Advancements in Phishing Detection: Automated Systems in Real-World Scenarios

Year : 2024 | Volume : 11 | Issue : 02 | Page : 12 17
    By

    S. Umamaheswari,

  • S. Saranya,

  1. Professor, Department of Information Technology, C. Abdul Hakeem College of Engineering and Technology/Anna University, Tamil Nadu, India
  2. Professor, Department of Information Technology, C. Abdul Hakeem College of Engineering and Technology/Anna University, Tamil Nadu, India

Abstract

The goal of the abstract is to offer an automated method that uses login URLs to identify real-world scenarios. Phishing is a type of cyberattack that involves social engineering, when malefactors trick victims into providing their login credentials via a login form that sends the information to a hostile site. In this research, we offer a system that uses URL analysis to detect phishing websites by comparing machine learning and deep learning methodologies. The legitimate class in the majority of modern state-of-the-art phishing detection technologies consists of homepages without login fields. Instead, we utilize the login page URL in both classes, since we believe it to be a lot more realistic case scenario, and we show that testing with legitimate login page URLs yields a significant false-positive rate for existing methodologies. Additionally, we train a base model with old datasets and test it with new URLs, using datasets from different years to illustrate how models lose accuracy with time. Additionally, we run a frequency analysis across active phishing domains to find various tactics used by phishers in their campaigns. In order to validate these claims, we have developed a new dataset called Phishing Index Login URL (PILU-90K), which consists of 30K phishing URLs and 60K authentic URLs, such as index and login webpages.

Keywords: Phishing index, Frequency, traffic, PyCharm, python libraries, naïve bayes

[This article belongs to Journal of Telecommunication, Switching Systems and Networks ]

How to cite this article:
S. Umamaheswari, S. Saranya. Advancements in Phishing Detection: Automated Systems in Real-World Scenarios. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(02):12-17.
How to cite this URL:
S. Umamaheswari, S. Saranya. Advancements in Phishing Detection: Automated Systems in Real-World Scenarios. Journal of Telecommunication, Switching Systems and Networks. 2024; 11(02):12-17. Available from: https://journals.stmjournals.com/jotssn/article=2024/view=165773


Browse Figures

References

  1. Maddireddy BR, Maddireddy BR. AI-Based Phishing Detection Techniques: A Comparative Analysis of Model Performance. Unique Endeavor in Business & Social Sciences. 2022 Jun 30;1(2):63-77.
  2. Alkhalil Z, Hewage C, Nawaf L, Khan I. Phishing attacks: A recent comprehensive study and a new anatomy. Frontiers in Computer Science. 2021 Mar 9;3:563060.
  3. Alzahrani A. Coronavirus social engineering attacks: Issues and recommendations. International Journal of Advanced Computer Science and Applications. 2020;11(5).
  4. Pandey N, Pal A. Impact of digital surge during Covid-19 pandemic: A viewpoint on research and practice. International journal of information management. 2020 Dec 1;55:102171.
  5. Patel P, Sarno DM, Lewis JE, Shoss M, Neider MB, Bohil CJ. Perceptual representation of spam and phishing emails. Applied cognitive psychology. 2019 Nov;33(6):1296-304.
  6. Chaudhry JA, Chaudhry SA, Rittenhouse RG. Phishing attacks and defenses. International journal of security and its applications. 2016 Jan 1;10(1):247-56.
  7. Hijji M, Alam G. A multivocal literature review on growing social engineering based cyber-attacks/threats during the COVID-19 pandemic: challenges and prospective solutions. Ieee Access. 2021 Jan 1;9:7152-69.
  8. Sahingoz OK, Buber E, Demir O, Diri B. Machine learning based phishing detection from URLs. Expert Systems with Applications. 2019 Mar 1;117:345-57.
  9. Rao RS, Pais AR. Jail-Phish: An improved search engine based phishing detection system. Computers & Security. 2019 Jun 1;83:246-67.
  10. Ramanathan V, Wechsler H. phishGILLNET—phishing detection methodology using probabilistic latent semantic analysis, AdaBoost, and co-training. EURASIP Journal on Information Security. 2012 Dec;2012:1-22.
  11. Jakobsson M, Myers S, editors. Phishing and countermeasures: understanding the increasing problem of electronic identity theft. John Wiley & Sons; 2006 Dec 5.
  12. Zhang W, Gupta S, Lian X, Liu J. Staleness-aware async-sgd for distributed deep learning. arXiv preprint arXiv:1511.05950. 2015 Nov 18.
Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 06/07/2024
Accepted 10/07/2024
Published 23/07/2024
401

Login


My IP

PlumX Metrics