High Accuracy Phishing Detection Based on Convolutional Neural Networks

The persistent growth in phishing and the rising volume of phishing websites has led to individuals and organizations worldwide becoming increasingly exposed to various cyber-attacks. Consequently, more effective phishing detection is required for improved cyber defence. Hence, in this paper we present a deep learning-based approach to enable high accuracy detection of phishing sites. The proposed approach utilizes convolutional neural networks (CNN) for high accuracy classification to distinguish genuine sites from phishing sites. We evaluate the models using a dataset obtained from 6,157 genuine and 4,898 phishing websites. Based on the results of extensive experiments, our CNN based models proved to be highly effective in detecting unknown phishing sites. Furthermore, the CNN based approach performed better than traditional machine learning classifiers evaluated on the same dataset, reaching 98.2% phishing detection rate with an F1-score of 0.976. The method presented in this pa-per compares favourably to the state-of-the art in deep learning based phishing website detection

Trademark Vigilance in the Twenty-First Century: An Update

The trademark laws impose a duty upon brand owners to be vigilant in policing their marks, lest they be subject to the defense of laches, a reduced scope of protection, or even death by genericide. Before the millennium, it was relatively manageable for brand owners to police the retail marketplace for infringements and counterfeits. The Internet changed everything. In ways unforeseen, the Internet has unleashed a tremendously damaging cataclysm upon brands—online counterfeiting. It has created a virtual pipeline directly from factories in China to the American consumer shopping from home or work. The very online platforms that make Internet shopping so convenient, and that have enabled brands to expand their sales, have exposed buyers to unwittingly purchasing fake goods which can jeopardize their health and safety as well as brand reputation. This Article updates a 1999 panel discussion titled Trademark Vigilance in the Twenty-First Century, held at Fordham Law School, and explains all the ways in which vigilance has changed since the Internet has become an inescapable feature of everyday life. It provides trademark owners with a road map for monitoring brand abuse online and solutions for taking action against infringers, counterfeiters and others who threaten to undermine brand value

An Evasion Attack against ML-based Phishing URL Detectors

Background: Over the year, Machine Learning Phishing URL classification (MLPU) systems have gained tremendous popularity to detect phishing URLs proactively. Despite this vogue, the security vulnerabilities of MLPUs remain mostly unknown. Aim: To address this concern, we conduct a study to understand the test time security vulnerabilities of the state-of-the-art MLPU systems, aiming at providing guidelines for the future development of these systems. Method: In this paper, we propose an evasion attack framework against MLPU systems. To achieve this, we first develop an algorithm to generate adversarial phishing URLs. We then reproduce 41 MLPU systems and record their baseline performance. Finally, we simulate an evasion attack to evaluate these MLPU systems against our generated adversarial URLs. Results: In comparison to previous works, our attack is: (i) effective as it evades all the models with an average success rate of 66% and 85% for famous (such as Netflix, Google) and less popular phishing targets (e.g., Wish, JBHIFI, Officeworks) respectively; (ii) realistic as it requires only 23ms to produce a new adversarial URL variant that is available for registration with a median cost of only $11.99/year. We also found that popular online services such as Google SafeBrowsing and VirusTotal are unable to detect these URLs. (iii) We find that Adversarial training (successful defence against evasion attack) does not significantly improve the robustness of these systems as it decreases the success rate of our attack by only 6% on average for all the models. (iv) Further, we identify the security vulnerabilities of the considered MLPU systems. Our findings lead to promising directions for future research. Conclusion: Our study not only illustrate vulnerabilities in MLPU systems but also highlights implications for future study towards assessing and improving these systems.Comment: Draft for ACM TOP

What attracts vehicle consumers’ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?

Purpose: The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint. Design/methodology/approach: A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel Naïve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint. Findings: The big data analytics argue that “cost-effectiveness” characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior. Research limitations/implications: The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation. Originality/value: Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective

From Amazon\u27s Domination of E-Commerce to Its Foray into Patent Litigation: Will Amazon Succeed as The District of Amazon Federal Court ?

Modern-day consumers often expect instant gratification. Instead of shopping via printed catalogues and retail stores, consumers flock to the convenience of online shopping platforms, like Amazon. On these platforms, consumers have instant access to items they need, anytime, and anywhere. The popularity of these platforms to both consumers and sellers of items has also ushered in a wave of counterfeit products to these platforms. Technology giant Amazon has a pervasive counterfeit problem that has been harming the legitimacy of its retail operation for some time. Amazon had previously employed a hands-off approach to counterfeits and left sellers to resolve disputes amongst themselves. Only recently has Amazon employed various programs aimed at removing infringing and counterfeit listings. In April 2019, Amazon launched a new anti-counterfeit enforcement protocol called the Utility Patent Neutral Evaluation Procedure. Amazon’s program aims to combat utility patent infringement on the Amazon Marketplace. This Recent Development will evaluate the new protocol as an alternative to traditional patent litigation pathways, examine Amazon’s previous attempts at curbing infringement, and will offer solutions to improve the efficacy of this program

February 1, 2019 - Panel 4: Online Platforms: Trademark Rights and Relevance

Marc Greenberg, Professor of Law, Golden Gate University School of Law (moderator) Marc Cooperman, Attorney, Banner Witcoff Xinghao Wang, Director, Global IP Enforcement, Alibaba Group Eric Gelwicks, Director, Business & Legal Affairs, Live Nation Dan Chen, Senior Partner, G.M., Unitalen IP Consulting LLC Patchen Haggerty, Partner, Perkins Coie Michael Kelly, Senior Corporate Counsel, IP, Amazon David Franklyn, Professor of Law, Golden Gate University School of La