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Open Access
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S. Umamaheswari, S. Saranya,
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- Professor, Professor Department of Information Technology, C. Abdul Hakeem College of Engineering and Technology/Anna University, Department of Information Technology, C. Abdul Hakeem College of Engineering and Technology/Anna University Tamil Nadu, Tamil Nadu India, India
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Abstract
nThe 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.
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Keywords: Phishing index, Frequency, traffic, PyCharm, python libraries, naïve bayes
n[if 424 equals=”Regular Issue”][This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]
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nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n
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Browse Figures
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References
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- 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.
- 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.
- Alzahrani A. Coronavirus social engineering attacks: Issues and recommendations. International Journal of Advanced Computer Science and Applications. 2020;11(5).
- 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.
- 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.
- Chaudhry JA, Chaudhry SA, Rittenhouse RG. Phishing attacks and defenses. International journal of security and its applications. 2016 Jan 1;10(1):247-56.
- 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.
- 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.
- Rao RS, Pais AR. Jail-Phish: An improved search engine based phishing detection system. Computers & Security. 2019 Jun 1;83:246-67.
- 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.
- Jakobsson M, Myers S, editors. Phishing and countermeasures: understanding the increasing problem of electronic identity theft. John Wiley & Sons; 2006 Dec 5.
- Zhang W, Gupta S, Lian X, Liu J. Staleness-aware async-sgd for distributed deep learning. arXiv preprint arXiv:1511.05950. 2015 Nov 18.
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Journal of Telecommunication, Switching Systems and Networks
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| Volume | 11 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 6, 2024 | |
| Accepted | July 10, 2024 | |
| Published | July 23, 2024 |
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