Past, present and future of mobile payments research: A literature review

The mobile payment services markets are currently under transition with a history of numerous tried and failed solutions, and a future of promising but yet uncertain possibilities with potential new technology innovations. At this point of the development, we take a look at the current state of the mobile payment services market from a literature review perspective. We review prior literature on mobile payments, analyze the various factors that impact mobile payment services markets, and suggest directions for future research in this still emerging field. To facilitate the analysis of literature, we propose a framework of four contingency and five competitive force factors, and organize the mobile payment research under the proposed framework. Consumer perspective of mobile payments as well as technical security and trust are best covered by contemporary research. The impacts of social and cultural factors on mobile payments, as well as comparisons between mobile and traditional payment services are entirely uninvestigated issues. Most of the factors outlined by the framework have been addressed by exploratory and early phase studies. </p

Time- and Amplitude-Controlled Power Noise Generator against SPA Attacks for FPGA-Based IoT Devices

Power noise generation for masking power traces is a powerful countermeasure against Simple Power Analysis (SPA), and it has also been used against Differential Power Analysis (DPA) or Correlation Power Analysis (CPA) in the case of cryptographic circuits. This technique makes use of power consumption generators as basic modules, which are usually based on ring oscillators when implemented on FPGAs. These modules can be used to generate power noise and to also extract digital signatures through the power side channel for Intellectual Property (IP) protection purposes. In this paper, a new power consumption generator, named Xored High Consuming Module (XHCM), is proposed. XHCM improves, when compared to others proposals in the literature, the amount of current consumption per LUT when implemented on FPGAs. Experimental results show that these modules can achieve current increments in the range from 2.4 mA (with only 16 LUTs on Artix-7 devices with a power consumption density of 0.75 mW/LUT when using a single HCM) to 11.1 mA (with 67 LUTs when using 8 XHCMs, with a power consumption density of 0.83 mW/LUT). Moreover, a version controlled by Pulse-Width Modulation (PWM) has been developed, named PWM-XHCM, which is, as XHCM, suitable for power watermarking. In order to build countermeasures against SPA attacks, a multi-level XHCM (ML-XHCM) is also presented, which is capable of generating different power consumption levels with minimal area overhead (27 six-input LUTS for generating 16 different amplitude levels on Artix-7 devices). Finally, a randomized version, named RML-XHCM, has also been developed using two True Random Number Generators (TRNGs) to generate current consumption peaks with random amplitudes at random times. RML-XHCM requires less than 150 LUTs on Artix-7 devices. Taking into account these characteristics, two main contributions have been carried out in this article: first, XHCM and PWM-XHCM provide an efficient power consumption generator for extracting digital signatures through the power side channel, and on the other hand, ML-XHCM and RML-XHCM are powerful tools for the protection of processing units against SPA attacks in IoT devices implemented on FPGAs.Junta de AndaluciaEuropean Commission B-TIC-588-UGR2

Categorizing Natural Language-Based Customer Satisfaction: An Implementation Method Using Support Vector Machine and Long Short-Term Memory Neural Network

Analyzing natural language-based Customer Satisfaction (CS) is a tedious process. This issue is practically true if one is to manually categorize large datasets. Fortunately, the advent of supervised machine learning techniques has paved the way toward the design of efficient categorization systems used for CS. This paper presents the feasibility of designing a text categorization model using two popular and robust algorithms – the Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) Neural Network, in order to automatically categorize complaints, suggestions, feedbacks, and commendations. The study found that, in terms of training accuracy, SVM has best rating of 98.63% while LSTM has best rating of 99.32%. Such results mean that both SVM and LSTM algorithms are at par with each other in terms of training accuracy, but SVM is significantly faster than LSTM by approximately 35.47s. The training performance results of both algorithms are attributed on the limitations of the dataset size, high-dimensionality of both English and Tagalog languages, and applicability of the feature engineering techniques used. Interestingly, based on the results of actual implementation, both algorithms are found to be 100% effective in accurately predicting the correct CS categories. Hence, the extent of preference between the two algorithms boils down on the available dataset and the skill in optimizing these algorithms through feature engineering techniques and in implementing them toward actual text categorization applications

A Trust-based Secure Service Discovery (TSSD) Model for Pervasive Computing

To cope with the challenges posed by device capacity and capability, and also the nature of ad hoc networks, a Service discovery model is needed that can resolve security and privacy issues with simple solutions. The use of complex algorithms and powerful fixed infrastructure is infeasible due to the volatile nature of pervasive environment and tiny pervasive devices. In this paper, we present a trust-based secure Service discovery model, TSSD (trust-based secure service discovery) for a truly pervasive environment. Our model is a hybrid one that allows both secure and non-secure discovery of services. This model allows Service discovery and sharing based on mutual trust. The security model handles the communication and service sharing security issues. TSSD also incorporates a trust mode for sharing Services with unknown devices

Applicability of clustering to cyber intrusion detection

Maintaining cyber security is a complex task, utilizing many levels of network information along with an array of technology. Current practices for combating cyber attacks typically use Intrusion Detection Systems (IDSs) to passively detect and block multi-stage attacks. Because of the speed and force at which a new type of cyber attack can occur, automated detection and response is becoming an apparent necessity. Anomaly-based detection systems, such as statistical-based or clustering algorithms, attempt to address this by analyzing the relative differences in network and host activity. Signature-based IDS systems are typically more accurate for known attacks, but require time and resources for an analyst to update the signature database. This work hypothesizes that the latency from zero-day attack to signature creation can be shortened via anomaly-based algorithms. In particular, the summarizing ability of clustering is leveraged and examined in its applicability of signature creation. This work first investigates a modified density-based clustering algorithm as an IDS, with its strengths and weaknesses identified. Being able to separate malicious from normal activity, the modified algorithm is then applied in a supervised way to signature creation. Lessons learned from the supervised signature creation are then leveraged for the development of unsupervised real-time signature classification. Automating signature creation and classification via clustering turns out satisfactory but with limitations. Density supports for new signatures via clustering can be diluted and lead to misclassification