Review on Phishing Attack Detection using Recurrent Neural Network

Phishing is a crime that involves the theft of personal information from users. Individuals, corporations, cloud storage, and government websites are all targets for the phishing websites. Anti-phishing technologies based on hardware are commonly utilised, while software-based options are preferred due to cost and operational considerations. Current phishing detection systems have no solution for problems like zero-day phishing assaults. To address these issues, a three-phase attack detection system called the Phishing Attack Detector based on Web Crawler was suggested, which uses a recurrent neural network to precisely detect phishing incidents. Based on the classification of phishing and non-phishing pages, it covers the input features Web traffic, web content, and Uniform Resource Locator (URL). Keywords: Attack detection, Recurrent Neural Network, Deep Learning.

Research on Value Investment of Start-up Companies

This study investigates how investors can strengthen their value investment by applying the SWOT analysis in the Start-up company context. By conducting a constructive study with two cases, we develop a construction for qualitative and quantitative reference information with the help of the literature on Start-ups and value investment, the data on CB insights.com. This reference information for two Start-ups comprises funding data, investors, web traffic, news articles, patent data, and regulatory filings. This study also associates each information element to the internal factor assessment and external factor assessment of two Start-ups and accordingly develops metrics regarding the value investment. In addition, it demonstrates the different nature of two Start-ups for operating business to highlight the divergent value metrics. The key contributions of this study are the developed construction for qualitative and quantitative reference information and concluding that the founding team, market, product, business model, and competition are important factors for the development of Start-up company and investors' decision-making. The results of this study, and particularly the developed criterion, build avenues for further research on Start-ups and value investment.

An Improved Framework for Content- and Link-Based Web-Spam Detection: A Combined Approach

In this modern era, people utilise the web to share information and to deliver services and products. The information seekers use different search engines (SEs) such as Google, Bing, and Yahoo as tools to search for products, services, and information. However, web spamming is one of the most significant issues encountered by SEs because it dramatically affects the quality of SE results. Web spamming’s economic impact is enormous because web spammers index massive free advertising data on SEs to increase the volume of web traffic on a targeted website. Spammers trick an SE into ranking irrelevant web pages higher than relevant web pages in the search engine results pages (SERPs) using different web-spamming techniques. Consequently, these high-ranked unrelated web pages contain insufficient or inappropriate information for the user. To detect the spam web pages, several researchers from industry and academia are working. No efficient technique that is capable of catching all spam web pages on the World Wide Web (WWW) has been presented yet. This research is an attempt to propose an improved framework for content- and link-based web-spam identification. The framework uses stopwords, keywords’ frequency, part of speech (POS) ratio, spam keywords database, and copied-content algorithms for content-based web-spam detection. For link-based web-spam detection, we initially exposed the relationship network behind the link-based web spamming and then used the paid-link database, neighbour pages, spam signals, and link-farm algorithms. Finally, we combined all the content- and link-based spam identification algorithms to identify both types of spam. To conduct experiments and to obtain threshold values, WEBSPAM-UK2006 and WEBSPAM-UK2007 datasets were used. A promising F-measure of 79.6% with 81.2% precision shows the applicability and effectiveness of the proposed approach.

Web Traffic Anomaly Detection using Stacked Long Short-Term Memory

An example of anomaly detection is detecting behavioral deviations in internet use. This behavior can be seen from web traffic, which is the amount of data sent and received by people who visit websites. In this study, anomaly detection was carried out using stacked Long Short-Term Memory (LSTM). First, stacked LSTM is used to create forecasting models using training data. Then the error value generated from the prediction on test data is used to perform anomaly detection. We conduct hyperparameter optimization on sliding window parameter. Sliding window is a sub-sequential data of time-series data used as input in the prediction model. The case study was conducted on the real Yahoo Webscope S5 web traffic dataset, consisting of 67 datasets, each of which has three features, namely timestamp, value, and anomaly label. The result shows that the average sensitivity is 0.834 and the average Area Under ROC Curve (AUC) is 0.931. In addition, for some of the data used, the window size selection can affect the sum of the sensitivity and AUC values. In this study, anomaly detection using stacked LSTM is described in detail and can be used for anomaly detection in other similar problems.Keywords: time-series data; sliding window; web traffic; window size. AbstrakSalah satu contoh deteksi anomali adalah mendeteksi penyimpangan perilaku dalam penggunaan internet. Perilaku ini dapat dilihat dari web traffic, yaitu jumlah data yang dikirim dan diterima oleh orang-orang yang mengunjungi situs web. Pada penelitian ini, deteksi anomali dilakukan menggunakan Long Short-Term Mermory (LSTM) bertumpuk. Pertama, LSTM bertumpuk digunakan untuk membuat model peramalan menggunakan data latih. Kemudian nilai error yang dihasilkan dari prediksi pada data uji digunakan untuk melakukan deteksi anomali. Kami melakukan optimasi hyperparameter pada parameter sliding window. Sliding window adalah data sub-sekuensial dari data runtun waktu yang digunakan sebagai input pada model prediksi. Studi kasus dilakukan pada dataset web traffic Yahoo Webscope S5 yang terdiri dari 67 dataset yang masing-masing memiliki tiga fitur yaitu timestamp, value, dan anomaly label. Hasil menunjukkan bahwa rata-rata sensitivitas sebesar 0.834 dan rata-rata Area Under ROC Curve (AUC) sebesar 0.931. Selain itu, untuk beberapa data yang digunakan, pemilihan window size dapat mempengaruhi jumlah dari nilai sensitivitas dan AUC. Pada penelitian ini, deteksi anomali menggunakan LSTM bertumpuk dijelaskan secara rinci dan dapat digunakan untuk deteksi anomali pada permasalahan lainnya yang serupa.Kata kunci: data runtun waktu; sliding window; web traffic; window size.

U.S. Housing as a Global Safe Asset: Evidence from China Shocks

This paper demonstrates that the measured stock of China's holding of U.S. assets could be much higher than indicated by the U.S. net international investment position data due to unrecorded historical Chinese in ows into an increasingly popular global safe haven asset: U.S. residential real estate. We first use aggregate capital ows data to show that the increase in unrecorded capital in ows in the U.S. balance of payment accounts over the past decade is mainly linked to in ows from China into U.S. housing markets. Then, using a unique web traffic dataset that provides a direct measure of Chinese demand for U.S. housing at the zip code level, we estimate via a difference-in-difference matching framework that house prices in major U.S. cities that are highly exposed to demand from China have on average grown 7 percentage points faster than similar neighborhoods with low exposure over the period 2010-2016. These average excess price growth gaps co-move closely with macro-level measures of U.S. capital in ows from China, and tend to widen following periods of economic stress in China, suggesting that Chinese households view U.S. housing as a safe haven asset.

Digital Communication: a technology of trust marketing

The paper represents the review of marketing management tools and technologies online: the concept of trust funnel, web-traffic temperature, digital marketing tools. The relevance of the work is determined by the following factors: first, globalization and digital revolution have irreversibly changed the trajectory of the marketing technology development path. Mass advertising has smoothly lost its influence, and companies began to focus more on the customer. The emergence of new digital media has led to new concept – digital marketing. Second, many organizations are using a combination of traditional and digital marketing channels; however, digital marketing is becoming more prominent as it allows for more precise tracking of resources and funds in relation to other traditional marketing channels. In conclusion, the authors highlight common elements in defining the concept of digital marketing: its connection with technological progress and the use of the Internet, leading us to social media. In the new digital age, the main function of marketing is to be in constant contact with users, customers, the community and other companies, provides relevant news and content that convey unique experiences, communicate with people, giving them the opportunity to interact with the company or brand.

DISEMBEDDEDNESS IN MACHINE LEARNING DATA WORK

Firms and research organizations require humans to annotate raw data to make it compatible with machine learning algorithms. These tasks are often outsourced to individuals worldwide through labor platforms or infrastructures that serve as marketplaces where labour is exchanged as a commodity. The firms that operate them consider workers as “independent contractors” without the social and economic benefits of traditional employment relations. This presentation explores the personal networks of Latin American data workers who train and verify data for machine learning algorithms from their homes. A series of in-depth interviews and an analysis of a self-completion questionnaire and web traffic data suggests that these workers are embedded of networks of trusts build on online and offline interactions. These findings show a continuation of exploitative supply chains in the current artificial intelligence market, where wealthy companies and research institutions in advanced economies profit from the economic and political situation of developing countries to access disembedded labor. This paper concludes by arguing that, though outsourced online labour, artificial intelligence developers not only extract value from their workers, but also indirectly from their communities and personal networks.

Role of COVID-19 and motionless communication on expected trends of mobility: an evidence from Italian and Turin data

The 2020 pandemic has been changing for months the everyday mobility of part of the world: we concentrate on one of the first areas hit by COVID-19, soon after China. One of the main elements of change is the consolidation of teleworking, which further prompted motionless communications. The emergency-induced reduction of the systematic travel demand has been counterbalanced by the increased volume of web traffic. As a result, communications which formerly required commuting or travel missions have been regularly performed motionless during the lockdown. All this is known, also by experience. The novelty is that this paper quantifies this phenomenon, with a focus on the city of Turin, Italy, and makes hypotheses on the post-COVID. Local mobility data, so as trends before and during the lockdown are presented, thereafter compared. Implications for the “new normal” ahead are fully elaborated, to reply to a pre-existing research question on the role of motionless communications in the future urban mobility management. Eventually, the paper provides directions to advance and create a reference for further transport policies, within the general research goal to contribute to advance scientific knowledge in this new transportation study topic.