| Autor: |
Tinuke Omolewa Oladele, Dayo Reuben Aremu, Adeleke Raheem Ajiboye, Muiz O. Raheem, Muhammed K. Jimoh, Oluwakemi Christiana Abikoye, Kayode S. Adewole |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics ISBN: 9783030662875 |
| DOI: |
10.1007/978-3-030-66288-2_7 |
| Popis: |
Within the multitude of security challenges facing the online community, malicious websites play a critical role in today’s cybersecurity threats. Malicious URLs can be delivered to users via emails, text messages, pop-ups or advertisements. To recognize these malicious websites, blacklisting services have been created by the web security community. This method has been proven to be inefficient. This chapter proposed meta-heuristic optimization method for malicious URLs detection based on genetic algorithm (GA) and wolf optimization algorithm (WOA). Support vector machine (SVM) as well as random forest (RF) were used for classification of phishing web pages. Experimental results show that WOA reduced model complexity with comparable classification results without feature subset selection. RF classifier outperforms SVM based on the evaluation conducted. RF model without feature selection produced accuracy and ROC of 0.972 and 0.993, respectively, while RF model that is based on WOA optimization algorithm produced accuracy of 0.944 and ROC of 0.987. Hence, in view of the experiments conducted using two well-known phishing datasets, this research shows that WOA can produce promising results for phishing URLs detection task. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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