Predicting Phishing Websites using Neural Network trained with Back-Propagation

Phishing is increasing dramatically with the development of modern technologies and the global worldwide computer networks. This results in the loss of customer’s confidence in e-commerce and online banking, financial damages, and identity theft. Phishing is fraudulent effort aims to acquire sensitive information from users such as credit card credentials, and social security number. In this article, we propose a model for predicting phishing attacks based on Artificial Neural Network (ANN). A Feed Forward Neural Network trained by Back Propagation algorithm is developed to classify websites as phishing or legitimate. The suggested model shows high acceptance ability for noisy data, fault tolerance and high prediction accuracy with respect to false positive and false negative rates

Experimental Case Studies for Investigating E-Banking Phishing Techniques and Attack Strategies

Phishing is a form of electronic identity theft in which a combination of social engineering and web site spoofing techniques are used to trick a user into revealing confidential information with economic value. The problem of social engineering attack is that there is no single solution to eliminate it completely, since it deals largely with the human factor. This is why implementing empirical experiments is very crucial in order to study and to analyze all malicious and deceiving phishing website attack techniques and strategies. In this paper, three different kinds of phishing experiment case studies have been conducted to shed some light into social engineering attacks, such as phone phishing and phishing website attacks for designing effective countermeasures and analyzing the efficiency of performing security awareness about phishing threats. Results and reactions to our experiments show the importance of conducting phishing training awareness for all users and doubling our efforts in developing phishing prevention techniques. Results also suggest that traditional standard security phishing factor indicators are not always effective for detecting phishing websites, and alternative intelligent phishing detection approaches are needed

Tutorial and Critical Analysis of Phishing Websites Methods

The Internet has become an essential component of our everyday social and financial activities. Internet is not important for individual users only but also for organizations, because organizations that offer online trading can achieve a competitive edge by serving worldwide clients. Internet facilitates reaching customers all over the globe without any market place restrictions and with effective use of e-commerce. As a result, the number of customers who rely on the Internet to perform procurements is increasing dramatically. Hundreds of millions of dollars are transferred through the Internet every day. This amount of money was tempting the fraudsters to carry out their fraudulent operations. Hence, Internet users may be vulnerable to different types of web threats, which may cause financial damages, identity theft, loss of private information, brand reputation damage and loss of customers’ confidence in e-commerce and online banking. Therefore, suitability of the Internet for commercial transactions becomes doubtful. Phishing is considered a form of web threats that is defined as the art of impersonating a website of an honest enterprise aiming to obtain user’s confidential credentials such as usernames, passwords and social security numbers. In this article, the phishing phenomena will be discussed in detail. In addition, we present a survey of the state of the art research on such attack. Moreover, we aim to recognize the up-to-date developments in phishing and its precautionary measures and provide a comprehensive study and evaluation of these researches to realize the gap that is still predominating in this area. This research will mostly focus on the web based phishing detection methods rather than email based detection methods

Hybrid features-based prediction for novel phish websites

Phishers frequently craft novel deceptions on their websites and circumvent existing anti-phishing techniques for insecure intrusions, users’ digital identity theft, and then illegal profits. This raises the needs to incorporate new features for detecting novel phish websites and optimizing the existing anti-phishing techniques. In this light, 58 new hybrid features were proposed in this paper and their prediction susceptibilities were evaluated by using feature co-occurrence criterion and a baseline machine learning algorithm. Empirical test and analysis showed the significant outcomes of the proposed features on detection performance. As a result, the most influential features are identified, and new insights are offered for further detection improvement

Herding Vulnerable Cats: A Statistical Approach to Disentangle Joint Responsibility for Web Security in Shared Hosting

Hosting providers play a key role in fighting web compromise, but their ability to prevent abuse is constrained by the security practices of their own customers. {\em Shared} hosting, offers a unique perspective since customers operate under restricted privileges and providers retain more control over configurations. We present the first empirical analysis of the distribution of web security features and software patching practices in shared hosting providers, the influence of providers on these security practices, and their impact on web compromise rates. We construct provider-level features on the global market for shared hosting -- containing 1,259 providers -- by gathering indicators from 442,684 domains. Exploratory factor analysis of 15 indicators identifies four main latent factors that capture security efforts: content security, webmaster security, web infrastructure security and web application security. We confirm, via a fixed-effect regression model, that providers exert significant influence over the latter two factors, which are both related to the software stack in their hosting environment. Finally, by means of GLM regression analysis of these factors on phishing and malware abuse, we show that the four security and software patching factors explain between 10\% and 19\% of the variance in abuse at providers, after controlling for size. For web-application security for instance, we found that when a provider moves from the bottom 10\% to the best-performing 10\%, it would experience 4 times fewer phishing incidents. We show that providers have influence over patch levels--even higher in the stack, where CMSes can run as client-side software--and that this influence is tied to a substantial reduction in abuse levels

Performance Assessment of some Phishing predictive models based on Minimal Feature corpus

Phishing is currently one of the severest cybersecurity challenges facing the emerging online community. With damages running into millions of dollars in financial and brand losses, the sad tale of phishing activities continues unabated. This led to an arms race between the con artists and online security community which demand a constant investigation to win the cyberwar. In this paper, a new approach to phishing is investigated based on the concept of minimal feature set on some selected remarkable machine learning algorithms. The goal of this is to select and determine the most efficient machine learning methodology without undue high computational requirement usually occasioned by non-minimal feature corpus. Using the frequency analysis approach, a 13-dimensional feature set consisting of 85% URL-based feature category and 15% non-URL-based feature category was generated. This is because the URL-based features are observed to be more regularly exploited by phishers in most zero-day attacks. The proposed minimal feature set is then trained on a number of classifiers consisting of Random Tree, Decision Tree, Artificial Neural Network, Support Vector Machine and Naïve Bayes. Using 10 fold-cross validation, the approach was experimented and evaluated with a dataset consisting of 10000 phishing instances. The results indicate that Random Tree outperforms other classifiers with significant accuracy of 96.1% and a Receiver’s Operating Curve (ROC) value of 98.7%. Thus, the approach provides the performance metrics of various state of art machine learning approaches popular with phishing detection which can stimulate further deeper research work in the evaluation of other ML techniques with the minimal feature set approach

Reeling in Big Phish with a Deep MD5 Net

Phishing continues to grow as phishers discover new exploits and attack vectors for hosting malicious content; the traditional response using takedowns and blacklists does not appear to impede phishers significantly. A handful of law enforcement projects — for example the FBI\u27s Digital PhishNet and the Internet Crime and Complaint Center (ic3.gov) — have demonstrated that they can collect phishing data in substantial volumes, but these collections have not yet resulted in a significant decline in criminal phishing activity. In this paper, a new system is demonstrated for prioritizing investigative resources to help reduce the time and effort expended examining this particular form of online criminal activity. This research presents a means to correlate phishing websites by showing that certain websites are created by the same phishing kit. Such kits contain the content files needed to create the counterfeit website and often contain additional clues to the identity of the creators. A clustering algorithm is presented that uses collected phishing kits to establish clusters of related phishing websites. The ability to correlate websites provides law enforcement or other potential stakeholders with a means for prioritizing the allocation of limited investigative resources by identifying frequently repeating phishing offenders

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