Abstract
Phishing is the act of attackers sending malicious emails to receivers in an effort to trick them into falling for a con. Normally, the intention is to persuade users to provide private information, such as system logins or financial data. Our project investigates such email phishing attacks using AI and ML to get a reasonable conclusion about which algorithm can spot these attacks the most successfully.
In terms of cybersecurity, a phishing attack is a cybercrime that aims to obtain a user’s personal information in order to carry out some destructive actions. This attack’s effects could lead to account takeover, privilege escalation, and other issues. And in order to lessen it, this study provides information on how to spot phishing emails so that businesses can correctly deal with them.
Natural Language Processing (NLP) [26], logistic regression, and fundamental AI ideas like CNN [27] are used, along with machine learning algorithms like KNN, Naive Bayers, and these. This study aims to identify the optimal machine learning algorithm that would provide the highest level of accuracy when it comes to phishing email detection.
“To improve the accuracy of detecting phishing emails we have implemented CNN which is an effective approach to fulfill the target. CNN [27] can learn to recognize tiny patterns and traits that may be challenging for humans to notice, they can be a great tool for detecting phishing emails. Handling this technique is very important, hence LSTM is introduced”. The CNN model’s LSTM [27] can be thought of as its brain. LSTM (Long Short-Term Memory) [27] is a form of recurrent neural network (RNN) that is frequently employed in tasks involving sequence prediction and natural language processing (NLP) [26]. It is intended to solve the vanishing gradients issue, which can arise in conventional RNNs when the gradients are extremely small and make the network struggle to learn long-term dependencies.
By understanding and executing everything we have observed that Logistic Regression is giving us the highest of 97.49% accuracy followed by KNN at 94%. This proves that how effective the model is been while handling the data and also detecting phishing emails.
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Sarkar, S., Yadav, A., Balachander, T. (2024). Email Phishing Detection Using AI and ML. In: R., A.U., et al. Deep Sciences for Computing and Communications. IconDeepCom 2023. Communications in Computer and Information Science, vol 2176. Springer, Cham. https://doi.org/10.1007/978-3-031-68905-5_31
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