].…”

Section: Resultsmentioning

confidence: 99%

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

et al. 2021

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Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has 2N feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.

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“…It is open-source and available at (www.evo-ml.com accessed on 28 May 2021). The population size and the maximum iterations were set to 10 and 100, respectively [52].…”

Section: Resultsmentioning

confidence: 99%

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

et al. 2021

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“…Metaheuristic search methods generate random feature subsets and examine them until reaching the near-optimal features subset. Many of these algorithms have been enhanced to improve the FS process by adopting new operators [29], novel update strategies [30], new initialization [31], new encoding schemes [32], and new fitness functions [33], as well as applying multi-objective [34] and parallel algorithms [35]. The evaluation process of FS is accomplished based on the characteristics of the dataset (e.g., filters) or based on a learning algorithm (e.g., wrappers).…”

Section: Feature Selectionmentioning

confidence: 99%

“…The scalability of SI means that it can perfectly cope with an increasing the number of agents without changing the control architecture of the algorithm. This category contains a considerable number of algorithms such as particle swarm optimization (PSO) [42], the moth-flame optimization (MFO) algorithm [26], and salp swarm optimization (SSA) [29,43]. SI algorithms are characterized by two embedded conflicting milestones: exploration and exploitation.…”

Section: Nature-inspired Algorithmsmentioning

confidence: 99%

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

et al. 2021

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As the COVID-19 pandemic rapidly spreads across the world, regrettably, misinformation and fake news related to COVID-19 have also spread remarkably. Such misinformation has confused people. To be able to detect such COVID-19 misinformation, an effective detection method should be applied to obtain more accurate information. This will help people and researchers easily differentiate between true and fake news. The objective of this research was to introduce an enhanced evolutionary detection approach to obtain better results compared with the previous approaches. The proposed approach aimed to reduce the number of symmetrical features and obtain a high accuracy after implementing three wrapper feature selections for evolutionary classifications using particle swarm optimization (PSO), the genetic algorithm (GA), and the salp swarm algorithm (SSA). The experiments were conducted on one of the popular datasets called the Koirala dataset. Based on the obtained prediction results, the proposed model revealed an optimistic and superior predictability performance with a high accuracy (75.4%) and reduced the number of features to 303. In addition, by comparison with other state-of-the-art classifiers, our results showed that the proposed detection method with the genetic algorithm model outperformed other classifiers in the accuracy.

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“…Salp swarm algorithm (SSA) is a recent swarm optimization algorithm that has been successfully employed for solving various optimization problems [23][24][25][26]. It has the advantage of simplicity and efficiency [27][28].…”

Section: Introductionmentioning

confidence: 99%

Successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis

Thelaidjia

Chetih

Moussaoui

et al. 2023

Int J Adv Manuf Technol

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In this paper we are interested in developing a new approach that combines successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis. Firstly, vibration signals are pre-processed using successive variational mode decomposition to increase the signal-to-noise ratio. Then, the dynamic time warping algorithm is adopted to select the most effective modes which will be considered as mixture signals. In the second step we apply salp swarm algorithm (SSA) for estimating the de-mixing matrix to extract independent components from mixture signals. However, SSA suffers from the problem of population diversity. Consequently, it offers somewhat different independent sources at every execution of the program. To overcome this shortcoming, the SSA based source estimation will be executed several times with different ranges of initial positions. Then, a fuzzy C-mean algorithm is introduced to select the reliable independent components. The suggested method is tested based on two experiments and compared with other blind source algorithms based on Bat and particle swarm optimization (PSO) algorithms. The obtained results demonstrate the effectiveness of the suggested method in recovering reliable independent components and extracting fault frequency of bearings.

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Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection

Cited by 13 publications

References 27 publications

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

An Enhanced Evolutionary Software Defect Prediction Method Using Island Moth Flame Optimization

An Evolutionary Fake News Detection Method for COVID-19 Pandemic Information

Successive variational mode decomposition and blind source separation based on salp swarm optimization for bearing fault diagnosis

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