• Mohammad Naeem Kanyar

Phishing continues to be an effective route to account compromise because it exploits both technical weaknesses and ordinary human expectations. Defenses have expanded from blocklists and hand-written rules to machine-learning classifiers, deep-learning models, email authentication, multi-factor authentication, and cryptographic sign-in methods such as passkeys. These approaches solve different parts of the problem, and treating any one of them as a complete solution leaves important gaps. This review examines the main families of phishing defenses, explains what each can and cannot do, and draws practical lessons from research literature and technical standards. The paper uses a structured narrative review of peer-reviewed studies, established books, and authoritative guidance. It compares detection approaches, discusses dataset and evaluation problems, examines the difference between conventional multi-factor authentication and phishing-resistant authentication, and considers the role of user reporting and incident response. Across the reviewed literature, a consistent lesson is that strong results on a static dataset do not automatically translate into dependable protection in a changing environment. Models must be tested on unseen and later-collected data, false positives must be managed, and identity controls should reduce the value of credentials captured by a fraudulent site. The paper proposes a layered defense model that combines message and web filtering, multi-signal detection, phishing-resistant authentication, least-privilege access, easy reporting, and continuous evaluation. This is a literature review; it does not present a newly trained model, original dataset, or experimental performance claims. Its contribution is a practical synthesis intended to help researchers and organizations evaluate phishing defenses as a coordinated security program.

phishing, cybersecurity, phishing website detection, machine learning, deep learning, passkeys, authentication, social engineering, defense in depth.

"A Review of Phishing Defenses Machine-Learning Detection, Phishing-Resistant Authentication, and Human-Centered Security", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.13, Issue 10, page no.b766-b772, October-2026, Available :http://www.jetir.org/papers/JETIR2610177.pdf

"A Review of Phishing Defenses Machine-Learning Detection, Phishing-Resistant Authentication, and Human-Centered Security", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.13, Issue 10, page no. ppb766-b772, October-2026, Available at : http://www.jetir.org/papers/JETIR2610177.pdf