How AI and Machine Learning are Revolutionizing Phishing Prevention

By: Jampula Navaneeth1

1Vel Tech University, Chennai, India

2International Center for AI and Cyber Security Research and Innovation, Asia University, Taiwan, Email: navaneethjampula@gmail.com

Abstract

In this article we are going to see how AI and Machine Learning are revolutionizing phishing prevention. Now a days we are seeing that AI is becoming large in many sectors, similarly in cyber attacks such as phishing is going to be prevented by the AI. AI is a model which resembles the human brains. To prevent phishing attacks AI and Machine Learning plays a major role. The value of using ML and AI methods is that similar organizations can parse large amounts of analyze data, to look for some sort of a pattern, which is usually determined by something non-normal that humans do, which is normally a sign of a cheat. This paper focuses on the further significance that has been assigned to functionalities of ML and AI in enhancing phishing prevention with the help of superior analysis of data and detection of unusual entities.

Keywords: Artificial Intelligence, Machine Learning, Models, Prevention

Introduction

In today’s digital world, organizations face a significant challenge: Forgery, theft, embezzlement, deceitfulness and any other related activities Which are normally regarded as immoral deeds fall under this category of crime we can summarize them as fraud. Fraud can be described as the unlawful or criminal act aimed at leading to gain of some economic value. It refers to a process of deliberately giving out or providing others with a wrong impression, fabrication, or processing the information or facts in a given situation in order to gain undue advantage or bring harm to other people. This paper intends to analyse different measures that are taken in protecting against and detecting fraud through the use of modern technology [1].

Background

Phishing refers to a cyberattack complete in sending an e-mail or a message to lure its targets to an actual web page where they enter personal data like usernames, passwords, and credit card numbers to the attacker’s benefit.

Figure 1: Process of phishing

Role of AI for preventing Phishing

Phishing stays highly effective because of a general population’s ignorance of possible risks connected with Internet usage and AI-based protection. As seen, few users realize such risks and the increasing trends in phishing threats and breach indicate the problem. By informing users on these threats, there are noticeable enhancements on the kind of protection they receive. BASIC training sessions on cybersecurity, the part of AI in security improvements, as well as various kinds of phishing attacks can assist people in improving their ability to protect themselves. The most important solutions to decrease phishing attacks worldwide and to raise users’ knowledge on how to protect their data, are awareness and education [1], [2].

Role of machine learning for preventing phishing

XGBoost, short for eXtreme Gradient Boosting is a machine learning algorithm that is famous for its efficacy especially in issues to do with fraud detection. It especially shines when handling affiliations in the data that are complex and not easily described in the traditional format of a straight line, which is essential in identifying the patterns of fraud that are continuously transforming. Learning from data which describe situations in the past, XGBoost can detect patterns/ anomalies which suggest fraud. Training on accurately labelled data makes the algorithm have a way of differentiating between the fraudulent transactions and the genuine ones. It is possible to note that organizations using XGBoost can also increase their potential in the detection of fraudulent activities by classifying the indicators of suspicious actions and decreasing the number of false positive, thereby enhancing the parameters of the fraud detection system [1].

Challenges and limitations

AI is being implemented and used in the cybersecurity industry for the ability of parsing data, understanding different patterns, and threat identification even though the traditional issues that relate to an adversary of human origin must still be attended to. However, the work of AI as a coherent defence mechanism is a current problem, which can be solved only by highly qualified specialists, because hackers are also very clever [2].

Data management is important since acquiring and processing data, storage of data, analysis of data and presentation of data in a format that is suitable demand a lot of resources to be developed.

Beyond 5G technology, CPS implementation requires large capitals of research, development, and infrastructure investments [3].

Results and Discussion

From this article we got to know the current practices of deploying AI and ML for phishing prevention, we have has found a number of fascinating truths numerous [4]. The increasing use of AI and ML in the specified area cybersecurity has been significant. These results emphasise that there is increasing awareness of the possibilities of AI and ML in enhancing the solidification of defensive strategies against cyber threats [5].

Conclusion

We can conclude that threat actors tirelessly adapt new types of attacks, the use of AI and machine learning components in counter-phishing measures is mandatory. Thus, by deploying the capabilities of Artificial Intelligence and Machine Learning, organizations will be able to foil the attempts of hackers who continuously try and breach corporate security in a world that is steadily moving online. You can now say that the revolution when it comes to avoiding phishing incidents is long overdue, and that this major shift is spearheaded by AI.

References

  1. P. Gupta, “Leveraging Machine Learning and Artificial Intelligence for Fraud Prevention,” SSRG-IJCSE, vol. 10, no. 5, pp. 47–52, May 2023, doi: 10.14445/23488387/IJCSE-V10I5P107.
  2. M. F. Ansari, P. Sharma, and B. Dash, “Prevention of Phishing Attacks Using AI-Based Cybersecurity Awareness Training,” vol. 3, pp. 61–72, Mar. 2022, doi: 10.47893/IJSSAN.2022.1221.
  3. A. Manoharan and M. Sarker, “Revolutionizing Cybersecurity: Unleashing the Power of Artificial Intelligence and Machine Learning for Next-Generation Threat Detection,” International Research Journal of Modernization in Engineering Technology and Science, vol. 4, p. 1, Dec. 2022, doi: 10.56726/IRJMETS32644.
  4. L. Triyono, R. Gernowo, P. Prayitno, M. Rahaman, and T. R. Yudantoro, “Fake News Detection in Indonesian Popular News Portal Using Machine Learning For Visual Impairment,” JOIV : International Journal on Informatics Visualization, vol. 7, no. 3, pp. 726–732, Sep. 2023, doi: 10.30630/joiv.7.3.1243.
  5. A. M. Widodo et al., “Port-to-Port Expedition Security Monitoring System Based on a Geographic Information System,” IJDSGBT, vol. 13, no. 1, pp. 1–20, Jan. 2024, doi: 10.4018/IJDSGBT.335897.
  6. Aldweesh, A., Alauthman, M., Al Khaldy, M., Ishtaiwi, A., Al-Qerem, A., Almoman, A., & Gupta, B. B. (2023). The meta-fusion: A cloud-integrated study on blockchain technology enabling secure and efficient virtual worlds. International Journal of Cloud Applications and Computing (IJCAC), 13(1), 1-24.
  7. M. Casillo, F. Colace, B. B. Gupta, A. Lorusso, F. Marongiu and D. Santaniello, “Blockchain and NFT: a novel approach to support BIM and Architectural Design,” 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakheer, Bahrain, 2022, pp. 616-620, doi: 10.1109/3ICT56508.2022.9990815.

Cite As

Navaneeth J. (2024) How AI and Machine Learning are Revolutionizing Phishing Prevention, Insights2Techinfo, pp.1

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