AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URLs Detection

Muntasir, Md. Fahim; Rahman, Sheikh Shah Mohammad Motiur; Jahan, Nusrat; Siddikk, Abu Bakkar; Islam, Takia

doi:10.1007/978-3-030-74575-2_9

Cited by 1 publication

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References 24 publications

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“…However, the limitation in recall (sensitivity) due to the nature of phishing URLs was pointed out for the past deep learning approach aiming to minimize the loss function in the classification task. Considering the characteristics of phishing URLs that are immediately discarded after being reported, an additional optimization process that can minimize the false negatives of unobserved attacks is essential [7]. Table 1 shows the characteristics of each URL type traditionally used for modeling phishing attacks.…”

Section: Introductionmentioning

confidence: 99%

Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection

Kim

2022

Electronics

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Deep learning models for phishing URL classification based on character- and word-level URL features achieve the best performance in terms of accuracy. Various improvements have been proposed through deep learning parameters, including the structure and learning strategy. However, the existing deep learning approach shows a degradation in recall according to the nature of a phishing attack that is immediately discarded after being reported. An additional optimization process that can minimize the false negatives by selecting the core features of phishing URLs is a promising avenue of improvement. To search the optimal URL feature set and to fully exploit it, we propose a combined searching and learning strategy that effectively models the URL classifier for recall. By incorporating the deep-learning-based URL classifier with the genetic algorithm to search the optimal feature set that minimizing the false negatives, an optimized classifier that guarantees the best performance was obtained. Extensive experiments on three real-world datasets consisting of 222,541 URLs showed the highest recall among the deep learning models. We demonstrated the superiority of the method by 10-fold cross-validation and confirmed that the recall improved compared to the latest deep learning method. In particular, the accuracy and recall were improved by 4.13%p and 7.07%p, respectively, compared to the convolutional–recurrent neural network in which the feature selection optimization was omitted.

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Section: Introductionmentioning

confidence: 99%

Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection

Kim

2022

Electronics

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AntiPhishTuner: Multi-level Approaches Focusing on Optimization by Parameters Tuning in Phishing URLs Detection

Cited by 1 publication

References 24 publications

Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection

Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection

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