Modular Platform for Detecting and Classifying Phishing Websites Using Cyber Threat Intelligence

Phishing attacks are deceptive types of social engineering techniques that attackers use to imitate genuine websites in order to steal the login credentials and private data of the end-users. The continued success of these attacks is heavily attributed to the prolific adoption of online services and the lack of proper training to foster a security awareness mindset of online users. In addition to the financial and reputational damages caused by data breaches of individual users and businesses, cyber adversaries can further use the leaked data for various malicious purposes. In this work, a modular platform was introduced that facilitates accurate detection and automatic evaluation of websites visited by employees of a company or organization. The basis for this approach is a preceding website analysis, which is essential when hunting for potential threats from proxy logs. The platform contains three modules. Characterization of suspicious websites relies on a set of pre-defined features and a multi-stage threat intelligence technique, the functionality of which has been ascertained in initial tests on real data set

Phishing Websites Detection using Machine Learning

Tremendous resources are spent by organizations guarding against and recovering from cybersecurity attacks by online hackers who gain access to sensitive and valuable user data. Many cyber infiltrations are accomplished through phishing attacks where users are tricked into interacting with web pages that appear to be legitimate. In order to successfully fool a human user, these pages are designed to look like legitimate ones. Since humans are so susceptible to being tricked, automated methods of differentiating between phishing websites and their authentic counterparts are needed as an extra line of defense. The aim of this research is to develop these methods of defense utilizing various approaches to categorize websites. Specifically, we have developed a system that uses machine learning techniques to classify websites based on their URL. We used four classifiers: the decision tree, Naïve Bayesian classifier, support vector machine (SVM), and neural network. The classifiers were tested with a data set containing 1,353 real world URLs where each could be categorized as a legitimate site, suspicious site, or phishing site. The results of the experiments show that the classifiers were successful in distinguishing real websites from fake ones over 90% of the time

Intelligent Detection for Cyber Phishing Attacks using Fuzzy rule-Based Systems

Cyber phishing attacks are increasing rapidly, causing the world economy monetary losses. Although various phishing detections have been proposed to prevent phishing, there is still a lack of accuracy such as false positives and false negatives causing inadequacy in online transactions. This study constructs a fuzzy rule model utilizing combined features based on a fuzzy inference system to tackle the foreseen inaccuracy in online transactions. The importance of the intelligent detection of cyber phishing is to discriminate emerging phishing websites with a higher accuracy. The experimental results achieved an excellent accuracy compared to the reported results in the field, which demonstrates the effectiveness of the fuzzy rule model and the feature-set. The findings indicate that the new approach can be used to discriminate between phishing and legitimate websites. This paper contributes by constructing a fuzzy rule model using a combined effective feature-set that has shown an excellent performance. Phishing deceptions evolve rapidly and should therefore be updated regularly to keep ahead with the changes

A Survey on Phishing Attacks in Cyberspace

Phishing is a type of cyber attack in which cybercriminals use various advanced techniques to deceive people, such as creating fake webpages or malicious e-mails. The objective of phishing attacks is to gather personal data, money, or personal information from victims illegally. The primary aim of this review is to survey the literature on phishing attacks in cyberspace. It discusses different types of phishing attacks, such as spear phishing, e-mail spoofing, phone phishing, web spoofing, and angler phishing, as well as negative consequences they may cause for people. Phishing is typically carried out through different delivery methods such as e-mail, phone calls, or messaging. Victims of phishing are usually either not sensitive to privacy protection or do not have enough knowledge about social engineering attacks to know they are at risk. In addition, this paper introduces different methods for detecting phishing attacks. The last section discusses certain limitations of existing studies on phishing detection and potential future researc

A Survey of Website Phishing Detection Techniques

This article surveys the literature on website phishing detection. Web Phishing lures the user to interact with the fake website. The main objective of this attack is to steal the sensitive information from the user. The attacker creates similar website that looks like original website. It allows attacker to obtain sensitive information such as username, password, credit card details etc. This paper aims to survey many of the recently proposed website phishing detection techniques. A high-level overview of various types of phishing detection techniques is also presented

Website detection for phishing attack by using browser extension

Internet is a worldwide framework that can be utilized for sharing data, giving overall administrations and correspondence. However there are problem in term of security. In this thesis we focus on phishing attack. Phishing is a criminal trap of taking unfortunate casualties individual data by sending them spoofed email encouraging them to visit a produced site page that resembles a genuine one of an authentic organization and requests that the beneficiaries enter individual data, for example, Mastercard number, secret word and so forth. The current problem that occur are implanting a connection in an email that diverts to an unbound site that demands delicate data. Satirizing the sender deliver in an email to show up as a respectable source and demand delicate data. The main objective of this research is to investigate phishing attack are to investigate current method on phishing attack, to propose the arrangement exposure assault dependent against the browser extension alert, to evaluate the suggested arrangement inspection assault dependent on the website browser. The methodology that been use in this research is first planning, second phase analysis, third phase implementation, fourth phase conclusion and last phase is documentation. The result of this research show that rule base approach manage to detect the phishing website which are also connected to phishitank database. There are 3 rule that been use in this research out of 14 rule. After running the extension, "Developer mode" must be activated first since this made extension is an unloaded extension and furthermore not enlisted yet in Google Chrome. From this examination, it is clear that the built framework is actualized by utilizing extension in Google Chrome. In Conclusion, the goal of this thesis has been achieved by testing the framework in the Google Chrome internet browse