195 research outputs found
User behavior modeling for the prediction, profiling and detection of malware in mobile devices
No full textRecent advances in Smartphone technology led to rapid development of mobile applications that closely integrate into our daily lives. The Android platform is the fastest growing market in Smartphone operating systems to date. It has become the target for security threats because of its wide spread presence among mobile operating systems to develop and publish applications into the Android market. It presents an opportunity for attackers to easily deliver malicious applications such as malware which steals sensitive information, gets root privilege and performs abuse functions such as send SMS, delete files etc., without the user\u27s knowledge. Also, the presence of advanced sensing functions attracts malware developers to develop targeted malware. This type of malware complicates behavioral detection since it triggers malicious behavior based on the factors such as user actions, profile and/or the presence of other applications. Many dynamic detection techniques have been proposed to detect Smartphone malware. However, a real time, scalable approach to detect the targeted malware in a dynamic analysis has not been studied extensively. The main objective of this thesis is to develop User behavior models which help in detection of malware and prediction of application usage that would prevent further loss to the user by prioritizing in real time the malware testing queue and send an alert beforehand to the user to make them aware about the malicious presence. Also, grouping similar User behavior models will provide scalability and alert multiple users who are using the same malware application in the group
SCREDENT: Scalable Real-time Anomalies Detection and Notification of Targeted Malware in Mobile Devices
No full textAbstractThe ubiquitous availability of Android devices has led to increasing malicious mobile attacks targeting the Android mobile operating system. In recent times, adversaries leverage situational awareness, user and device context to create targeted malware for mobile devices. Several mobile security tools such as Mobile Sandbox, TargetDroid, and ANANAS focus on tailoring the detection schemes for individual users and suffer from scalability by analyzing individual user's activities. To the best of our knowledge, these tools do not incorporate user group profiling in their automated user-behavior driven dynamic analysis. In addition, adaptive and location-based alerts are not provided to mobile users. We propose SCREDENT: Scalable Real-time Anomalies Detection and Notification of Targeted Malware in Mobile Devices, to provide a scalable system to classify, detect, and predict targeted malware in real-time. SCREDENT incorporates behavior-triggering probabilistic models and user grouping to minimize the number of parallel dynamic analysis instances needed. SCREDENT leverages container technology to perform dynamic analysis and allow for modularity as emulation technology improves. SCREDENT uses adaptive, location-based notification principles to create a geographical fence which warn users of malicious attacks. Finally, SCREDENT provides proactive, adaptive alerts to individual users if at least one of the group members has triggered malicious activities in an application currently used by the individual
Merchants of Virtue
No full textMerchants of Virtue explores the question of what it meant to be Hindu in precolonial South Asia. Divya Cherian presents a fine-grained study of everyday life and local politics in the kingdom of Marwar in eighteenth-century western India to uncover how merchants enforced their caste ideals of vegetarianism and bodily austerity as universal markers of Hindu identity. Using legal strategies and alliances with elites, these merchants successfully remade the category of “Hindu,” setting it in contrast to “Untouchable” in a process that reconfigured Hinduism in caste terms. In a history pertinent to understanding India today, Cherian establishes the centrality of caste to the early-modern Hindu self and to its imagination of inadmissible others.
“A refreshingly different perspective on the history of caste and untouchability in India, enlarging the field of scholarship from its focus on the colonial era by telling us how precolonial configurations of power in the locality shaped the everyday experience of caste.” — GOPAL GURU, coauthor of The Cracked Mirror and Experience, Caste, and the Everyday Social
“This provocative and empirically rich study offers a plenitude of fascinating insights into aspects of western Indian history ca. 1800, from kingship and caste hierarchy to abortion and alcohol consumption. Particularly innovative is its focus on the critical role played by merchants in articulating social identities that became widespread in modern times.” — CYNTHIA TALBOT, author of The Last Hindu Emperor
“A pathbreaking book that explodes essentialist views of the construction of Hindu and Muslim identities in precolonial India. Divya Cherian provocatively argues that the category of ‘Hindu’ was the primary locus for a system of radical othering that excluded Untouchables (and Muslims as Untouchables) through mechanisms of state, law, and everyday life.” — CHRISTIAN LEE NOVETZKE, Professor of South Asian and Religious Studies, University of Washingto
Merchants of Virtue
No full textMerchants of Virtue explores the question of what it meant to be Hindu in precolonial South Asia. Divya Cherian presents a fine-grained study of everyday life and local politics in the kingdom of Marwar in eighteenth-century western India to uncover how merchants enforced their caste ideals of vegetarianism and bodily austerity as universal markers of Hindu identity. Using legal strategies and alliances with elites, these merchants successfully remade the category of “Hindu,” setting it in contrast to “Untouchable” in a process that reconfigured Hinduism in caste terms. In a history pertinent to understanding India today, Cherian establishes the centrality of caste to the early-modern Hindu self and to its imagination of inadmissible others.
“A refreshingly different perspective on the history of caste and untouchability in India, enlarging the field of scholarship from its focus on the colonial era by telling us how precolonial configurations of power in the locality shaped the everyday experience of caste.” — GOPAL GURU, coauthor of The Cracked Mirror and Experience, Caste, and the Everyday Social
“This provocative and empirically rich study offers a plenitude of fascinating insights into aspects of western Indian history ca. 1800, from kingship and caste hierarchy to abortion and alcohol consumption. Particularly innovative is its focus on the critical role played by merchants in articulating social identities that became widespread in modern times.” — CYNTHIA TALBOT, author of The Last Hindu Emperor
“A pathbreaking book that explodes essentialist views of the construction of Hindu and Muslim identities in precolonial India. Divya Cherian provocatively argues that the category of ‘Hindu’ was the primary locus for a system of radical othering that excluded Untouchables (and Muslims as Untouchables) through mechanisms of state, law, and everyday life.” — CHRISTIAN LEE NOVETZKE, Professor of South Asian and Religious Studies, University of Washingto
Improved collision detection in StarLogo Nova
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 65).StarLogo Nova is blocks-based educational software that allows students to write and play their own 3D games online. It is the online version of StarLogo TNG. This thesis explores the problem of needing more accurate collision detection in StarLogo Nova while maintaining reasonable performance. Three new collision detection systems for StarLogo Nova are developed and evaluated. Compared to the spheres used to perform collision checks in the current system, the first new system, called the TightestFitCollider, introduces a variety of bounding spheres, bounding boxes, and bounding capsules as bounding structures that may fit the models in StarLogo Nova more closely. The second system, called the HierarchicalCollider, uses hierarchies of bounding boxes to perform even more precise collision detection than the TightestFitCollider. Finally, the third system combines the first two systems, so that the advantages of each can be used as appropriate. The three systems are evaluated for their accuracy and performance within the StarLogo Nova framework.by Divya Bajekal.M. Eng
Antiepileptic drugs for the primary and secondary prevention of seizures after subarachnoid haemorrhage
No full textBackground: subarachnoid haemorrhage may result in seizures both acutely and in the longer term. The use of antiepileptic drugs (AEDs) in the primary and secondary prevention of seizures after subarachnoid haemorrhage is uncertain, and there is currently no consensus on treatment.Objectives: to assess the effects of AEDs for the primary and secondary prevention of seizures after subarachnoid haemorrhage.Search methods: we searched the Cochrane Epilepsy Group Specialised Register, the Cochrane Central Register of Controlled Trials (CENTRAL) (2013, Issue 1) in The Cochrane Library, and MEDLINE (1946 to 12th March 2013). We checked the reference lists of articles retrieved from these searches.Selection criteria: we considered all randomised and quasi-randomised controlled trials in which patients were assigned to a treatment (one or more AEDs) or placebo.Data collection and analysis: two review authors (RM and JK) independently screened and assessed the methodological quality of the studies. If studies were included, one author extracted the data and the other checked it.Main results: no relevant studies were found.Authors' conclusions: there was no evidence to support or refute the use of antiepileptic drugs for the primary or secondary prevention of seizures related to subarachnoid haemorrhage. Well-designed randomised controlled trials are urgently needed to guide clinical practice
Magnetic resonance imaging of the cerebral metabolic rate of oxygen (CMRO₂)
No full textThesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 120-128).Oxygen consumption is an essential process of the functioning brain. The rate at which the brain consumes oxygen is known as the cerebral metabolic rate of oxygen (CMRO₂). CMRO₂ is intimately related to brain health and function, and will change in settings of disease and functional activation. Accurate CMRO₂ measurement will enable detailed investigation of neuropathology and facilitate our understanding of the brain's underlying functional architecture. Despite the importance of CMRO₂ in both clinical and basic neuroscience settings, a robust CMRO₂ mapping technique amenable to functional and clinical MRI has not been established. To address this issue, a novel method called QUantitative Imaging of eXtraction of Oxygen and TIssue Consumption, or QUIXOTIC, is introduced. The key innovation in QUIXOTIC is the use of velocity-selective spin labeling to isolate MR signal exclusively from post-capillary venular blood on a voxel-by-voxel basis. This isolated signal can be related to venular oxygen saturation, oxygen extraction fraction, and ultimately CMRO₂. This thesis first explores fundamental theory behind the QUIXOTIC technique, including design of a novel MRI pulse sequence, explanation of the principal sequence parameters, and results from initial human experiences. A human trial follows, in which QUIXOTIC is used to measure cortical gray matter CMRO₂ in ten healthy volunteers.(cont.) QUIXOTIC-measured CMRO₂ is found to be within the expected physiological range and is comparable to values reported by other techniques. QUIXOTIC is then applied to evaluate CMRO₂ response to carbon-dioxide-induced hypercapnia in awake humans. In this study, CMRO₂ is observed to decrease in response to mild hypercapnia. Finally, pilot studies that show feasibility of QUIXOTIC-based functional MRI (fMRI) and so-called "turbo" QUIXOTIC are presented and discussed.by Divya Sanam Bolar.Ph.D
European Narratives on Remote Working and Coworking During the COVID-19 Pandemic
No full textThis open access book offers a multidisciplinary and comprehensive perspective regarding the immediate and long-term effects of the Covid-19 pandemic on coworking spaces in the European Region. The current pandemic has imposed several effects on work and spaces for work. Some are immediate effects and will last for a short time (such as the closing down of the space), some will last longer (namely, the reorganisation of the space to meet the physical distancing), and some will stay for a long time (remote working and hybrid working). Although the literature on coworking spaces and the effects of the pandemic is growing fast, empirical studies are yet limited. Within this context, this book seeks a twofold aim: (i) to contribute to the fast-growing literature on coworking space and their effects at different scales; (ii) to present a multidisciplinary perspective about the effects of the yet-lasting Corona-pandemic effects on the patterns of remote working and consequently on coworking spaces, as the most diffused form of new working spaces.History, Form & Aesthetic
Clinical trend discovery and analysis of Taiwanese health insurance claims data
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 61-62).Data-driven analysis can improve our understanding of medicine, and data from electronic health records and labs has been used successfully in predictive tasks. Less advanced analysis has been done on health insurance claims data, which can be rich and more structured but large in scale. Taiwan has had nationalized health insurance for twenty years; its National Health Research Institute Database (NHIRD) contains records of insurance claims, including medications, prescriptions, and treatment costs for both inpatient and outpatient visits, spanning sixteen years and a million patients. The NHIRD enables longitudinal studies of a patient's medical progression as well as aggregation and generalization to population-level insights. We conducted preliminary exploration of data trends in aggregate, such as diagnosis code frequency and average treatment cost over time. An infrastructure to perform large-scale queries and handle results was required to effectively use the NHIRD for research applications. After indexing database tables to improve query performance, we created a pipeline in Python to connect to and query the database, analyze data for hypothesis discovery and hypothesis testing, convert Taiwanese codes to international standards, and produce plots and graphs. This pipeline was used to examine drug side effects and comorbidities observed across a population, accounting for demographic variables. We also studied patient-specific longitudinal matrices of medical events, which were highly sparse. We attempted quantitative imputation methods to densify these matrices, but because the data was binary (indicating the presence of an event at a given time), categorical, and irregular, advanced imputation offered limited benefit. Nevertheless, we discovered interesting patterns in cohorts of diabetes patients treated with various classes of drugs. This information can be exploited in computational phenotyping and other learning methods, and combined with other data sources it could increase accuracy of clinical predictive tasks.by Divya P. Pillai.M. Eng
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