Abstract
This paper explores machine learning techniques and evaluates their performances when trained to perform against datasets consisting of features that can differentiate between a Phishing Website and a safe one. This capability of telling these sites apart from one another is vital in the modern-day internet surfing. As more and more of our resources shift online, one vulnerability and a leak of sensitive information by someone could bring everything down in a connected network. This paper's objective through this research is to highlight the best technique for identifying one of the most commonly occurring cyberattacks and thus allow faster identification and blacklisting of such sites, therefore leading to a safer and more secure web surfing experience for everyone. To achieve this, we describe each of the techniques we look into in great detail and use different evaluation techniques to portray their performance visually. After pitting all of these techniques against each other, we have concluded with an explanation in this paper that Random Forest Classifier does indeed work best for Phishing Website Detection.
Highlights
Phishing Attacks are the most common ways of attack in the digital world these days
A random forest classifier consists of a large number of decision trees that work as an ensemble
The kernel is very useful here because it can make a non-separable problem into a separable problem by adding more dimensions to it, and the number of dimensions depends on the number of features each sample has; some of the kernels that we found compelling are Linear Kernel, Polynomial Kernel, and the Radial Basis Function (RBF) kernel
Summary
Phishing Attacks are the most common ways of attack in the digital world these days. Any method of communication can be used to target an individual to trick them into leaking confidential data in a fake environment, which can later be used to harm the sole victim or even an entire business depending on the attacker's intent and the type of data leaked. Security Hackers issued thirty-five fraudulent instructions via the SWIFT network to illegally transfer almost 1 billion US dollars from the Federal Reserve Bank of New York account that belonged to Bangladesh Bank. Out of these 35 instructions, 5 of them successfully transferred 101 million dollars, with 20 million traced to Sri Lanka and 81 million traced to the Philippines. Most of the money transferred to the Philippines were collected into four personal accounts [2] The method of this attack has been suspected to be a Dridex malware. We focus on training machine learning models that can detect phishing web pages apart from real web pages.
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