Biblio
Phishing attacks have reached record volumes in recent years. Simultaneously, modern phishing websites are growing in sophistication by employing diverse cloaking techniques to avoid detection by security infrastructure. In this paper, we present PhishFarm: a scalable framework for methodically testing the resilience of anti-phishing entities and browser blacklists to attackers' evasion efforts. We use PhishFarm to deploy 2,380 live phishing sites (on new, unique, and previously-unseen .com domains) each using one of six different HTTP request filters based on real phishing kits. We reported subsets of these sites to 10 distinct anti-phishing entities and measured both the occurrence and timeliness of native blacklisting in major web browsers to gauge the effectiveness of protection ultimately extended to victim users and organizations. Our experiments revealed shortcomings in current infrastructure, which allows some phishing sites to go unnoticed by the security community while remaining accessible to victims. We found that simple cloaking techniques representative of real-world attacks- including those based on geolocation, device type, or JavaScript- were effective in reducing the likelihood of blacklisting by over 55% on average. We also discovered that blacklisting did not function as intended in popular mobile browsers (Chrome, Safari, and Firefox), which left users of these browsers particularly vulnerable to phishing attacks. Following disclosure of our findings, anti-phishing entities are now better able to detect and mitigate several cloaking techniques (including those that target mobile users), and blacklisting has also become more consistent between desktop and mobile platforms- but work remains to be done by anti-phishing entities to ensure users are adequately protected. Our PhishFarm framework is designed for continuous monitoring of the ecosystem and can be extended to test future state-of-the-art evasion techniques used by malicious websites.
As a cyber attack which leverages social engineering and other sophisticated techniques to steal sensitive information from users, phishing attack has been a critical threat to cyber security for a long time. Although researchers have proposed lots of countermeasures, phishing criminals figure out circumventions eventually since such countermeasures require substantial manual feature engineering and can not detect newly emerging phishing attacks well enough, which makes developing an efficient and effective phishing detection method an urgent need. In this work, we propose a novel phishing website detection approach by detecting the Uniform Resource Locator (URL) of a website, which is proved to be an effective and efficient detection approach. To be specific, our novel capsule-based neural network mainly includes several parallel branches wherein one convolutional layer extracts shallow features from URLs and the subsequent two capsule layers generate accurate feature representations of URLs from the shallow features and discriminate the legitimacy of URLs. The final output of our approach is obtained by averaging the outputs of all branches. Extensive experiments on a validated dataset collected from the Internet demonstrate that our approach can achieve competitive performance against other state-of-the-art detection methods while maintaining a tolerable time overhead.
Phishing attacks are prevalent and humans are central to this online identity theft attack, which aims to steal victims' sensitive and personal information such as username, password, and online banking details. There are many antiphishing tools developed to thwart against phishing attacks. Since humans are the weakest link in phishing, it is important to educate them to detect and avoid phishing attacks. One can argue self-efficacy is one of the most important determinants of individual's motivation in phishing threat avoidance behaviour, which has co-relation with knowledge. The proposed research endeavours on the user's self-efficacy in order to enhance the individual's phishing threat avoidance behaviour through their motivation. Using social cognitive theory, we explored that various knowledge attributes such as observational (vicarious) knowledge, heuristic knowledge and structural knowledge contributes immensely towards the individual's self-efficacy to enhance phishing threat prevention behaviour. A theoretical framework is then developed depicting the mechanism that links knowledge attributes, self-efficacy, threat avoidance motivation that leads to users' threat avoidance behaviour. Finally, a gaming prototype is designed incorporating the knowledge elements identified in this research that aimed to enhance individual's self-efficacy in phishing threat avoidance behaviour.
Phishing is the major problem of the internet era. In this era of internet the security of our data in web is gaining an increasing importance. Phishing is one of the most harmful ways to unknowingly access the credential information like username, password or account number from the users. Users are not aware of this type of attack and later they will also become a part of the phishing attacks. It may be the losses of financial found, personal information, reputation of brand name or trust of brand. So the detection of phishing site is necessary. In this paper we design a framework of phishing detection using URL.
Phishing attacks continue to be one of the most common attack vectors used online today to deceive users, such that attackers can obtain unauthorised access or steal sensitive information. Phishing campaigns often vary in their level of sophistication, from mass distribution of generic content, such as delivery notifications, online purchase orders, and claims of winning the lottery, through to bespoke and highly-personalised messages that convincingly impersonate genuine communications (e.g., spearphishing attacks). There is a distinct trade-off here between the scale of an attack versus the effort required to curate content that is likely to convince an individual to carry out an action (typically, clicking a malicious hyperlink). In this short paper, we conduct a preliminary study on a recent realworld incident that strikes a balance between attacking at scale and personalised content. We adopt different visualisation tools and techniques for better assessing the scale and impact of the attack, that can be used both by security professionals to analyse the security incident, but could also be used to inform employees as a form of security awareness and training. We pitched the approach to IT professionals working in information security, who believe this may provide improved awareness of how targeted phishing campaigns can impact an organisation, and could contribute towards a pro-active step of how analysts will examine and mitigate the impact of future attacks across the organisation.
Phishing is a form of cybercrime where an attacker imitates a real person / institution by promoting them as an official person or entity through e-mail or other communication mediums. In this type of cyber attack, the attacker sends malicious links or attachments through phishing e-mails that can perform various functions, including capturing the login credentials or account information of the victim. These e-mails harm victims because of money loss and identity theft. In this study, a software called "Anti Phishing Simulator'' was developed, giving information about the detection problem of phishing and how to detect phishing emails. With this software, phishing and spam mails are detected by examining mail contents. Classification of spam words added to the database by Bayesian algorithm is provided.
Phishing has increased tremendously over last few years and it has become a serious threat to global security and economy. Existing literature dealing with the problem of phishing is scarce. Phishing is a deception technique that uses a combination of technology and social engineering to acquire sensitive information such as online banking passwords, credit card or bank account details [2]. Phishing can be done through emails and websites to collect confidential information. Phishers design fraudulent websites which look similar to the legitimate websites and lure the user to visit the malicious website. Therefore, the users must be aware of malicious websites to protect their sensitive data [1]. But it is very difficult to distinguish between legitimate and fake website especially for nontechnical users [4]. Moreover, phishing sites are growing rapidly. The aim of this paper is to demonstrate phishing detection using fuzzy logic and interpreting results using different defuzzification methods.
The prevalence and effectiveness of phishing attacks, despite the presence of a vast array of technical defences, are due largely to the fact that attackers are ruthlessly targeting what is often referred to as the weakest link in the system - the human. This paper reports the results of an investigation into how end users behave when faced with phishing websites and how this behaviour exposes them to attack. Specifically, the paper presents a proof of concept computer model for simulating human behaviour with respect to phishing website detection based on the ACT-R cognitive architecture, and draws conclusions as to the applicability of this architecture to human behaviour modelling within a phishing detection scenario. Following the development of a high-level conceptual model of the phishing website detection process, the study draws upon ACT-R to model and simulate the cognitive processes involved in judging the validity of a representative webpage based primarily around the characteristics of the HTTPS padlock security indicator. The study concludes that despite the low-level nature of the architecture and its very basic user interface support, ACT-R possesses strong capabilities which map well onto the phishing use case, and that further work to more fully represent the range of human security knowledge and behaviours in an ACT-R model could lead to improved insights into how best to combine technical and human defences to reduce the risk to end users from phishing attacks.
In the last decade, numerous fake websites have been developed on the World Wide Web to mimic trusted websites, with the aim of stealing financial assets from users and organizations. This form of online attack is called phishing, and it has cost the online community and the various stakeholders hundreds of million Dollars. Therefore, effective counter measures that can accurately detect phishing are needed. Machine learning (ML) is a popular tool for data analysis and recently has shown promising results in combating phishing when contrasted with classic anti-phishing approaches, including awareness workshops, visualization and legal solutions. This article investigates ML techniques applicability to detect phishing attacks and describes their pros and cons. In particular, different types of ML techniques have been investigated to reveal the suitable options that can serve as anti-phishing tools. More importantly, we experimentally compare large numbers of ML techniques on real phishing datasets and with respect to different metrics. The purpose of the comparison is to reveal the advantages and disadvantages of ML predictive models and to show their actual performance when it comes to phishing attacks. The experimental results show that Covering approach models are more appropriate as anti-phishing solutions, especially for novice users, because of their simple yet effective knowledge bases in addition to their good phishing detection rate.
The development of internet comes with the other domain that is cyber-crime. The record and intelligently can be exposed to a user of illegal activity so that it has become important to make the technology reliable. Phishing techniques include domain of email messages. Phishing emails have hosted such a phishing website, where a click on the URL or the malware code as executing some actions to perform is socially engineered messages. Lexically analyzing the URLs can enhance the performance and help to differentiate between the original email and the phishing URL. As assessed in this study, in addition to textual analysis of phishing URL, email classification is successful and results in a highly precise anti phishing.
Phishing is an act of technology-based deception that targets individuals to obtain information. To minimize the number of phishing attacks, factors that influence the ability to identify phishing attempts must be examined. The present study aimed to determine how individual differences relate to performance on a phishing task. Undergraduate students completed a questionnaire designed to assess impulsivity, trust, personality characteristics, and Internet/security habits. Participants performed an email task where they had to discriminate between legitimate emails and phishing attempts. Researchers assessed performance in terms of correctly identifying all email types (overall accuracy) as well as accuracy in identifying phishing emails (phishing accuracy). Results indicated that overall and phishing accuracy each possessed unique trust, personality, and impulsivity predictors, but shared one significant behavioral predictor. These results present distinct predictors of phishing susceptibility that should be incorporated in the development of anti-phishing technology and training.
We propose a novel phishing detection architecture based on transparent virtualization technologies and isolation of the own components. The architecture can be deployed as a security extension for virtual machines (VMs) running in the cloud. It uses fine-grained VM introspection (VMI) to extract, filter and scale a color-based fingerprint of web pages which are processed by a browser from the VM's memory. By analyzing the human perceptual similarity between the fingerprints, the architecture can reveal and mitigate phishing attacks which are based on redirection to spoofed web pages and it can also detect “Man-in-the-Browser” (MitB) attacks. To the best of our knowledge, the architecture is the first anti-phishing solution leveraging virtualization technologies. We explain details about the design and the implementation and we show results of an evaluation with real-world data.