| Title | A Novel Ensemble Machine Learning Method to Detect Phishing Attack |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Basit, Abdul, Zafar, Maham, Javed, Abdul Rehman, Jalil, Zunera |
| Conference Name | 2020 IEEE 23rd International Multitopic Conference (INMIC) |
| Keywords | cyberattacks, Electronic mail, feature extraction, Human Behavior, machine learning, machine learning classifiers, phishing, Phishing Detection, Phishing URLs, pubcrawl, Radio frequency, random forests, Support vector machines, Web Threat |
| Abstract | Currently and particularly with remote working scenarios during COVID-19, phishing attack has become one of the most significant threats faced by internet users, organizations, and service providers. In a phishing attack, the attacker tries to steal client sensitive data (such as login, passwords, and credit card details) using spoofed emails and fake websites. Cybercriminals, hacktivists, and nation-state spy agencies have now got a fertilized ground to deploy their latest innovative phishing attacks. Timely detection of phishing attacks has become most crucial than ever. Machine learning algorithms can be used to accurately detect phishing attacks before a user is harmed. This paper presents a novel ensemble model to detect phishing attacks on the website. We select three machine learning classifiers: Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Decision Tree (C4.5) to use in an ensemble method with Random Forest Classifier (RFC). This ensemble method effectively detects website phishing attacks with better accuracy than existing studies. Experimental results demonstrate that the ensemble of KNN and RFC detects phishing attacks with 97.33% accuracy. |
| DOI | 10.1109/INMIC50486.2020.9318210 |
| Citation Key | basit_novel_2020 |
A Novel Ensemble Machine Learning Method to Detect Phishing Attack