Biblio

The prevalence of photo processing apps suggests the demands of picture editing. As an implementation of the convolutional neural network, style transfer has been deep investigated and there are supported materials to realize it on PC platform. However, few approaches are mentioned to deploy a style transfer model on the mobile and meet the requirements of mobile users. The traditional style transfer model takes hours to proceed, therefore, based on a Perceptual Losses algorithm [1], we created a feedforward neural network for each style and the proceeding time was reduced to a few seconds. The training data were generated from a pre-trained convolutional neural network model, VGG-19. The algorithm took thousandth time and generated similar output as the original. Furthermore, we optimized the model and deployed the model with TensorFlow Mobile library. We froze the model and adopted a bitmap to scale the inputs to 720×720 and reverted back to the original resolution. The reverting process may create some blur but it can be regarded as a feature of art. The generated images have reliable quality and the waiting time is independent of the content and pattern of input images. The main factor that influences the proceeding time is the input resolution. The average waiting time of our model on the mobile phone, HUAWEI P20 Pro, is less than 2 seconds for 720p images and around 2.8 seconds for 1080p images, which are ten times slower than that on the PC GPU, Tesla T40. The performance difference depends on the architecture of the model.

Mobile phones have become nowadays a commodity to the majority of people. Using them, people are able to access the world of Internet and connect with their friends, their colleagues at work or even unknown people with common interests. This proliferation of the mobile devices has also been seen as an opportunity for the cyber criminals to deceive smartphone users and steel their money directly or indirectly, respectively, by accessing their bank accounts through the smartphones or by blackmailing them or selling their private data such as photos, credit card data, etc. to third parties. This is usually achieved by installing malware to smartphones masking their malevolent payload as a legitimate application and advertise it to the users with the hope that mobile users will install it in their devices. Thus, any existing application can easily be modified by integrating a malware and then presented it as a legitimate one. In response to this, scientists have proposed a number of malware detection and classification methods using a variety of techniques. Even though, several of them achieve relatively high precision in malware classification, there is still space for improvement. In this paper, we propose a text mining all repeated pattern detection method which uses the decompiled files of an application in order to classify a suspicious application into one of the known malware families. Based on the experimental results using a real malware dataset, the methodology tries to correctly classify (without any misclassification) all randomly selected malware applications of 3 categories with 3 different families each.

The fifth generation of mobile technology (5G) is positioned to address the demands and business contexts of 2020 and beyond. Therefore, in 5G, there is a need to push the envelope of performance to provide, where needed, for example, much greater throughput, much lower latency, ultra-high reliability, much higher connectivity density, and higher mobility range. A crucial point in the effective provisioning of 5G Next Generation Mobile Networks (NGMN) lies in the efficient error control and in more details in the utilization of Forward Error Correction (FEC) codes on the application layer. FEC is a method for error control of data transmission adopted in several mobile multicast standards. FEC is a feedback free error recovery method where the sender introduces redundant data in advance with the source data enabling the recipient to recover from different arbitrary packet losses. Recently, the adoption of FEC error control method has been boosted by the introduction of powerful Application Layer FEC (AL-FEC) codes. Furthermore, several works have emerged aiming to address the efficient application of AL-FEC protection introducing deterministic or randomized online algorithms. In this work we propose a novel AL-FEC scheme based on online algorithms forced by the well stated AL-FEC policy online problem. We present an algorithm which exploits feedback capabilities of the mobile users regarding the outcome of a transmission, and adapts the introduced protection respectively. Moreover, we provide an extensive analysis of the proposed AL-FEC algorithm accompanied by a performance evaluation against common error protection schemes.

Access authentication is a key technology to identify the legitimacy of mobile users when accessing the space-ground integrated networks (SGIN). A hierarchical identity-based signature over lattice (L-HIBS) based mobile access authentication mechanism is proposed to settle the insufficiencies of existing access authentication methods in SGIN such as high computational complexity, large authentication delay and no-resistance to quantum attack. Firstly, the idea of hierarchical identity-based cryptography is introduced according to hierarchical distribution of nodes in SGIN, and a hierarchical access authentication architecture is built. Secondly, a new L-HIBS scheme is constructed based on the Small Integer Solution (SIS) problem to support the hierarchical identity-based cryptography. Thirdly, a mobile access authentication protocol that supports bidirectional authentication and shared session key exchange is designed with the aforementioned L-HIBS scheme. Results of theoretical analysis and simulation experiments suggest that the L-HIBS scheme possesses strong unforgeability of selecting identity and adaptive selection messages under the standard security model, and the authentication protocol has smaller computational overhead and shorter private keys and shorter signature compared to given baseline protocols.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

With the rapid proliferation of mobile users, the spectrum scarcity has become one of the issues that have to be addressed. Cognitive Radio technology addresses this problem by allowing an opportunistic use of the spectrum bands. In cognitive radio networks, unlicensed users can use licensed channels without causing harmful interference to licensed users. However, cognitive radio networks can be subject to different security threats which can cause severe performance degradation. One of the main attacks on these networks is the primary user emulation in which a malicious node emulates the characteristics of the primary user signals. In this paper, we propose a detection technique of this attack based on the RSS-based localization with the maximum likelihood estimation. The simulation results show that the proposed technique outperforms the RSS-based localization method in detecting the primary user emulation attacker.

In this paper, we discuss the digital forensic procedure and techniques for analyzing the local artifacts from four popular Instant Messaging applications in Android. As part of our findings, the user chat messages details and contacts were investigated for each application. By using two smartphones with different brands and the latest Android operating systems as experimental objects, we conducted digital investigations in a forensically sound manner. We summarize our findings regarding the different Instant Messaging chat modes and the corresponding encryption status of artifacts for each of the four applications. Our findings can be helpful to many mobile forensic investigations. Additionally, these findings may present values to Android system developers, Android mobile app developers, mobile security researchers as well as mobile users.

Location privacy preservation has become an important issue in providing location based services (LBSs). When the mobile users report their locations to the LBS server or the third-party servers, they risk the leak of their location information if such servers are compromised. To address this issue, we propose a Location Privacy Preservation Scheme (LPPS) based on distributed cache pushing which is based on Markov Chain. The LPPS deploys distributed cache proxies in the most frequently visited areas to store the most popular location-related data and pushes them to mobile users passing by. In the way that the mobile users receive the popular location-related data from the cache proxies without reporting their real locations, the users' location privacy is well preserved, which is shown to achieve k-anonymity. Extensive experiments illustrate that the proposed LPPS achieve decent service coverage ratio and cache hit ratio with low communication overhead.
 

Mobile users access location services from a location based server. While doing so, the user's privacy is at risk. The server has access to all details about the user. Example the recently visited places, the type of information he accesses. We have presented synergetic technique to safeguard location privacy of users accessing location-based services via mobile devices. Mobile devices have a capability to form ad-hoc networks to hide a user's identity and position. The user who requires the service is the query originator and who requests the service on behalf of query originator is the query sender. The query originator selects the query sender with equal probability which leads to anonymity in the network. The location revealed to the location service provider is a rectangle instead of exact co-ordinate. In this paper we have simulated the mobile network and shown the results for cloaking area sizes and performance against the variation in the density of users.

User authentication is an important security mechanism that allows mobile users to be granted access to roaming service offered by the foreign agent with assistance of the home agent in mobile networks. While security-related issues have been well studied, how to preserve user privacy in this type of protocols still remains an open problem. In this paper, we revisit the privacy-preserving two-factor authentication scheme presented by Li et al. at WCNC 2013. We show that, despite being armed with a formal security proof, this scheme actually cannot achieve the claimed feature of user anonymity and is insecure against offline password guessing attacks, and thus, it is not recommended for practical applications. Then, we figure out how to fix these identified drawbacks, and suggest an enhanced scheme with better security and reasonable efficiency. Further, we conjecture that under the non-tamper-resistant assumption of the smart cards, only symmetric-key techniques are intrinsically insufficient to attain user anonymity.

Nowadays, the design of a secure access authentication protocol in heterogeneous networks achieving seamless roaming across radio access technologies for mobile users (MUs) is a major technical challenge. This paper proposes a Distributed Anonymous Authentication (DAA) protocol to resolve the problems of heavy signaling overheads and long signaling delay when authentication is executed in a centralized manner. By applying MUs and point of attachments (PoAs) as group members, the adopted group signature algorithms provide identity verification directly without sharing secrets in advance, which significantly reduces signaling overheads. Moreover, MUs sign messages on behalf of the group, so that anonymity and unlinkability against PoAs are provided and thus privacy is preserved. Performance analysis confirm the advantages of DAA over existing solutions.

The paper presents a secure solution that provides VoIP service for mobile users, handling both pre-call and mid-call mobility. Pre-call mobility is implemented using a presence server that acts as a DNS for the moving users. Our approach also detects any change in the attachment point of the moving users and transmits it to the peer entity by in band signaling using socket communications. For true mid-call mobility we also employ buffering techniques that store packets for the duration of the signaling procedure. The solution was implemented for Android devices and it uses ASP technology for the server part.
 

Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.