• Ayushi Soni
• Ayushi Srivastava
• Badal Singh
• Kaushal Pandey
• Ayush Mishra

The exponential growth of cyber threats, including phishing attacks, polymorphic malware, and network inventions, is inadequate to the inadequate traditional security solutions due to their fragmented design and responsive nature. This article introduces Authenticity Analyzer, a Chrome extension for real-time phishing recognition, malware analysis, network intrusion prevention, cybersecurity formations, and a machine learning (ML) control module. The use of data records such as Phishtank, MalwareBazaar, and CICIDS2017 has reached 98.2% accuracy for phishing recognition and 95.7% malware recognition, exceeding industry standards such as Google's safety and Virustotal. User survey (n = 500) showed that context-related threat warnings and educational interventions resulted in a 78% reduction in risky behavior. By bridging the gap between multi-vector threat detection and user awareness, this work highlights the need for uniform cybersecurity tools in reducing modern attacks.

Cybersecurity, machine learning, phishing recognition, network penetration, cyber recognition

"Authenticity Analyzer: A Chrome Extension for Cybersecurity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.e674-e682, May-2025, Available :http://www.jetir.org/papers/JETIR2505552.pdf

"Authenticity Analyzer: A Chrome Extension for Cybersecurity", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppe674-e682, May-2025, Available at : http://www.jetir.org/papers/JETIR2505552.pdf