Abstract

     Physical layer security (PLS) in Internet-of-Things (IoT) has attracted great attention in recent years. As an important and intrinsic property in an IoT system, node mobility is not fully investigated when designing a PLS scheme. Existing PLS works with mobility are designed with typical random mobile models, which cannot represent a real scenario. Considering this challenge, we propose a friendly jamming handover scheme to protect the downlink transmission from a controller to an actuator, with the help of multiple cooperative jammers to fight against a mobile conscious eavesdropper. In particular, we first introduce a conscious eavesdropping model (CEM) to characterize a real mobile wiretapping scenario. Second, we present a handover scheme to enhance security in a mobile scenario and then define handover efficiency to discuss the handover performance. Finally, numerical results are provided to verify the effectiveness of the proposed handover scheme, which demonstrates that our scheme can efficiently fight against a conscious mobile eavesdropper.

Introduction

     Internet of Things (IoT) is expected as a promising technique to achieve ubiquitous connectivity and intelligent information processing for the intelligent future life [1, 2]. Since the IoT is penetrating our daily life, the security and privacy are of the utmost importance [3–5]. Physical layer security (PLS) techniques have been attracted growing interests for the scenarios with resource-constrained IoT sensors and actuators [6–8]. A PLS scheme taking advantage of the unique properties of wireless channels can guarantee the information-theoretic communication security, regardless of the computing capability of eavesdroppers [9]. Various PLS schemes have been investigated to enhance secrecy in IoT networks, including multi-antenna beamforming and precoding [10, 11], artificial noise (AN) [12], intermittent cooperative jamming [13], and sociability-enabled PLS [8, 14, 15].

Conclusion

     This work investigated secure downlink transmmission in an IoT system with a mobile eavesdropper, and proposed a handover scheme to enhance PLS with the help of multiple cooperative jammers. In order to reflect the real situation, the conscious eavesdropping model was proposed to compare with the typical RWP model. Moreover, we introduced a metric called handover efficiency to reflect the handover performance. The numerical results verified that the handover scheme could efficiently improve the secrecy performance and effectively confront the conscious eavesdropper. The proposed scheme is feasible to provide security enhancement in many real mobile eavesdropping scenarios.

Highlights

Abstract

JEL classification

Keywords

1. Introduction

2. Background

3. Methodology

4. Insights from interviews

5. Investigation of 100 consortia

6. A taxonomy of industry consortia

7. Conclusions and implications

Declaration of Competing Interest

Acknowledgments

Annex A. List of consortia and their cluster allocation

Appendix B. Supplementary data

References

Abstract

In mobile telecommunications, numerous formal and quasi-formal standard setting organizations (SSOs) exist that are dedicated to technology standardization. Yet, hundreds of more or less informal industry consortia have been established that also deal with technical standards. Some of these aim to develop standards themselves – and thus compete with existing, formal standard setting organizations – while others complement existing organizations or relate to them in other ways. Based on a novel dataset covering 100 consortia in the field of mobile telecommunications as well as interviews, we aim to explore and understand the role and diversity of standards-related consortia. We investigate their purpose, functioning, their members’ motives, the position they occupy in the standardization landscape, and the way they interact with formal standard setting. On the basis of our analysis, we propose a taxonomy of standards-related consortia consisting of six groups: large industry and technology influencers, high-level concept developers, established standards developers, young technology specialists, small industry and technology influencers, and SSO-hosted industry drivers. We believe the resulting taxonomy to be relevant for researchers, practitioners and policymakers alike. It sheds light on the intricate landscape of industry consortia, and offers transparency on the peculiarities of these organizations. Hence, it can build the basis for an evaluation of potential memberships as well as policies and regulations.

1. Introduction

Since the 1980s, the mobile telecommunications industry has evolved rapidly, from various first generation analog standards, to second-generation GSM, third-generation UMTS/WCDMA, and fourth-generation LTE standards.1 This evolution has not only changed the performance of telecommunication networks, such as data rates and latency, but in many respects also the underlying technologies. Standards impact potential uses as well as organizations and individuals, as we see with 3G, which was the result of a convergence between traditional telecommunications and the IT industry (Leiponen, 2008). For 5G standards in the making, this convergence is happening on an even larger scale, involving the Internet of Things (IoT), Industry 4.0, various vertical sectors, and more.

1- Introduction

2- Related work

3- Proposed the cognitive agent paradigm and design issues

4- Problem formulation and solution

5- Performance analysis

6- Conclusion

References

Abstract

The service of mobile network develops rapidly nowadays, which generates various computing and resource-intensive applications, such as Internet of vehicles and virtual reality. Mobile edge computing (MEC) is close to data source and users, so terminals can execute tasks at the edge of network. In this way, the heavy load on core network can be relieved and tasks can be executed effectively. However, the demands of users vary from each other and users move all the time. It is difficult for the existing way of service supply to meet demands of all users. Cognitive Agent (CA) is put forward in this paper to help users cache and execute tasks on MEC in advance. In detail, CA is used to build personalized model combined with users’ behavior data. At the same time, it uses Long short-term memory neural network to forecast the moving trajectory of terminal equipment and the service types to be requested, uses the prediction result to generate caching strategy, cache business and shorten the delay of task execution. Besides, to further reduce the stress on MEC, we propose the collaboration of computing, communicating and caching resource with neighboring users’ equipment. To verify the effectiveness of CA, we build a model that assesses the performance of the system. Finally, we design a simulation experiment to execute resource request and resource collaboration. The result of the experiments show that CA can improve the efficiency of communication network, relieve the stress on network and improve the quality of services to users.

Introduction

At present, emerging information services and applications increase rapidly with the development of wireless communication and Internet of Things (IoT) [1,2]. More and more intelligent devices, such as wearable devices, remote medical system and virtual/augmented reality (VR/AR), need advanced communication technology and computing ability to guarantee ultra-reliable low-latency communication (URLLC), which is a great challenges for network suppliers [3,4]. Besides, wireless access device in large quantity outputs massive quantity of data all the time [5]. The reasonable use of valuable information is greatly significant for improving the quality of services supplied to users [6]. Mobile edge computing (MEC) supplies infrastructures of communication, computing and caching at the network edge that is close to data source or users [7]. It offers cloud services and information technology environment for edge applications and supplies high-quality communication services to users [8–13]. The advantages of the emerging service model MEC can be summarized below: (i). Due to be close to users or data source, some problems can be solved, such as long delay and heavy data traffic. It well supports the real-time and bandwidth-dense Internet of Things facilities, especially for the emotion-aware multimedia systems [14–16]. Taking unmanned driving for example, automobile needs to calculate running speed and the distance from others and sense surroundings in real time.

1- Introduction

2- Mobile banking in developing countries

3- Egypt and the U.S. – insights into innovation adaptation based on cultural differences

4- Theoretical framework and research model

5- Method

6- Results

7- Discussion and conclusion

8- Limitations and future research

References

Abstract

This study provides insights into the potential impact of country culture on consumers’ perceptions toward usage of new innovative technological services. Focusing on mobile banking (m-banking), this work compares responses from three distinct consumer segments, including– 1) consumers living in Egypt, 2) consumers from Egypt who are living in the U.S. and 3) U.S. consumers. The study utilizes constructs from the Technology Acceptance Model (TAM) including, perceived ease of use and perceived usefulness, along with perceived risk, trust and social influence to examine the differences between these three distinct consumer segments’ usage intentions toward mobile banking. The hypothesized model was tested using structural equation modeling (SEM). Results indicate that country culture (both primary and secondary) can, to some degree, influence consumers’ perceptions and intentions toward mobile banking. Implications and future research suggestions are provided.

Introduction

Recent years have witnessed major advances in the tools used to provide financial services to consumers both business to business (B-to-B) and business to consumers (B-to-C) (Laukkanen, 2007). Many of the most impactful and important advancements have been in the area of mobile banking. Zhoua, Lu, and Wang (2010) define mobile banking or m-banking as “the use of mobile terminals such as cellphones and personal digital assistants (PDAs) to access banking networks via the wireless application protocol (WAP)” (p.760). It represents a means to obtain banking services needed to administer financial affairs through mobile devices (Anderson, 2010). Mobile banking as an innovative technology is valuable for key institutions in the financial sector (e.g., traditional banks and other lending organizations) but also has the ability to improve the quality of life of underserved populations (Malaquias and Hwang, 2016). For consumers of all types, mobile-banking allows financial transactions to be undertaken from any place, at any time (Zhou, 2012). It permits consumers to make payments, transfer money, manage bank accounts and buy and sell stocks and other financial instruments using multiple types of mobile devices (Gu et al., 2009; Laukkanen, 2007). Mobile banking, if embraced, could have a significant impact in developing, nonwestern countries where multitudes of consumers do not have access to traditional banking services or the cost of such services are prohibitive (Gutierrez and Singh, 2013). Despite impressive advantages, the usage penetration of m-banking services by traditional banking establishments is still limited in many countries (see – Akturan and Tezcan, 2012; Alalwan et al., 2016; Lin, 2011; Malaquias and Hwang, 2016; Zhou, 2012).

Nowadays, retailers are interested in how customer preferences regarding service quality are changing due to the adoption of different devices for shopping purposes in both the desktop and mobile contexts. To answer this question, this paper first replicates, in the mobile commerce (m-commerce) context, the results from Blut et al. (2015), who conducted a meta-analytic review of electronic service quality. Replication results question the robustness and generalizability of the conceptualization in the mobile service quality context. Thereby, practitioners and academics are encouraged to adapt a customer-centric approach in organizing marketing practices. The replication extends the conceptualization of electronic service quality by considering a unique dimension named ubiquity of services, defined as the retailer’s ability to provide offers based on location and time. To reveal psychological mechanisms explaining the results of the replication study, a follow-up study draws on these contextual factors. In this context, this study uses a quasi-experimental approach by utilizing propensity score matching to account for self-selection effects to examine differences between desktop and mobile device users. As a result, this research contributes to the literature by identifying contextual boundary conditions regarding the shopping trip intentions and risk perceptions of mobile device users and desktop device users. Based on the results, major implications for retailers and further research are given.

Introduction

Nowadays, retailers are interested in how customer preferences regarding service quality are changing due to the adoption of different devices for shopping purposes in both the desktop and mobile contexts (e.g., Kannan and Li, 2017; Kumar, 2018; Marketing Science Institute, 2018; Souiden et al., 2018). Whereas the desktop context is mainly represented by laptops and desktop computers, customers are increasingly using smartphones and tablets as primary devices for shopping in the mobile context (Criteo, 2018). This classification is reasonable, since recent research indicates that tablets act as complementary devices for smartphones and as substitutes for laptop or desktop computers, respectively (Xu et al., 2017). In order to better understand and satisfy customer needs, and therefore to generate greater revenue, retailers are advised to adopt a customer-centric approach in organizing their marketing practices around distinct customer groups (e.g., mobile device users and desktop device users) rather than product categories (Althuizen, 2018; Crecelius et al., 2019). Consequently, the main goal of this research is to compare shopping trip perceptions and intentions of e and m-commerce users in order to enable customer-centric marketing practices. To achieve this goal, a replication study and a follow-up study will be conducted. First, the replication study is justified by the rise of mobile devices as primary shopping device (Criteo, 2018). In this context, retailers are still struggling to deliver satisfactory levels of mobile service quality (MSQ), resulting in lower conversion rates and higher shopping cart abandonment rates compared to the electronic counterpart (de Haan et al., 2018; Huang et al., 2018; Kaatz et al., 2019).

Despite mobile applications being at the frontier of mobile computation technologies, security issues pose a threat to their adoption and diffusion. Recent studies suggest that security violations could be mitigated through improved security behaviors and attitudes, not just through better technologies. Existing literature on behavioral security suggests that one of the main predictors of users’ perceptions of security is their perceived privacy concerns. Using communication privacy management theory (CPM), this study examines the effects of privacyrelated perceptions, such as privacy risk and the effectiveness of privacy policies, on the security perceptions of mobile app users. To empirically test the proposed theoretical model, two survey studies were conducted using mobile apps requesting less sensitive information (n = 487) and more sensitive information (n = 559). The findings show that the perceived privacy risk negatively influences the perceived security of the mobile apps; the perceived effectiveness of a privacy policy positively influences user perceptions of mobile app security; and perceived privacy awareness moderates the effect of perceived privacy risk on the perceived security of mobile apps. The results also suggest that users have different privacy-security perceptions based on the information sensitivity of the mobile apps. Theoretical and practical implications are discussed.

Introduction

Mobile apps have become an inherent part of everyday life. They have dominated individuals’ digital habits due to the progress of mobile technologies, mobile access to high-speed internet, and the interactivity of mobile phone interfaces. From communicating to entertaining, mobile apps provide a variety of useful features that cause individuals to spend a great amount of time on their use (Reychav et al., 2019). A recent study demonstrated that individuals in the US spend an average of three and a half hours per day on mobile devices, with mobile apps comprising 90 % of internet time on smartphones and 77 % on tablets (Wurmser, 2018). As a result, a plethora of mobile apps are released into app marketplaces every day, to respond to demands for online shopping, games, finance management, and other tasks that users can complete with their mobile devices (Balapour, Reychav, Sabherwal, & Azuri, 2019; Balapour and Sabherwal, 2017). One report suggests that an average of 6140 mobile apps were released through the Google Play Store every day from the third quarter of 2016 to the first quarter of 2018 (Statista, 2018). However, despite these high numbers, most individuals in the US are hesitant to download mobile apps, with the number of downloads at an average of zero per month (Perez, 2017).

Conventional mobile communication systems often use one single channel for data transmission, i.e., mobile devices use cellular network to transfer multimedia information. However, if attackers successfully hijack the single transmission channel, they can recover the communicated data. Focused on this issue, we introduce a Multichannel Communication System (MSYM), which aims to improve the data communication security for Android devices. The key idea of our approach is to leverage the diversity of communication mechanisms (e.g., Wi-Fi/cellular network, Bluetooth, and SMS) for transferring sensitive data in a secure way. More specifically, we use the VpnService interface provided by the Android platform to intercept the network data delivered by a sender program. Then, we split the network data into different fragments and improve the security by disordering and encrypting them via multiple transmission channels. When the target Android device receives the data fragments from different channels, it can decrypt and reorder them to reassemble the original data. In the end, we reuse the VpnService interface to inject the network data into the receiver program. Our approach can be deployed in Android devices to secure communication without the need of modifying the communication programs. In the evaluation, as a proof of concept, we implemented our approach on Android system. The experimental results show that our prototype system can secure data transmission with moderate performance cost.

Introduction

Mobile communication plays an increasingly important role in recent years, as more and more people exchange information via mobile devices. For example, over 2 billion people are using the instant messaging apps (e.g., WeChat and WhatsApp) on their mobile phones for exchanging messages, pictures and videos [6]. In addition, 90% of enterprises utilize mobile communication to boost productivity and streamline various business processes. Traditionally, most mobile devices only use one single wireless transmission channel (e.g., cellular network) for mobile communication. However, due to the openness of the wireless channel, it is possible for advanced attackers to eavesdrop the transmission and recover the sensitive data. For instance, adversaries can make use of bogus base stations to conduct man-in-the-middle (MITM) attack on modern Samsung mobile devices and obtain the sensitive information [23]. To prevent the communication data from being eavesdropped and recovered, a conventional solution is to leverage cryptographic protocols to perform data encryption and endpoint authentication. For example, the Wi-Fi Protected Access (WPA) and its subsequent standards (WPA2, WPA3) attempt to secure the wireless networks by applying multiple security protocols [13]. However, they may still suffer from eavesdropping attacks like the KRACK [35] attack. This attack shows that an attacker can exploit the vulnerability of WPA2 to read previously encrypted information. On the other hand, most mobile applications adopt the client-server model, which requires that all the communication data between two or more clients should be firstly forwarded to a central server. Nevertheless, some service providers may not be fully trusted. Taking the messaging server as an example, once it is hacked by adversaries, the recorded information would be leaked.

1- Literature review

2- Overview of studies

3- Study 1

4- Study 2

5- General discusssion

References

Abstract

The authors conduct two studies to examine how time urgency affects consumer responses to mobile reward apps. For participants who have made high progress toward reaching goals, short expiration dates (“collect 10 stamps for a free coffee by tomorrow”) cause to-go framing (”2 more stamps to go”) to be more effective than to-date framing (”8 stamps collected so far”), but for participants who have a long way to go before reaching the goal, short expiration dates cause to-date framing to be more effective than to-go framing. However, a long expiration date produced no difference between to-go and to-date framings, under both high and low progress.

Limitations and directions for future research

Our research has some limitations that warrant future research. First, we conducted both studies in a controlled lab setting to maximize control over extraneous factors. Replications in a field setting would increase the robustness of our observations (Rossi et al., 2015). Second, although college student samples are a suitable population for using shopping mobile apps and testing theory (Baek and Yoo, 2018), they might limit the generalizability of our results. Previous findings suggest that the relationship between chronological age and time constraints becomes more salient as individuals age (Strough et al., 2016). An investigation using a non-student adult sample might provide valuable insights into the interplay between goal progress framing and time urgency. Future research is also needed to examine the current research across more representative samples of different mobile app categories (Kim and Baek, 2018; Kim et al., 2016). Finally, as previous findings indicate that attitude and behavioral intention both predict future behavior (Ajzen, 1991), we used three different dependent measures across the two studies: purchase intention (Study 1), attitude toward the mobile reward app (Study 2), and attitude toward the brand (Study 2). These constructs are widely used as dependent variables in consumer-retailing research (e.g., Baek et al., 2015; Bellman et al., 2011; Han et al., 2019; Yim et al., 2018; Yoon et al., 2016), yet people often express attitudes that are inconsistent with their behaviors (Hidalgo-Baz et al., 2017). Although we observed convergence in the three measures, scholars should consider simultaneously examining all three variables in a single study.

1- Introduction

2- Related work

3- Methodology and experiments

4- Experimental results and discussions

5- Conclusion

References

Abstract

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.

Introduction

Android operating system, which is provided by Google, is predicted to continue have a dramatic increase in the market with around 1.5 billion Android-based devices to be shipped by 2021 sta. It is currently leading the mobile OS market with over 80% market share compared to iOS, Windows, Blackberry, and Symbian OS. The availability of diverse Android markets such as Google Play, the official store, and third-party markets makes Android devices a popular target to not only legitimate developers, but also malware developers. Over one billion devices have been sold and more than 65 billion downloads have been made from Google Play (Smartphone, 0000). Android apps can be found in different categories, such as educational apps, gaming apps, social media apps, entertainment apps, banking apps, etc. As a technology that is open source and widely adopted, Android is facing many challenges especially with malicious applications. The malware infected apps have the ability to send text mes-sages to premium rate numbers without the user acknowledgment, gain access to private data, or even install code that can download and execute additional malware on the victim’s device. The malware can also be used to create mobile botnets (Anagnostopoulos et al., 2016). Over the last few years, the number of malware samples attacking Android has significantly increased. According to a recent report from McAfee, over 2.5 million new Android malware apps were discovered in 2017, thus increasing the number of mobile malware samples in the wild to almost 25 million in 2017 (McA, 0000).