@conference {88, title = {A framework for secured collaboration in mHealth}, booktitle = {Collaboration Technologies and Systems (CTS), 2015 International Conference on}, year = {2015}, month = {06/2015}, abstract = {

We have designed a novel framework of services, protocols and technologies to ensure the secure collaboration in M2M networks, specifically in mobile health. The promise of mobile health to reform preventive self-care opens new doors for remote monitoring to improve health care communication. With cardiopulmonary resuscitation (CPR) as an example, we classify our M2M elements into services, roles, human-computer protocols and technologies through which we require trust, anonymity, scalability, and active detachment. We simulate a scenario in which a patient needs CPR and through the use of widely available technologies (such as a smartphone and secure web sockets) we demonstrate a technological collaboration that facilitates secure emergency mobile health services.

}, keywords = {cardiology, cardiopulmonary resuscitation, Cardiopulmonary resuscitation(CPR), Collaboration, Computers, CPR, health care, health care communication, human computer interaction, human-computer protocol, M2M network, machine to machine(M2M), Medical services, mHealth, Mobile communication, mobile computing, mobile health, Protocols, remote monitoring, Scalability, secure collaboration, secure emergency mobile health services, secured collaboration, security of data, servers, smart phones, smartphone}, doi = {10.1109/CTS.2015.7210450}, author = {Widick, Logan and Talkington, Josh and Bajwa, Garima and Ram Dantu} } @conference {232, title = {Random anonymization of mobile sensor data: Modified Android framework}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

With the increasing ability to accurately classify activities of mobile users from what was once viewed as innocuous mobile sensor data, the risk of users compromising their privacy has risen exponentially. Currently, mobile owners cannot control how various applications handle the privacy of their sensor data, or even determine if a service provider is adversarial or trustworthy. To address these privacy concerns, third party applications have been designed to allow mobile users to have control over the data that is sent to service providers. However, these applications require users to set flags and parameters that place restrictions on the anonymized or real sensor data that is sent to the requestor. Therefore, in this paper, we introduce a new framework, RANDSOM, that moves the decision-making from the application level to the operating system level.

}, keywords = {Accelerometers, Android, anonymized, application level, Data models, data privacy, Hidden Markov models, Mobile communication, mobile computing, mobile sensor data, mobile users, modified Android framework, operating system level, pervasive, Privacy, privacy concerns, provider, random anonymization, RANDSOM, RANDSOM framework, sensor data privacy, service providers, smart phone, smart phones, telecommunication security, third party applications}, doi = {10.1109/ISI.2015.7165968}, author = {Claiborne, Cynthia and Ncube, Cathy and Ram Dantu} } @conference {67, title = {Another free app: Does it have the right intentions?}, booktitle = {Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on}, year = {2014}, month = {July}, abstract = {

Security and privacy holds a great importance in mobile devices due to the escalated use of smart phone applications (app). This has made the user even more vulnerable to malicious attacks than ever before. We aim to address this problem by proposing a novel framework to identify potential Android malware apps by extracting the intention and their permission requests. First, we constructed a dataset consisting of 1,730 benign apps along with 273 malware samples. Then, both datasets were subjected to source code extraction. From there on, we followed a two phase approach to identify potential malware samples. In phase 1, we constructed a machine learning model to group benign apps into different clusters based on their operations known as the task-intention. Once we trained the model, it was used to identify the task-intention of an Android app. Further, in this phase, we only used the benign apps to construct the task-intentions and none of the malware signatures were involved. Therefore, our approach does not use machine learning models to identify malware apps. Then, for each task-intention group, we extracted the permission-requests of the apps and constructed the probability mass functions (PMF). We named the shape of this PMF as Intention-Shape or I-Shape. In phase 2, we used the permission-requests, task-intentions and I-Shapes to identify potential malware apps. We compared the permission-requests of an unknown app with its corresponding I-Shape to identify the potential malware apps. Using this approach, we obtained an accuracy of 89\% in detecting potential malware samples. The novelty of our work is to perform potential malware identification without training any models with malware signatures, and utilization of I-Shapes to identify such potential malware samples. Our approach can be utilized to identify the safety of an app before it is installed as it performs static code analysis. Further, it can be utilized in pre-screening or multi-layer security sys- ems. It is also highly useful in screening malware apps when launching in Android markets.

}, keywords = {Android app task-intention identification, Android malware apps, Androids, benign apps, clusters, Feature extraction, Humanoid robots, I-Shape, intention-shape, invasive software, learning (artificial intelligence), machine learning model, malicious attacks, Malware, Mathematical model, mobile computing, permission-requests, PMF, probability mass functions, Shape, smart phone applications, smart phones, source code (software), source code extraction, static code analysis, Unsupervised learning}, doi = {10.1109/PST.2014.6890950}, author = {Fazeen, Mohamed and Ram Dantu} } @conference {92, title = {Tire-road friction estimation utilizing smartphones}, booktitle = {Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on}, year = {2014}, month = {08/2014}, abstract = {

Tire-road friction is an important parameter for a number of different safety features present in modern-day vehicles, and the knowledge of this friction may also prove useful to the driver of a vehicle while it is in motion. In particular, this information may help inform a driver of dangerous low-traction situations that he or she may need to be aware of. Furthermore, since a growing number of drivers have access to Bluetooth-enabled smartphones, it is worth exploring how these devices may be leveraged in concert with vehicular CAN-bus networks to provide valuable safety information.

}, keywords = {Acceleration, Bluetooth, bluetooth-enabled smart phones, CAN-bus, controller area networks, dangerous low-traction situations, driver information systems, Estimation, friction, graphical user interfaces, mobile computing, road safety, road vehicles, Roads, Safety, safety information, smart phones, tire-road friction estimation, traction, tyres, vehicle safety features, Vehicles, vehicular CAN-bus networks, vehicular safety, Wheels}, doi = {10.1109/IRI.2014.7051980}, author = {Jaynes, Michael and Ram Dantu} } @article {51, title = {A Testbed for Large Mobile Social Computing Experiments}, journal = {Int. J. Sen. Netw.}, volume = {8}, year = {2010}, pages = {89{\textendash}97}, abstract = {

We present a testbed for mobile social computing that can be used to perform research in security, privacy and context-awareness policies and mechanisms appropriate for a wide range of applications. We compare several mobile platforms and present the rational for our design choices and reasons we chose Android as the primary smartphone for the testbed. We also discuss some of the experiments that can be conducted using this testbed.

}, keywords = {context awareness, mobile communications, mobile computing, mobile devices, Privacy, Security, sensor information, sensors, smartphones, social computing, testbed, ubiquitous devices}, issn = {1748-1279}, doi = {10.1504/IJSNET.2010.034618}, url = {http://dx.doi.org/10.1504/IJSNET.2010.034618}, author = {Alazzawe, Ahmed and Alazzawe, Anis and Wijesekera, Duminda and Ram Dantu} } @conference {55, title = {A testbed for mobile social computing}, booktitle = {Testbeds and Research Infrastructures for the Development of Networks Communities and Workshops, 2009. TridentCom 2009. 5th International Conference on}, year = {2009}, month = {April}, abstract = {

We present a testbed for mobile social computing that can be used to perform research in security, privacy, and context-awareness policies and mechanisms appropriate for a wide range of applications. We compare several mobile platforms and present the rational for our design choices and reasons we chose Android as the primary smartphone for this testbed. We also discuss some of the possible experiments that can be conducted using the testbed.

}, keywords = {Acoustic sensors, Computer science, Computer security, context-awareness policy, data privacy, Feedback, Global Positioning System, mobile computing, mobile social computing, Performance evaluation, Privacy, privacy policy, security of data, security policy, Social network services, Testing}, doi = {10.1109/TRIDENTCOM.2009.4976250}, author = {Alazzawe, Ahmed and Alazzawe, Anis and Wijesekera, Duminda and Ram Dantu} }