PhisherCop: Developing an NLP-Based Automated Tool for Phishing Detection

Abstract

Phishing poses a major security risk to organizations and individuals leading to loss of billions of dollars yearly. While risk communication serves as a tool to mitigate phishing attempts, it is imperative to create automated phishing detection tools. Numerous Natural Language Processing (NLP) approaches have been deployed to tackle phishing. However, phishing attempts continue to increase exponentially, which reiterates the need for more effective approach. To address this, we have developed an anti-phishing tool called; PhisherCop. PhisherCop is built upon Stochastic Gradient Descent classifier (SGD) and Support Vector Classifier (SVC) which showed an average accuracy of 96%, performing better than six other popular classifiers including: Decision Tree, Logistics Regression, Random Forest, Gradient Boosting Classifier, K-Nearest Neighbors and Multinomial Naive Bayes. Our tool is significant in distinguishing between phishing and legitimate content both over emails and text messages.

Methodological Approach for Identifying Websites with Infringing Content via Text Transformers and Dense Neural Networks

“…On the other hand, in the active identification of infringing sites, twenty-one [25,[28][29][30][31][32][33][34][35][64][65][66][67][68][69][70][71][72][73][74][75] significant research contributions were compiled. Among these, three [65][66][67] engage in non-technical actions such as manual search and reporting, contributing to the legal takedown of offending sites.…”

“…Six projects [28,29,[68][69][70][71] follow a technical route, involving surveillance and cyber patrolling within the IoE. Regrettably, only twelve initiatives [28][29][30][31][32][33][34][35][72][73][74][75] implement one or more AI/ML techniques for identifying infringing content on the IoE, and of these, only eight [28][29][30][31][32][33][34][35] are considered closely related studies. Notably, a single article [25] stands out for its purely technical approach to the defensive and active aspects.…”

“…NLP-Based Automation [31,33,35,75] Converts text found within IoE resources into detailed contextual representations, which are designed to detect similarities in potentially infringing content.…”

“…-Technical [28,29,[68][69][70][71] involves any technical procedure that allows for traversing the IoE to identify websites, links, URLs, P2P platforms, File Transfer Protocol (FTP) endpoints, torrents, and storage clouds where an identified infringing multimedia resource is residing. -AI/ML-driven [28][29][30][31][32][33][34][35][72][73][74][75] includes advanced schemes that harness the power of AI/ML algorithms to efficiently perform cyber patrolling by traversing the IoE using NLP techniques, Supervised Learning (SL), Non-Supervised Learning (NSL), and DL analyses to analyze, discover, and present potential infringing multimedia content.…”