Butt Umer Ahmed, Amin Rashid, Aldabbas Hamza, Mohan Senthilkumar, Alouffi Bader, Ahmadian Ali
Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan.
Department of Computer Science, University of Chakwal, Chakwal, Pakistan.
Complex Intell Systems. 2023;9(3):3043-3070. doi: 10.1007/s40747-022-00760-3. Epub 2022 Jun 2.
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.
云计算是指个人计算机系统资产（特别是数据存储和处理能力）的按需可用性，无需客户输入。电子邮件通常用于个人或群体发送和接收数据。财务数据、信用报告和其他敏感数据经常通过互联网发送。网络钓鱼是欺诈者使用的一种技术，通过看似来自可信来源来从用户那里获取敏感数据。发送者可以通过在网络钓鱼电子邮件中误导来诱使你提供机密数据。主要问题是在发送和接收电子邮件时的电子邮件网络钓鱼攻击。攻击者使用电子邮件发送垃圾邮件数据，并在你打开和阅读电子邮件时接收你的数据。近年来，这对每个人来说都是一个大问题。本文使用不同的合法和网络钓鱼数据大小，检测新电子邮件，并使用不同的特征和算法进行分类。在衡量现有方法之后创建了一个修改后的数据集。我们创建了一个提取特征的逗号分隔值（CSV）文件和标签文件，应用了支持向量机（SVM）、朴素贝叶斯（NB）和长短期记忆（LSTM）算法。 本实验将网络钓鱼电子邮件的识别视为一个分类问题。根据比较和实施情况，SVM、NB和LSTM的性能更好，在检测电子邮件网络钓鱼攻击方面更准确。使用SVM、NB和LSTM分类器对电子邮件攻击进行分类时，准确率分别达到99.62%、97%和98%的最高水平。
