Cloud-based email phishing attack using machine and deep learning algorithm

Abstract

Cloud computing refers to the on-demand availability of personal computer system assets, specifically data storage and processing power, without the client's input. Emails are commonly used to send and receive data for individuals or groups. Financial data, credit reports, and other sensitive data are often sent via the Internet. Phishing is a fraudster's technique used to get sensitive data from users by seeming to come from trusted sources. The sender can persuade you to give secret data by misdirecting in a phished email. The main problem is email phishing attacks while sending and receiving the email. The attacker sends spam data using email and receives your data when you open and read the email. In recent years, it has been a big problem for everyone. This paper uses different legitimate and phishing data sizes, detects new emails, and uses different features and algorithms for classification. A modified dataset is created after measuring the existing approaches. We created a feature extracted comma-separated values (CSV) file and label file, applied the support vector machine (SVM), Naive Bayes (NB), and long short-term memory (LSTM) algorithm. This experimentation considers the recognition of a phished email as a classification issue. According to the comparison and implementation, SVM, NB and LSTM performance is better and more accurate to detect email phishing attacks. The classification of email attacks using SVM, NB, and LSTM classifiers achieve the highest accuracy of 99.62%, 97% and 98%, respectively.

Anesthesia decision analysis using a cloud-based big data platform

“…Because medical records and information are now stored in disparate data formats, they are typically kept in comma-separated values (CSV) or table data format and can simply be stored on platforms utilizing these data formats [ 21 ]. The upload of data to the cloud is performed using DataX offline data synchronization software.…”

Business Email Compromise Phishing Detection Based on Machine Learning: A Systematic Literature Review

“…Butt et al [8] This paper proposes utilising SVM, LSTM, RF, LR, ANN, and NB algorithms for detecting BEC and phishing content in emails. The proposed models achieve an accuracy of 99.6%, 98%, 94.5%, 93.9%, 95%, and 97% in SVM, LSTM, RF, LR, ANN and NB, respectively.…”

“…Some researchers presented a comparative study of various supervised and unsupervised ML techniques to provide an effective BEC phishing detection model that provides the highest accuracy, precision, recall, and F-measure to detect phishing emails. For example, Butt et al [8], Ripa, Islam, and Arifuzzaman [41], and Chakraborty and Mondal [11] created a comparative study using various ML algorithms, including DT, SVM, LSTM, RF, LR, ANN, NB, KR, and DT, to identify a ML algorithm that provides the highest accuracy on a specific dataset. Other researchers provided hybrid ML-based techniques that combine two or more algorithms with changes in variables to provide better accuracy for the BEC phishing detection model.…”

“…This technique is a collection of algorithms that attempt to replicate the way the human brain works to discover hidden patterns and connections within a dataset. Neuronal systems, whether biological or synthetic, are what we mean when we talk about neural networks [8].…”

“…Even though HTML files are rarely utilised in commercial transactions, these attachments can nonetheless deceive unwary users. The target will be sent to a malicious URL if he or she clicks and downloads the attachment [8].…”