Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection

Park, Kyoung-Won; Bu, Seok-Jun; Cho, Sung-Bae

doi:10.1007/978-3-030-86271-8_8

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…Suleiman et al [27] improved the accuracy of NB classifiers, k-NN classifiers, DT and RF classifiers by incorporating evolutionary computation-based feature selection algorithms into traditional machine-based algorithm-based phishing website detection tasks. Park et al [28] improvement of discovery rules in light of hereditary calculation, amplifying the exactness and review of profound learning classifiers, and further developing identification execution.…”

Section: Literature Reviewmentioning

confidence: 99%

Efficient And Accurate Technique For Improving ML Classifier Performance Using Feature Selection In Phishing Website Prediction

Awasthi,

Goel

2024

eatp

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Section: Literature Reviewmentioning

confidence: 99%

Efficient And Accurate Technique For Improving ML Classifier Performance Using Feature Selection In Phishing Website Prediction

Awasthi,

Goel

2024

eatp

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“…Cyber Security A genetic algorithm is proposed to find combinatorial optimization of logic programmed constraints and deep learning from given components, which are rule-based and neural components. The genetic algorithm explores numerous searching spaces of combinations of rules with deep learning to get an optimal combination of the components [64].…”

Section: Outcome Of Clevrer Modelsmentioning

confidence: 99%

Neuro-Symbolic AI: Bringing a new era of Machine Learning

Kishor¹

2022

Int.J.Res.Publ.Rev.

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Processing Natural Language using machines is not a new concept. Back in 1940 researchers estimated the importance of a machine that could translate one language to another. Further, during 1957-1970 researchers split into two divisions concerning NLP: symbolic and stochastic. This paper presents an extensive review of recent breakthroughs in Neuro Symbolic Artificial Intelligence (NSAI), an area of AI research which seeks to combine traditional rules-based AI approaches with modern deep learning techniques. Neuro Symbolic models have already demonstrated the capability to outperform state-of-the-art deep learning models in domains such as image and video reasoning. Such models not only performed better when trained on a fraction of dataset compared with traditional machine learning models, but also solved an underlined issue called generalization of deep neural network systems. We also find that symbolic models are good in visual question answering (VQA). In this paper, we also review research results related to Neuro Symbolic AI with the objective of exploring the importance of such AI systems and how it would shape the future of AI as a whole. We discuss different types of dataset of Visual Question Answering (VQA) tasks based on NSAI and extensive comparison of performance of different NSAI models. Later, the article focuses on the contemporary real time application of NSAI systems and how NSAI is shaping the world’s different sectors including finance, healthcare, and cyber security.

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A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences

Park

Cho

2022

Lecture Notes in Computer Science

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Evolutionary Optimization of Neuro-Symbolic Integration for Phishing URL Detection

Cited by 7 publications

References 19 publications

Efficient And Accurate Technique For Improving ML Classifier Performance Using Feature Selection In Phishing Website Prediction

Efficient And Accurate Technique For Improving ML Classifier Performance Using Feature Selection In Phishing Website Prediction

Neuro-Symbolic AI: Bringing a new era of Machine Learning

A Neuro-Symbolic AI System for Visual Question Answering in Pedestrian Video Sequences

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