An ISO/IEC 7816-4 Application Layer Approach to Mitigate Relay Attacks on near Field Communication

Near Field Communication (NFC) has become prevalent in access control and contactless payment systems, however, there is evidence in the literature to suggest that the technology possesses numerous vulnerabilities. Contactless bank cards are becoming commonplace in society; while there are many benefits from the use of contactless payments, there are also security issues present that could be exploited by a malicious third party. The inherently short operating distance of NFC (typically about 4 cm) is often relied upon as a means of ensuring intentional interaction on the user’s part and limiting attack vectors. However, NFC is particularly sensitive to relay attacks, which entirely negate the security usefulness of the short-range aspect of technology. The aim of this article is to demonstrate how standard hardware can be used to exploit the technology to carry out a relay attack. Considering the risk that relay attacks pose, a countermeasure is proposed to mitigate this threat. Our countermeasure yields a 100% detection rate in experiments undertaken – in which over 10,000 contactless transactions were carried out on a range of different contactless cards and devices. In these experiments, there was a false positive rate of 0.38% – 0.86%. As little as 1 in every 250 transactions were falsely classified as being the subject of a relay attack and so the user experience was not significantly impacted. With our countermeasure implemented, transaction time was lengthened by only 0.22 seconds

Experimental Studies of Android APP Development for Smart Chess Board System

Playing chess on a smart phone has gained popularity in the last few years, offering the convenience of correspondence play, automatic recording of a game, etc. Although a good number of players love playing chess on a tablet/smart phone, it doesn\u27t come close to the experience of playing over the traditional board. The feel and pleasure are more real when playing face down with the opponent sitting across each other rather than playing in mobile devices. This is especially true during chess tournaments. It would be ideal to enhance the experience of playing chess on board with the features of chess playing on smart phones. Based on the design of a roll able smart chess board, an android app has been implemented to interact with the board. It reads signals from the smart chess board and maps the movements of the chess pieces to the phone. The recorded play would be used as input for game analysis. The design and implementation of a server for playing and reviewing a game online have also been studied in this thesis

Exploring Privacy Leakage from the Resource Usage Patterns of Mobile Apps

Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile app? This thesis answers this question for two kinds of systems, a single mobile device and a mobile cloud computing system. First, two privacy attacks under the same framework are proposed based on supervised learning algorithms. Then these attacks are implemented and explored in a mobile device and in a cloud computing environment. Experimental evaluations show that the type of app can be inferred with high probability. In particular, the attacks achieve up to 100% accuracy on a mobile device, and 66.7% accuracy in the mobile cloud computing environment. This study shows that resource usage patterns of mobile apps can be used to infer the type of apps being used, and thus can cause privacy leakage if not protected

A Graph-Based Reinforcement Learning Method with Converged State Exploration and Exploitation

In any classical value-based reinforcement learning method, an agent, despite of its continuous interactions with the environment, is yet unable to quickly generate a complete and independent description of the entire environment, leaving the learning method to struggle with a difficult dilemma of choosing between the two tasks, namely exploration and exploitation. This problem becomes more pronounced when the agent has to deal with a dynamic environment, of which the configuration and/or parameters are constantly changing. In this paper, this problem is approached by first mapping a reinforcement learning scheme to a directed graph, and the set that contains all the states already explored shall continue to be exploited in the context of such a graph. We have proved that the two tasks of exploration and exploitation eventually converge in the decision-making process, and thus, there is no need to face the exploration vs. exploitation tradeoff as all the existing reinforcement learning methods do. Rather this observation indicates that a reinforcement learning scheme is essentially the same as searching for the shortest path in a dynamic environment, which is readily tackled by a modified Floyd-Warshall algorithm as proposed in the paper. The experimental results have confirmed that the proposed graph-based reinforcement learning algorithm has significantly higher performance than both standard Q-learning algorithm and improved Q-learning algorithm in solving mazes, rendering it an algorithm of choice in applications involving dynamic environments

IntelliChess

Amateurs and professionals alike are increasingly shifting to virtual chess. However, this shift to virtualization has left a void in the heart of many enthusiasts. The ‘feel’ of the game seems to be slowly fading away. With our smart chess board, IntelliChess, the user will be able to enjoy chess the way it is meant to be enjoyed with no compromise on functionality or capability. Our automated chess movement system has the incredible ability to move its own pieces which makes the user feel like he is playing against a real opponent. We also have a real time assistant that trains you to make the best move possible and identifies illegal actions. The difficulty of the computer player is also adjustable allowing new players to grow and learn at their own pace. Our companion app will display relevant information and provide a convenient portal for the user to tweak settings based on their preference. The implicit ability to recognize the chess pieces and their movement also allows us to display a duplicated virtual board for streaming worthy occasions (such as tournaments), providing an overall better experience for the fans of the game as wel

Cognitive Computing: Architecture, Technologies and Intelligent Applications

This work was supported in part by the National Natural Science Foundation of China under Grant U1705261 and in part by the Director Fund of WNLO. The work of F. Herrara was supported in part by FEDER Funds and in part by the Spanish Ministry of Science and Technology under Project TIN2017-89517-P

Effectiveness of Modern Educational Games on Society

The study is about how modern education games are effecting the society. The authors have discussed different point of views published and working in the same domain after critically evaluating previous work in the same domain. The authors have covered how games are changing the way of learning new education trends

Monte-Carlo Tree Search Algorithm in Pac-Man Identification of commonalities in 2D video games for realisation in AI (Artificial Intelligence)

The research is dedicated to the game strategy, which uses the Monte-Carlo Tree Search algorithm for the Pac-Man agent. Two main strategies were heavily researched for Pac-Man’s behaviour (Next Level priority) and HS (Highest Score priority). The Pacman game best known as STPacman is a 2D maze game that will allow users to play the game using artificial intelligence and smart features such as, Panic buttons (where players can activate on or off when they want and when they do activate it Pacman will be controlled via Artificial intelligence). A Variety of experiments were provided to compare the results to determine the efficiency of every strategy. A lot of intensive research was also put into place to find a variety of 2D games (Chess, Checkers, Go, etc.) which have similar functionalities to the game of Pac-Man. The main idea behind the research was to see how effective 2D games will be if they were to be implemented in the program (Classes/Methods) and how well would the artificial intelligence used in the development of STPacman behave/perform in a variety of different 2D games. A lot of time was also dedicated to researching an ‘AI’ engine that will be able to develop any 2D game based on the users submitted requirements with the use of a spreadsheet functionality (chapter 3, topic 3.3.1 shows an example of the spreadsheet feature) which will contain near enough everything to do with 2D games such as the parameters (The API/Classes/Methods/Text descriptions and more). The spreadsheet feature will act as a tool that will scan/examine all of the users submitted requirements and will give a rough estimation(time) on how long it will take for the chosen 2D game to be developed. It will have a lot of smart functionality and if the game is not unique like chess/checkers it will automatically recognize it and alert the user of it