Muhammad Hassnain et al, International Journal of Computer Science and Mobile Computing, Vol.14 Issue.3, March- 2025, pg. 20-27 © 2025, IJCSMC All Rights Reserved, ZAIN Publications, Fridhemsgatan 62, 112 46, Stockholm, Sweden 20 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 7.056 IJCSMC, Vol. 14, Issue. 3, March 2025, pg.20 – 27 Detection and Identification of Novel Attacks in Phishing using AI Algorithms Muhammad Hassnain 1 ; Ibrahim Ahmed Qureshi 2 ; Dr. Ammar Haider 3 ¹ , ² , ³Department of Computer Science, National University of Computer and Emerging Sciences, Pakistan 1 muhammad.hassnain.jamshed@gmail.com; 2 ibrahim.aq67@gmail.com; 3 ammar.haider@nu.edu.pk DOI: https://doi.org/10.47760/ijcsmc.2025.v14i03.003 Abstract: Phishing attacks pose a significant threat to cybersecurity, exploiting human vulnerabilities to compromise sensitive information and undermine trust in digital communication. Currently, anti- phishing techniques that have been predominantly researched and used in software products include list- based and web-content based approaches. While these techniques provide excellent accuracy against previously known phishing attacks, they offer subpar accuracy against zero-day or novel attacks. Acknowledging this gap, our project, “Detection and Identification of Novel Attacks in Phishing using Artificial Intelligence Algorithms,” endeavours to elevate the detection precision for novel/zero -day phishing incidents. Our approach includes the use of visual similarity methods for enhanced detection where it analyses website screenshots. Our research started from a state-of-the-art dataset which we further enhanced by sourcing and analysing reported phishing pages of globally recognized brands from PhishTank, subsequently pairing these with screenshots of the corresponding legitimate websites. This strategic enrichment process aims to capture the sophisticated visual discrepancies between legitimate and phishing web pages, thereby facilitating the training of our model with a more comprehensive and representative dataset. Coupled with dataset enhancement, we have explored a diverse array of algorithms to ascertain the most effective approach for novel phishing detection. Our proposed approach achieved up to 84% accuracy in detection of phishing pages, compared to existing visual similarity based methods. Keywords: Novel Attacks, Zero Day Attacks, Phishing, Artificial Intelligence, Visual Similarity. I. INTRODUCTION The Internet has brought with it an incredible number of benefits as well as significant security threats. These threats have caught the attention of governments, businesses, and people all around the world. Phishing stands out among these dangers as a persistent threat that preys on the fundamental trust that supports online interactions. Phishing is a pervasive cyber threat that encompasses a range of deceptive tactics designed to steal sensitive information or deliver malware. It happens when the attacker pretends to be a legitimate website and tricks the victim into giving away crucial information including credentials. It’s imperative to recognize and comprehend the diverse types of phishing attacks to fortify defenses against this malicious menace. Some types of phishing