MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience

Shwetambari Borade; Parshva Chetan Doshi; Darsh Bhavesh Patel

doi:10.17485/IJST/v17i10.148

Article

MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i10.148

Year: 2024, Volume: 17, Issue: 10, Pages: 870-880

Original Article

MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience

Shwetambari Borade^1*, Parshva Chetan Doshi², Darsh Bhavesh Patel²

¹Assistant Professor, Cyber Security, Shah & Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India
²Student, Cyber Security, Shah & Anchor Kutchhi Engineering College, Mumbai, Maharashtra, India

*Corresponding Author
Email: [email protected]

Received Date:19 January 2024, Accepted Date:30 January 2024, Published Date:23 February 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To enhance cyber security by implementing advanced algorithms to swiftly identify and neutralize phishing threats. Also, to bolster user protection, fortify data integrity, and ensure a resilient defense against evolving cyber threats. Methods: MaliceSpotter aims in classifying user-entered URLs by analysing 28 features, using algorithms like Logistic Regression, Random Forest, and KNN, combined via a Voting Classifier. Dataset on Kaggle provides diverse samples for evaluation. This methodology's unique aspects include multiple algorithm integration and the utilization of Kaggle as a data source. Findings: MaliceSpotter demonstrates a commendable accuracy of 95%, effectively classifying input URLs as phishing or legitimate. The system's uniqueness lies in its provision of a detailed report on URL behavior, facilitating informed decision-making. The implementation of ensemble learning is notable, particularly the introduction of the Voting Classifier. This approach leverages various algorithms, successfully incorporating bagging and voting concepts. Through the Voting Classifier, MaliceSpotter gains insights into the working of machine learning algorithms, enhancing the scrutiny of URL behavior. This innovative feature sets MaliceSpotter apart, offering a nuanced perspective on the reliability of URLs through the collective input of diverse algorithms. Novelty: MaliceSpotter uniquely combines diverse algorithms, leveraging a voting classifier for robust results. Continuously updating in real time, it meticulously dissects URLs into 28 parts, ensuring thorough scrutiny and effective detection.

Keywords: Phishing, Machine Learning, Web Security, Voting Classifier, Bagging

References

Rashid J, Mahmood T, Nisar MW, Nazir T. Phishing Detection Using Machine Learning Technique. In: 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH). (pp. 43-46) IEEE. 2020.
Lakshmanarao A, Rao PSP, Krishna MMB. Phishing website detection using novel machine learning fusion approach. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). (pp. 1164-1169) IEEE. 2021.
Alkawaz MH, Steven SJ, Hajamydeen AI, Ramli R. A Comprehensive Survey on Identification and Analysis of Phishing Website based on Machine Learning Methods. In: 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). (pp. 82-87) IEEE. 2021.
Alkawaz MH, Steven SJ, Hajamydeen AI. Detecting Phishing Website Using Machine Learning. In: 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA). (pp. 111-114) IEEE. 2020.
Paliath S, Qbeitah MA, Aldwairi M. PhishOut: Effective Phishing Detection Using Selected Features. In: 2020 27th International Conference on Telecommunications (ICT). (pp. 1-5) IEEE. 2020.
Mathankar S, Sharma SR, Wankhede T, Sahu M, Thakur S. Phishing Website Detection using Machine Learning Techniques. In: 2023 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP). (pp. 1-6) IEEE. 2023.
Jain S, Gupta C. A Support Vector Machine Learning Technique for Detection of Phishing Websites. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON). (pp. 1-6) IEEE. 2023.
Helmi RAA, Johar MGM, Hafiz MASBM. Online Phishing Detection Using Machine Learning. In: 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC). IEEE. 2023.
Mohammed M, Prasanth KK, Subhash SVS. Phishing Detection Using Machine Learning Algorithms. In: 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT). (pp. 921-924) IEEE. 2022.
Shoaib M, Umar MS. URL based Phishing Detection using Machine Learning. In: 2023 6th International Conference on Information Systems and Computer Networks (ISCON). (pp. 1-7) IEEE. 2023.
Mohammad RM, Thabtah F, Mccluskey L. An assessment of features related to phishing websites using an automated technique. In: 2012 International Conference for Internet Technology and Secured Transactions. (pp. 492-497) IEEE. 2012.
Tanimu J, Shiaeles S. Phishing Detection Using Machine Learning Algorithm. In: 2022 IEEE International Conference on Cyber Security and Resilience (CSR). (pp. 317-322) IEEE. 2022.
Puri N, Saggar P, Kaur A, Garg P. Application of ensemble Machine Learning models for phishing detection on web networks. In: 2022 Fifth International Conference on Computational Intelligence and Communication Technologies (CCICT). (pp. 296-303) IEEE. 2022.
Thahira A, John A. Phishing Website Detection Using LGBM Classifier With URL-Based Lexical Features. In: 2022 IEEE Silchar Subsection Conference (SILCON). (pp. 1-7) IEEE. 2023.

Copyright

© 2024 Borade et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)