Review of the machine learning methods in the classification of phishing attack

The development of computer networks today has increased rapidly. This can be seen based on the trend of computer users around the world, whereby they need to connect their computer to the Internet. This shows that the use of Internet networks is very important, whether for work purposes or access to social media accounts. However, in widely using this computer network, the privacy of computer users is in danger, especially for computer users who do not install security systems in their computer. This problem will allow hackers to hack and commit network attacks. This is very dangerous, especially for Internet users because hackers can steal confidential information such as bank login account or social media login account. The attacks that can be made include phishing attacks. The goal of this study is to review the types of phishing attacks and current methods used in preventing them. Based on the literature, the machine learning method is widely used to prevent phishing attacks. There are several algorithms that can be used in the machine learning method to prevent these attacks. This study focused on an algorithm that was thoroughly made and the methods in implementing this algorithm are discussed in detail

A Novel computer assisted genomic test method to detect breast cancer in reduced cost and time using ensemble technique

Breast cancer is the leading cause of death among women around the world. It is a primary malignancy for which genetic markers have revealed the ability for clinical decision making. It is a genetic disease that generates due to gene mutations, but the cost of a genetic test is relatively high for a number of patients in developing nations like India. The results of a genetic test can take a few weeks to determine cancer. This time duration influences the prognosis of genes since certain patients suffer from a high rate of malignant cell proliferation. Therefore, a computer-assisted genetic test method (CAGT) is proposed to detect breast cancer. This test method will predict the gene expressions and convert these expressions in the state of mutation (under-expression (-1), transition (0) overexpression (1)) and afterwards perform the classification to get the benign and malignant class in reduced time and cost. In the research work, machine learning techniques are applied to identify the most responsive genes of breast cancer on the premises of the clinical report of a patient and generated a CAGT. In the research work, the hard voting ensemble approach is applied to detect breast cancer on the basis of most responsive genes by CAGT which leads to improving 3.5% accuracy in cancer classification

VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity

Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection for the trusted websites - in particular against unseen phishing pages. This paper contributes VisualPhishNet, a new similarity-based phishing detection framework, based on a triplet Convolutional Neural Network (CNN). VisualPhishNet learns profiles for websites in order to detect phishing websites by a similarity metric that can generalize to pages with new visual appearances. We furthermore present VisualPhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner. We show that our method outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks

AN ENHANCEMENT ON TARGETED PHISHING ATTACKS IN THE STATE OF QATAR

The latest report by Kaspersky on Spam and Phishing, listed Qatar as one of the top 10 countries by percentage of email phishing and targeted phishing attacks. Since the Qatari economy has grown exponentially and become increasingly global in nature, email phishing and targeted phishing attacks have the capacity to be devastating to the Qatari economy, yet there are no adequate measures put in place such as awareness training programmes to minimise these threats to the state of Qatar. Therefore, this research aims to explore targeted attacks in specific organisations in the state of Qatar by presenting a new technique to prevent targeted attacks. This novel enterprise-wide email phishing detection system has been used by organisations and individuals not only in the state of Qatar but also in organisations in the UK. This detection system is based on domain names by which attackers carefully register domain names which victims trust. The results show that this detection system has proven its ability to reduce email phishing attacks. Moreover, it aims to develop email phishing awareness training techniques specifically designed for the state of Qatar to complement the presented technique in order to increase email phishing awareness, focused on targeted attacks and the content, and reduce the impact of phishing email attacks. This research was carried out by developing an interactive email phishing awareness training website that has been tested by organisations in the state of Qatar. The results of this training programme proved to get effective results by training users on how to spot email phishing and targeted attacks