Phishing Detection in Emails Using Machine Le...

Phishing Detection in Emails Using Machine Learning

Author Name : Vidyam Sree Vathsav Sharma, Perabathula Lokesh Sri Sai Kumar, Malla Vimal Sanathan, Sonali Sanjay Kubde, Sayali Arun Bhongade, Lakshmi Kiranmayi Chelluboyina

ABSTRACT

Phishing is a form of fraud in which the attacker tries to learn sensitive information such as login credentials or account information by sending as a reputable entity or person in email or other com- munication channels. Typically, a victim receives a message that appears to have been sent by a known contact or organization. The message contains malicious software targeting the user’s com- puter or has links to direct victims to malicious websites in order to trick them into divulging personal and financial information, such as passwords, account IDs or credit card details. Detection of phishing websites is a really important safety measure for most of the online platforms.

Evolving digital transformation has exacerbated cybersecurity threats globally. Digitization expands the doors wider to cybercriminals. Initially cyberthreats approach in the form of phishing to steal the confidential user credentials. Usually, Hackers will influence the users through phishing in order to gain access to the organization’s digital assets and networks. With security breaches, cybercriminals execute ransomware attack, get unauthorized access, and shut down systems and even demand a ransom for releasing the access. Anti-phishing software and techniques are circumvented by the phishers for dodging tactics. Though threat intelligence and behavioral analytics systems support organizations to spot the unusual traffic patterns, still the best practice to prevent phishing attacks is defended in depth. In this per- spective, the proposed research work has developed a model to detect the phishing attacks using machine learning (ML) algorithms like random forest (RF) and decision tree (DT). A standard legitimate dataset of phishing attacks from Kaggle was aided for ML processing. To analyze the attributes of the dataset, the proposed model has used feature selection algorithms like principal component analysis (PCA). Finally, a maximum accuracy of 97% was achieved through the random forest algorithm.