Identifying Phishing Attacks

Title: Identifying Phishing Attacks
Permalink: identifying-phishing-attacks
Description: This research examines phishing attack techniques such as fake emails, social media scams, and scam calls while presenting detection methods including URL analysis, content monitoring, and machine learning algorithms to strengthen cybersecurity defenses.
Focus Keywords: Phishing Attacks, URL Analysis, Content Analysis, Machine Learning Algorithms, Cybersecurity, high-impact factor journal, journal with a DOI

International Journal of Engineering and Techniques – Volume 10 Issue 2, March 2024

www.ijetjournal.org

ISSN: 2395-1303

Surya Prakash Tripathi1, Gaurav Ameta2
1Dept. Cyber Security and Forensics, Parul University, India. Email: sptripathi2502@gmail.com
2Dept. Computer Science and Engineering, Parul University, India. Email: gaurav.ameta24442@paruluniversity.ac.in

Abstract

Phishing poses a threat to cybersecurity by attacking individuals and organizations around the world. This article examines techniques used in phishing attacks, such as fake emails, text messages, social media scams, and scam calls. It charts the evolution of these cyber threats by providing an in-depth look at the various techniques used by phishers, including polymorphic URLs, content obfuscation, and localization attacks. The research highlights various detection methods such as URL analysis, content monitoring, machine learning algorithms, behavioral analysis, headline analysis, and cross-validation techniques to identify phishing attempts effectively.

Keywords

Phishing Attacks, URL Analysis, Content Analysis, Machine Learning Algorithms, Behavioral Analysis, Cybersecurity

How to Cite

Surya Prakash Tripathi, Gaurav Ameta, “Identifying Phishing Attacks,” International Journal of Engineering and Techniques, Volume 10, Issue 2, March 2024.

Submit your research article: editorijetjournal@gmail.com
Tags: High-impact factor journal, UGC-approved journal, DOI publication, peer-reviewed journal, phishing detection, cybersecurity research, machine learning in security, fraud prevention strategies.

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