N-opcode Analysis for Android Malware Classification and Categorization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Malware detection is a growing problem particularly on the Android mobile platform due to its increasing popularity and accessibility to numerous third party app markets. This has also been made worse by the increasingly sophisticated detection avoidance techniques employed by emerging malware families. This calls for more effective techniques for detection and classification of Android malware. Hence, in this paper we present an n-opcode analysis based approach that utilizes machine learning to classify and categorize Android malware. This approach enables automated feature discovery that eliminates the need for applying expert or domain knowledge to define the needed features. Our experiments on 2520 samples that were performed using up to 10-gram opcode features showed that an f-measure of 98% is achievable using this approach

Development of a Wireless Mobile Computing Platform for Fall Risk Prediction

Falls are a major health risk with which the elderly and disabled must contend. Scientific research on smartphone-based gait detection systems using the Internet of Things (IoT) has recently become an important component in monitoring injuries due to these falls. Analysis of human gait for detecting falls is the subject of many research projects. Progress in these systems, the capabilities of smartphones, and the IoT are enabling the advancement of sophisticated mobile computing applications that detect falls after they have occurred. This detection has been the focus of most fall-related research; however, ensuring preventive measures that predict a fall is the goal of this health monitoring system. By performing a thorough investigation of existing systems and using predictive analytics, we built a novel mobile application/system that uses smartphone and smart-shoe sensors to predict and alert the user of a fall before it happens. The major focus of this dissertation has been to develop and implement this unique system to help predict the risk of falls. We used built-in sensors --accelerometer and gyroscope-- in smartphones and a sensor embedded smart-shoe. The smart-shoe contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data. The interactions between these sensors and the user resulted in distinct challenges for this research while also creating new performance goals based on the unique characteristics of this system. In addition to providing an exciting new tool for fall prediction, this work makes several contributions to current and future generation mobile computing research

Are backdoor mandates ethical? A position paper

Over the past several years, several governments have, at times, pushed for the idea that commercial software should be required to include a 'backdoor,' a deliberate vulnerability whose existence and exploitation mechanism are disclosed only to the appropriate authorities. This would enable the authorities to obtain access to the information contained in any device running this software when needed to react to criminal activity

Android malware detection: An eigenspace analysis approach

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method

Étude de l’évasion tarifaire dans un réseau de transport collectif à l’aide de données de carte à puce et de comptage à bord

RÉSUMÉ : Les systèmes de transports en commun des grandes villes mondiales ont besoin de s’améliorer en permanence. En effet, qu’il soit question de satisfaction des utilisateurs, d’amélioration de la fluidité des réseaux routiers, ou encore de la réduction des gaz à effet de serre, les enjeux du développement de ces réseaux sont immenses. Dans cette optique, les agences de transport n’ont alors pas d’autres choix que de suivre l’évolution technologique. On appelle systèmes de transports intelligents (STI), l'ensemble des technologies de communication et d'information que l'on retrouve dans les transports. Depuis quelques années, ces technologies connaissent un véritable essor et ont pour objectif de rendre plus efficaces et plus fiables les infrastructures et systèmes existants. La plupart des outils STI sont basés sur la collecte, le traitement et la diffusion de l'information. Une large quantité de données peut alors être exploitée afin de fournir des informations sur la planification, la gestion du trafic, la sûreté ou encore l'évasion tarifaire. Les systèmes de carte à puce sont un bon exemple de technologie visant à améliorer les réseaux de transport en commun. En effet, de nombreuses études ont déjà prouvé l'étendue des possibilités que peuvent offrir les cartes à puces. Cependant, il existe d’autres outils STI aussi efficaces. C'est le cas des systèmes de comptage à bord qui peuvent permettre aux agences de transport d'avoir une meilleure connaissance de la fréquentation en vue d'une meilleure gestion de leurs réseaux. C'est dans ce sens que nous développons dans ce mémoire un état de l'art sur ces deux STI afin d'y voir les réels intérêts que les agences peuvent y trouver. La revue de littérature s'oriente ensuite vers l'objectif de cette recherche en faisant un bilan des connaissances sur le phénomène d'évasion tarifaire dans les transports en commun. Ce mémoire s'inscrit dans un projet de recherche impliquant la compagnie Thalès, grand fournisseur de systèmes de perception par cartes à puces, et la Société de transport de Montréal, le tout cofinancé par le Conseil national de recherche en sciences naturelles et en génie du Canada (CRSNG). Il est basé sur l'étude de données provenant du système de perception par carte à puce et du système automatisé de comptage à bord fournies par la Société des Transports de Montréal (STM). Celle-ci équipe ses autobus de ces technologies depuis 2008 pour la carte à puce et 2013 pour les comptages à bord. L’objectif principal de ce mémoire est d'exploiter ces données afin d'analyser le phénomène l'évasion tarifaire. Plusieurs pistes exploratoires ont alors été étudiées afin de mieux appréhender ce phénomène relativement complexe, qui peut causer des pertes de plusieurs millions de dollars par an pour les agences de transport. Autrement dit, nous nous sommes intéressés aux différents paramètres exploitables grâce aux données qui nous permettraient de mettre en lumière l'évasion tarifaire. Il s’avère que l’on observe des variations du ratio du nombre de validations sur le nombre de montants en fonction des heures de la journée. On ne peut pas affirmer hors de tout doute qu’un ratio plus faible est causé par l’évasion tarifaire, mais cela reste notre hypothèse d’étude. En prenant cela en compte, nous trouvons que les heures creuses semblent alors être plus sensibles à ce phénomène. De plus, les études par arrêts et par ligne nous laissent croire que ce phénomène a réellement une composante récurrente. Globalement, nous trouvons un ratio moyen d’environ 0.97 entre le nombre de validations et le nombre de montants sur l’ensemble du réseau de bus. Cependant, nous avons relevé des écarts entre ce ratio moyen et celui de certaines lignes ou arrêts. En effet, des ratios plus faibles de 4 points de pourcentage par rapport à la moyenne sont mis évidence à plusieurs reprises sur une même ligne ou un même arrêt. Le deuxième objectif de ce mémoire consiste à étendre l'étude de ce phénomène en proposant un outil dynamique visant à améliorer les contrôles sur le réseau. Cet outil s'appuie à la fois sur le traitement des données, mais aussi sur les observations du personnel de terrain (guichetiers, conducteurs de bus) afin de devenir un véritable outil d'aide à la décision pour le personnel d’inspection. Les perspectives du projet sont ensuite exposées et résident dans la continuité et l'élargissement de l'outil dynamique à d'autres problématiques comme la maintenance ou la sécurité. Des méthodes mathématiques telles que le Bayésien pourraient aussi être implantées dans l’outil pour lui donner un aspect prévisionnel. Le transfert de ces résultats de recherche permettra éventuellement à la STM de mettre en place une solution permettant de mieux appréhender ce phénomène. Quant à Thalès, cette recherche pourra leur fournir une première marche vers la mise en place d'une solution clef en main pour la réduction de l'évasion tarifaire. Cette solution pourrait être pour eux un atout non négligeable concernant la vente de leurs technologies aux agences de transports.----------ABSTRACT : Public transport systems of major world cities have an ongoing need to improve. Indeed, whether it is a question of user satisfaction, improve the fluidity of road networks, or the reduction of greenhouse gases, the issues of development of these networks are immense. With this in mind, transportation agencies have no other choice but to follow technological developments. Called intelligent transport systems (ITS), all technologies of communication and information that can be found in transportation. In recent years, these technologies are currently booming and are intended to make existing infrastructure and systems more efficient and reliable. Most tools are based on the collection, processing and dissemination of information. A large amount of data can be exploited to provide information on planning, traffic management, safety or fare evasion. Smart card systems are a good example of technology to improve public transport services. Indeed, many studies have already demonstrated the extent of the possibilities offered smartcards. However, there are other tools that have the ability to make more efficient transport networks. This is particularly true for automatic passengers counting systems (APC) that can allow transport agencies to have a better understanding of traffic in order to better manage their networks. This is what we develop in this paper a state of the art on both ITS to see the real interests that agencies may have to use them. The literature review then turned toward the goal of this research by making a state of knowledge on the fare evasion phenomenon in transport. This thesis is part of a research project involving the company Thales, a leading provider of smart card systems for perception and the Montreal transit corporation (STM), all financed by the national research council, the Natural Sciences and Engineering Canada (NSERC). The study of this thesis is performed based on data from the collection system based on smart card and the automatic passenger counting system provided by the Montréal transit corporation (STM), which equips its buses with these technologies since 2008 for the smart card and 2013 for on-board counting. The main goal of this paper is to use these data to analyze the phenomenon of fare evasion. Several exploratory tracks were studied to better understand this fairly complex phenomenon that can cause loss of several million dollars per year for transport agencies. In other words, we looked at different settings through data that allowed us to highlight the fare evasion. It turns out that there are variations concerning the ratio between number of validations and number of passengers depending to time of day. We can not claim that a lower ratio is caused by the fare evasion, but this is our hypothesis of study. Taking this into account, we find that the peak hours then seem to be more sensitive to this phenomenon. In addition, studies by stops and lines of buses lead us to believe that this phenomenon actually has a recurring component. Indeed, lower ratios of 4 percentage points from the average are repeatedly highlighted several times on the same line or even stop. The second objective of this paper is to extend the study of this phenomenon by offering a dynamic tool to improve controls over the network. This tool is based on data processing, but also on field staff observations (tellers, bus drivers) to become a true decision support tool for the inspection staff. The project's prospects are then exposed and reside in the continuation and expansion of the dynamic tool to other issues such as maintenance or safety. Mathematical methods such as bayesian could also be implemented in the tool to give it a forward look. The transfer of research results to the STM will eventually implement a solution to better understand this phenomenon. As for Thales, this research will provide a first step towards the establishment of a turnkey solution for the reduction of fare evasion. This solution could be for them an important asset for the sale of their technologies to transportation agencies

Comparison and Characterization of Android-Based Fall Detection Systems

Falls are a foremost source of injuries and hospitalization for seniors. The adoption of automatic fall detection mechanisms can noticeably reduce the response time of the medical staff or caregivers when a fall takes place. Smartphones are being increasingly proposed as wearable, cost-effective and not-intrusive systems for fall detection. The exploitation of smartphones’ potential (and in particular, the Android Operating System) can benefit from the wide implantation, the growing computational capabilities and the diversity of communication interfaces and embedded sensors of these personal devices. After revising the state-of-the-art on this matter, this study develops an experimental testbed to assess the performance of different fall detection algorithms that ground their decisions on the analysis of the inertial data registered by the accelerometer of the smartphone. Results obtained in a real testbed with diverse individuals indicate that the accuracy of the accelerometry-based techniques to identify the falls depends strongly on the fall pattern. The performed tests also show the difficulty to set detection acceleration thresholds that allow achieving a good trade-off between false negatives (falls that remain unnoticed) and false positives (conventional movements that are erroneously classified as falls). In any case, the study of the evolution of the battery drain reveals that the extra power consumption introduced by the Android monitoring applications cannot be neglected when evaluating the autonomy and even the viability of fall detection systems.Ministerio de Economía y Competitividad TEC2009-13763-C02-0

Enhancing the Quality of Care in Long-Term Care Settings

Quality of care in long-term care is a worldwide issue given the growing numbers of dependent older people. This book presents international research, 22 varied papers, exploring quality of care from several different angles. Important themes include: (1) workforce issues, such as staff training and support; job competencies, satisfaction, and intention to stay in work; staff burnout; effects of personal- and work-related factors on quality of care; (2) intervention studies: for depressive symptoms in nursing home residents; adjustment for new residents; social and psychological support; and loneliness and isolation; (3) methodology, including: developing and testing quality indicators; measuring residents' experience of quality; and assessing partnership between staff and families; and (4) older people's experiences, such as dry eyes and using ocular lubricants; associations between length of stay and end of life care; palliative care service use and comfort at end of of life; and causes of infection-related hospitalization. The book concludes with a systematic review of the current evidence base of care home research in Brazil