Phishing Attacks Detection Using Convolutional Dense Neural Network

R. Surendiran

doi:https://doi.org/10.59232/CYS-V1I1P104

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2023 | Article Id: CYS-V1I1P104 DOI: https://doi.org/10.59232/CYS-V1I1P104

Phishing Attacks Detection Using Convolutional Dense Neural Network

R. Surendiran

Received	Revised	Accepted	Published
28 May 2023	09 Jun 2023	22 Jun 2023	05 Jul 2023

Citation

R. Surendiran. “Phishing Attacks Detection Using Convolutional Dense Neural Network.” DS Journal of Cyber Security, vol. 1, no. 1, pp. 39-45, 2023.

Abstract

The term "cyber security" covers all aspects of safeguarding an existing performance, customers, and activities from internet threats. The advancing digital transformation has led to an increase in cybersecurity risks on a global scale. The possibilities for cybercriminals are made deeper by technology. Cyberattacks sometimes begin as phishing efforts to get secret user passwords. Attackers frequently employ phishing to deceive individuals into giving them access to networks and digital assets inside an organisation. Cybercriminals use security flaws to conduct attacks, gain unauthorised access, disable systems, and even charge a fee for access to be restored. The phishers use strategies to get beyond anti-phishing software and tools. Cybersecurity is still the best method for preventing phishing efforts, even though threat intelligence and perception solutions aid enterprises in identifying unusual traffic patterns. In light of this, the suggested Phishing attack detection (PAD) research project has developed an approach that uses convolutional dense neural networks to identify phishing threats (CDNNs). Initially, the data are pre-processed using a data mining technique to remove the noise from the data. Based on pre-processed data, CDNN is utilised to categorise the attacks. Finally, a maximum accuracy of suggested (PAD) of 97% was accomplished using existing random approaches.

Keywords

Cyber security, Artificial Intelligence, Deep Learning, Convolutional Dense Neural Networks, Phishing attack detection.

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