Phishing attacks, in which scammers send emails and other messages to con victims into providing their login credentials and personal information, snare millions of victims each year [43]. A variety of efforts aim to combat phishing through law enforcement, automated detection, and end-user education. Researchers have studied why people fall for phishing attacks; however, little research has be...

With an upsurge in the use of internet, there are various attacks being launched every day. These attacks target the vulnerabilities of various computer resources, such as, the operating system, web browsers, toolbars, etc. along with the susceptibility of the users due to lack of awareness about the possible scams. The existing solutions suffer various drawbacks. The website phishing solutions...

This paper presents a context-aware phishing threat detection model from users’ behavioral perspectives. The context of users’ information accesses is investigated to explore the users’ browsing behaviors that confront phishing situations. Large-scale experiments show that our approach achieves an accuracy of 0.9973 and an F1 score of 0.9311 for predicting the phishing threats of users’ next ac...

Phishing detection systems are principally based on the analysis of data moving from phishers to victims. In this paper we describe a novel approach for detecting phishing websites based on analysis of users’ online behaviours— i.e., the websites users have visited, and the data users have submitted to those websites. Such user behaviours can not be manipulated freely by attackers; detection ba...

The problem of Web phishing attacks has grown considerably in recent years and phishing is considered as one of the most dangerous Web crimes, which may cause tremendous and negative effects on online business. In a Web phishing attack, the phisher creates a forged or phishing website to deceive Web users in order to obtain their sensitive financial and personal information. Several conventiona...

Phishing has become a lucrative business for cyber criminals whose victims range from end users to large corporations and government organizations. Though Internet users are generally becoming more aware of phishing websites, cyber scammers come up with novel schemes that circumvent phishing filters and often succeed in fooling even savvy users. Recent studies to detect phishing and malicious w...

Phishing email detection is highly dependent on the accuracy of anti-phishing classifiers. Classifiers that use Machine-Learning techniques achieve highest phishing email classification accuracy results according to the literature. Using effective features in Machine-Learning is a critical step in raising classifiers detection accuracy. This study aims at evaluating a number of feature subset s...

In this paper, we have proposed a new approach named as " A Novel Approach for Predicting Phishing Websites using Map Reduce Framework " to overcome the difficulty and complexity in detecting and predicting phishing website. We proposed an efficient, resilient and effective approach that is based on using MapReduce framework, classification Data Mining algorithms and cluster methodology. Detect...

Phishing continues to be a problem for both individuals and organisations, with billions of dollars lost every year. We propose the use of nudges – more specifically social saliency nudges – that aim to highlight important information to the user when evaluating emails. We used Signal Detection Theory to assess the effects of both sender saliency (highlighting important fields from the sender) ...