Artificial Intelligence Techniques for Phishing Detection
M. Arivukarasi1, A. Antonidoss2
1M. Arivukarasi, Computer science engineering, Hindustan Institute of Technology and Science, Chennai, India.
2Dr. A. Antonidoss, Computer science engineering, Hindustan Institute of Technology and Science, Chennai, India.
Manuscript received on 25 August 2019. | Revised Manuscript received on 03 September 2019. | Manuscript published on 30 September 2019. | PP: 2330-2335 | Volume-8 Issue-11, September 2019. | Retrieval Number: I8499078919/2019©BEIESP | DOI: 10.35940/ijitee.I8499.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The objective of this undertaking is to apply neural systems to phishing email recognition and assess the adequacy of this methodology. We structure the list of capabilities, process the phishing dataset, and execute the Neural Network frameworks. we analyze its exhibition against that of other real Artificial Intelligence Techniques – DT , K-nearest , NB and SVM machine.. The equivalent dataset and list of capabilities are utilized in the correlation. From the factual examination, we infer that Neural Networks with a proper number of concealed units can accomplish acceptable precision notwithstanding when the preparation models are rare. Additionally, our element determination is compelling in catching the qualities of phishing messages, as most AI calculations can yield sensible outcomes with it.
Keywords: Neural Network, KNN, support vector machine, decision tree, Naive Bayes .
Scope of the Article: Artificial Intelligence and Machine Learning