Harnessing Deep Learning for Advanced Phishing Detection

By: Jampula Navaneeth^1,2

¹Vel Tech University, Chennai, India

²International center for AI and Cyber Security Research and Innovations, Asia University, Taiwan

Email: navaneethjampula@gmail.com

Abstract

Now a days the usage of smart phones and internets are increased rapidly. As we all are habituated of using mobile phones and online transactions which makes quite easier to the hackers to phish the people. To prevent this type of cyberattacks deep learning is playing a vital role. As the technology is growing simultaneously the cyberattacks are increasing. In this study we are going to look at how the deep learning is utilized for detecting the advanced phishing. The purpose of this review is to give information about how deep learning works to detect phishing attacks.

In this article we are going to study how to prevent advanced phishing using deep learning, highlighting the methodologies and challenges.

Keywords: Phishing detection, Deep Learning, Cyberattacks

Introduction

Deep learning is the subset of machine learning which is based on artificial neural networks. Deep learning algorithms excel at identifying complex patterns within the large dataset [1]. When it comes to phishing, deep learning is used to analyze various components like email content, email headers, URL analysis and behavioral analysis [2]. By processing large amount of data, deep learning models can learn to distinguish between legitimate and phishing emails with remarkable accuracy. Which enables them to detect even the most advanced phishing attacks.

Background

In this stage we need to discuss on two main sub topics, which are firstly phishing and the other one deep learning.

Phishing

This the fraudulent attempt to obtain sensitive information such as passwords, usernames and bank details. In the area of cyberattacks phishing is the one of the most occurring attack [1]. Where attackers send fake messages, emails, or texts that appear to be from a legitimate source.

Deep Learning

In the previous works deep learning models played a major role to detect the phishing attacks. Deep learning particularly used for the tasks like image recognition, natural language processing, speech recognition and generative models like generating images , music [1].

Methodologies

The methodology of phishing detection using deep learning involves various stages with different type of operations. The first step involves collecting dataset and training, preprocessing the data. The second step involves the future extraction and continuously in the next step the data is visualized [3].Then based on various deep learning models the data can be treated with the different phishing classifications [4]. Deep learning involved in various application to detect phishing attacks.

Figure 1: Process of detecting phishing attacks

Challenges

The challenges that has to be considered in detecting advanced phishing are, Data quality in which high quality training data is required to develop effective deep learning models, Training and deploying deep learning models requires significant computational resources [1], [3].

Result and Discussion

Applying the various deep learning models to detect the advanced phishing has shown the significant results. Every deep learning models performs the various operations based on there requirements [1]. Overall harnessing deep learning for advanced phishing detection can significantly enhance the security and effectiveness of phishing detection systems [5].

Conclusion

Concluding that harnessing deep learning techniques for advanced phishing detection is a powerful approach to combat the evolving threat of phishing attaks. The various deep learning models are utilized in detecting the traps. And this is more useful in identifying in the large set of data compared to other techniques like machine learning.

References

1. N. Q. Do, A. Selamat, O. Krejcar, E. Herrera-Viedma, and H. Fujita, “Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions,” IEEE Access, vol. 10, pp. 36429–36463, 2022, doi: 10.1109/ACCESS.2022.3151903.
2. E. Benavides, W. Fuertes, S. Sanchez, and M. Sanchez, “Classification of Phishing Attack Solutions by Employing Deep Learning Techniques: A Systematic Literature Review,” in Developments and Advances in Defense and Security, Á. Rocha and R. P. Pereira, Eds., Singapore: Springer, 2020, pp. 51–64. doi: 10.1007/978-981-13-9155-2_5.
3. E. A. Aldakheel, M. Zakariah, G. A. Gashgari, F. A. Almarshad, and A. I. A. Alzahrani, “A Deep Learning-Based Innovative Technique for Phishing Detection in Modern Security with Uniform Resource Locators,” Sensors, vol. 23, no. 9, Art. no. 9, Jan. 2023, doi: 10.3390/s23094403.
4. M. Rahaman, C.-Y. Lin, P. Pappachan, B. B. Gupta, and C.-H. Hsu, “Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control,” Sensors, vol. 24, no. 13, Art. no. 13, Jan. 2024, doi: 10.3390/s24134157.
5. M. Rahaman, V. Arya, S. M. Orozco, and P. Pappachan, “Secure Multi-Party Computation (SMPC) Protocols and Privacy,” in Innovations in Modern Cryptography, IGI Global, 2024, pp. 190–214. doi: 10.4018/979-8-3693-5330-1.ch008.
6. Gupta, B. B., & Panigrahi, P. K. (2022). Analysis of the Role of Global Information Management in Advanced Decision Support Systems (DSS) for Sustainable Development. Journal of Global Information Management (JGIM), 31(2), 1-13.
7. Gupta, B. B., & Narayan, S. (2021). A key-based mutual authentication framework for mobile contactless payment system using authentication server. Journal of Organizational and End User Computing (JOEUC), 33(2), 1-16.

Cite As

Navaneeth J. (2024) Harnessing Deep Learning for Advanced Phishing Detection, Insights2Techinfo, pp.1

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