A LITERATURE SURVEY ON THE PHISHING WEBSITES

Registration ID: IJNRD_303662

Published ID: IJNRD2501306

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Mr. Chandan J, Mr . Akash B, Mr . Chinmay C K, Mr. Dhanush U Gowda, Asst . Prof. Keerthi v

Phishing Detection, Machine Learning, Phishing Statistics, Graph Plotting, Data Visualization

Phishing domains are the most active threats to security which are stealing sensitive information like log-in credentials, financial data, and other personal ID details based totally upon the trusting nature of unaware users. Blacklists and heuristic-based systems fail most of the time because these sophisticated attacks change every other day not to get found by the safety measures. To date, ML has emerged to be very effective in the detection of phishing sites. From analyzing the URL, feature-based domains and even the content, techniques have been employed to discover existing phishing sites. In this paper, a summary of some methods presented specifically for the purpose of detecting phishing websites is covered, focusing especially on most usually used algorithmic approaches within the discipline. This review assesses the performance of most techniques adopted, such as Random Forest, Decision Trees, Support Vector Machines (SVM), and ensemble learning in the detection of phishing sites. The review also talks of the present advancement that has passed into deep learning techniques, that is, the usage of LSTM networks and CNNs in more challenging tasks of phishing detection. The latest results from the studies indicate that ensemble-based hybrid methods, although their detection techniques are different in these models, often gained the best outcomes for recall, precision, and accuracy. This paper addresses the efficiencies of these methods but opens up some very critical areas that need further research. These are real time detection systems that have to be more efficient, zero day phishing attacks, and imbalanced datasets that make the machines less generalizable. Additionally, the dynamic nature of phishing approaches necessitates resistant and adaptive models; these should fit into becoming adaptive against adversarial attacks against misleading the detecting systems. One such area that would drive future work on phishing detection involves the improvement of machine learning models in terms of real time performances, including adversarial learning strategies, and using explainable artificial intelligence (XAI) to make the detection more visible and understandable. Conclusion of the article: This article discusses a few ways through which such future work might leverage advances in deep learning and hybrid techniques to enhance phishing detection systems further and concludes with suggestions on filling gaps.

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Mr. Chandan J, Mr . Akash B, Mr . Chinmay C K, Mr. Dhanush U Gowda, & Asst . Prof. Keerthi v (January-2025). A LITERATURE SURVEY ON THE PHISHING WEBSITES. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(1), d43-d48. https://ijnrd.org/papers/IJNRD2501306.pdf

Volume 10 Issue 1, January-2025

Pages : d43-d48

Paper Reg. ID: IJNRD_303662

Published Paper Id: IJNRD2501306

Research Area: Science and Technology

Author Type: Indian Author

Country: Bengaluru, karnataka, India

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