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Nowadays, proactive P2P worm (PRWORM) poses a latent threat to internet infrastructure and common users for the fatal vulnerabilities in homogeneous P2P software. It is more difficult to contain PRWORM because of its fast spread speed. Current techniques are not adequate to quarantine PRWORM, mainly because of their inaccuracy and slow response to attacks. The purpose of this paper is to propose an accurate and real‐time approach for PRWORM containment.

First, the authors present a new methodology to contain PRWORM via proper authentication of initiators of P2P communications. Second, three simple network protocols are proposed to fulfill automatic authentication.

Both simulations and strictly mathematical proof by Strand Space Model represent that the authors' work is able to accurately quarantine PRWORM in real time. Furthermore, proof shows the three network protocols are resistant to popular attacks such as man‐in‐the‐middle attack and replay attack.

First, the authors propose an authentication based method to contain proactive P2P worm and second, use strand space model to proof the effectiveness and security of the method.

The purpose of this paper is to focus on the design of a counterfeit network analyzer (CNA) for aggregating all the problematic product flows in order to discover any counterfeit distribution source, and to control the spread of counterfeit goods. The analyzer leverages radio frequency identification (RFID) information stored within the electronic product code (EPC) network.

The system architecture of the proposed CNA is first discussed. A case study of the system application in a Hong Kong pharmaceutical manufacturing company then presents the adoption process and the challenges encountered in such technologies.

Compared with traditional approaches, the results show that the proposed RFID solution is reliable and is capable of discovering counterfeit distributions, as well as reducing the detection costs by means of higher product movement visibility within the supply chain.

A prototype system has been constructed and trial implemented in a pharmaceutical manufacturing company. It proved to be of benefit to the manufacturer who is able to deter product counterfeiting effectively and visualize real‐time supply chain data automatically.

The proposed CNA is designed to overcome issues of information invisibility, which is considered a huge cost lost in the identification of counterfeit products, loss of sales and reputation associated with the counterfeiting, and ineffective product authentication in the manufacturing industry. This paper contributes to the RFID research in the counterfeiting area by studying the feasibility and practicality of shifting the focus of product identification from the traditional package redesign solutions to the visualization of the movement of the product (i.e. product supply chain) via the use of RFID and EPC. Furthermore, a pharmaceutical manufacturing site provided a case study for discussing the advantages, critical issues for implementation of the RFID system, and lessons learned.

Because of the continued use of mobile, cloud and the internet of things, the possibility of data breaches is on the increase. A secure authentication and authorization strategy is a must for many of today’s applications. Authentication schemes based on knowledge and tokens, although widely used, lead to most security breaches. While providing various advantages, biometrics are also subject to security threats. Using multiple factors together for authentication provides more certainty about a user’s identity; thus, leading to a more reliable, effective and more difficult for an adversary to intrude. This study aims to propose a novel, secure and highly stable multi-factor one-time password (OTP) authentication solution for mobile environments, which uses all three authentication factors for user authentication.

The proposed authentication scheme is implemented as a challenge-response authentication where three factors (username, device number and fingerprint) are used as a secret key between the client and the server. The current scheme adopts application-based authentication and guarantees data confidentiality and improved security because of the integration of biometrics with other factors and each time new challenge value by the server to client for OTP generation.

The proposed authentication scheme is implemented on real android-based mobile devices, tested on real users; experimental results show that the proposed authentication scheme attains improved performance. Furthermore, usability evaluation proves that proposed authentication is effective, efficient and convenient for users in mobile environments.

The proposed authentication scheme can be adapted as an effective authentication scheme to accessing critical information using android smartphones.

Facial recognition systems represent a viable solution to today’s hotels’ security and service challenges. The purpose of this study was to build and empirically validate a conceptual model that examined consumers’ willingness to create a profile based on biometric information disclosed via facial recognition systems.

Data were collected from 421 US general population consumers who stayed in hotels. The study used a confirmatory factor analysis to test the measurement model and a structural equation modeling approach to empirically validate the structural model.

It was found that the benefit of information disclosure was the strongest predictor of value of disclosure and that value of disclosure and privacy concerns influenced consumers’ willingness to disclose biometric information. In turn, consumers’ willingness to disclose biometric information and their desire to be loyal to hotels influenced consumers’ willingness to create a profile.

To the best of the author’s knowledge, this is the first study to examine profile creation and biometric information disclosure via facial recognition systems in hotels, a technology that is likely to disrupt the current authentication and service quality models in hotels. This study also advances the literature by expanding the scope of the privacy calculus by adding social rewards, and by elucidating the role of desires in service contexts.

This study aims to identify leather type and authenticity through optical coherence tomography.

Optical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.

The optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.

For the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

Libraries offer a diverse array of information resources‐both in electronic and print formats. The challenge for libraries is to provide access to all of these resources in an integrated and user‐friendly way. MAP (Millennium AccessPlus) meets this challenge with three different functions: WebBridge, MetaFind, and WebAccess Management. Together, these tools provide libraries with the ability to manage and control access to all of their information resources by providing smart linking, multi‐protocol meta‐searching, and authentication.

Blockchain is a distributed ledger, in which the blocks containing transaction details are connected chronologically to form a series of chains, thus raising the possibility of improving the process and innovating business model for the financial institutions. The purpose of this paper is to study the actual cases of Blockchain applied in Korea in 2017, so that a vision of business model innovation of financial institutions can be drawn.

The financial institutions in Korea are in the technology verification stage to introduce Blockchain technology. Since there is an insufficient amount of actual measurement data, case study method was adopted. The authors interviewed ICT officers of major banks in Korea. The purpose of the interview was to understand the relationship between Blockchain and business models of financial institutions, and the effects and challenges that Blockchain has on the business model of financial institutions.

From the perspective of financial institutions, the emergence of Blockchain does not just have technical significance – emergence of highly efficient database system – but has the possibility that if the business model of existing financial intermediaries disappears or get reduced, the financial services relying on them can disappear altogether, or some of them can be replaced, and financial transaction patterns of consumers can be changed. As a case studies researched for this paper, it was discovered that the distributed characteristic of Blockchain cannot be applied when actually developing financial services.

The purpose of this paper is to explore how technological and strategic developments enable new billing processes for mobile content services.

Interviews with practitioners are used as input for designing different archetypical role division models for billing and process models. The potential of these process models to reshape the mobile industry is evaluated on three criteria: convenience for the end‐user; potential resource barriers; and the fit with strategic interests of the actors involved.

Both technological advances and the introduction of new roles and strategies in the mobile domain enable the emergence of alternative billing methods. While network operator‐centric models remain relevant in the short term, in the longer term they will co‐exist with other models in which the customer transaction is owned by the content aggregator, the content provider, the ISP or the payment provider.

The research demonstrates the relevance of analysis at the process level in assessing the feasibility of new role division models at the value creation level.

The emergence of alternative billing providers is expected to change the power balance in the value network and assist in opening up the “walled garden”.

The analysis extends beyond existing discussions on billing in the mobile industry, which typically focus on the value network level, as the process level and the related resources are included. Moreover, the empirical data from the interviews with practitioners at various organizations provide new insights into the feasibility of these models in practice.

The purpose of this paper is to analyze the applicability of biometric systems in travel and explain how they can be used in response to today's increasing security problems.

This research is conceptual. Its analysis is based on an extensive review of literature on biometric systems.

Biometric systems can improve travel security without compromising consumer value, convenience, and privacy. Deployment of various applications of biometric systems (i.e. immigration/visitor management systems, trusted traveler programs) improve specific aspects of travel security. Biometric systems present challenges, associated with consumers' perceptions of system functionality, privacy, trust, and anxiety, which must be examined in the context of consumer adoption. To achieve synergy in travel information technology and provide benefits to all stakeholders, biometric systems must seamlessly integrate with other travel technologies, both intra‐firm and inter‐firm.

This research provides a domain statement for biometric systems in travel and stays at the foundation of a methodical approach for the study of biometric systems in travel. It offers a conceptual framework that asserts that an integrated deployment and adoption of biometric systems in travel can transform the current travel system into an ideal, more secure system. Further, this study formulates a number of propositions for further empirical examination of biometric systems in specific fields within travel.

This research provides specific suggestions to integrate biometric systems with the existing systems to achieve synergies and derive benefits for travel stakeholders.

Addressing the security‐privacy relationship, biometric systems have social implications. Travelers' concerns about privacy, fear of harm, trust, and anxiety are found to influence their view of biometric systems, with potential implications for adoption and use.

To date, there is scant academic research examining how biometric systems improve travel security. Thus, the position of this research is unique: it offers insight into a technology that is promising for both research and practitioners investigating the role of biometric systems in improving travel security and paves the way for a multitude of specific research directions.

The paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates.

This paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained.

It is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process.

A small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles.

The presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice.

The social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment.

In contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.