Abstract: The modern era of this digital age has a lot of importance for email communication for personal and professional purposes. But this has been a source for scams and cyber based threats where attackers use spam emails and send malware virus based emails where if a user opens the mail the viruses affect the device the user is using because of which the cyber attacker can access and manipulate your data for their personal use and benefits . So to avoid all these kind off outbreaks in this paper we are going to describe how can we filter these kind of emails to avoid the user from attempting to open these and become a victim to this scam ,this can be done using machine learning techniques like Support Vector Machine, Naive Bayes Classifier,Decision Trees which help classify these emails as spam and ham which means the emails of proper content and with malicious content are categorized based on which cyber scams can be prevented.
The creation of these kind of effective machine learning filtering systems are vital for prevention of scams present in this era because these are the kind of systems which can help prevent cyber scams we have even used classification techniques and deep learning based approaches like Artificial Neural Network which help improve accuracy and robustness in the spam email classification and methods like Random Forest and KNN are used for ensembling whereas NLP(Natural language Processing ) processes email text while keu attributes for classification are extracted by feature engineering. this approach involves a combination of various machine learning methods and efficiently trained models with proficient data for spam email detection.
Keywords: Email spam, SVM ,KNN,Random Forest,Decision Trees machine learning,NLP,Naive Bayes, cybersecurity, data manupulation, filtering techniques, email scam.
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DOI:
10.17148/IJARCCE.2024.13483
