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Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

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Cybersecurity system to detect phishing attack and possible classification of spam and smishing SMS using deep learning algorithm
Author NameAffiliationPostcode
K Gowri Department of Computer Science, St.Peters Institute of Higher Education And Research, Chennai 600054, India 600054
S Brindha* Department of Computer Science and Application, St.Peters Institute of Higher Education And Research, Chennai 600054, India 600054
Abstract:
Phishing threats, especially those sent via SMS (known as smishing), are a big worry in the digital world. So better ways are needed to fight against phishing because cybercriminals keep changing their tactics. This study aims to make the online systems safer by using advanced technology called deep learning , specifically a method called the AlexNet-LSTM algorithm, to sort out smishing messages effectively. Phishing vulnerability depends on different things such as age, gender, how much time someone spends online, and how stressed they are. In this study, deep learning is used to find SMS threats, and different types of text-based attacks are figured out by using the AlexNet-LSTM algorithm. To evaluate the superiority of this new method and how well it performs, it is compared with traditional methods such as logistic regression, random forest, and support vector machine. The results show a big improvement in accuracy, up to 99.6%, proving the efficacy of deep learning to make the online systems safer against various tricky attacks.
Key words:  cyber security system  phishing attack  classification  deep learning
DOI：10.11916/j.issn.1005-9113.2023126
Clc Number:TP393
Fund:

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