@conference {587, title = {Verifying OAuth Implementations Through Encrypted Network Analysis}, booktitle = {Proceedings of the 24th ACM Symposium on Access Control Models and Technologies}, year = {2019}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

Verifying protocol implementations via application analysis can be cumbersome. Rapid development cycles of both the protocol and applications that use it can hinder up-to-date analysis. A better approach is to use formal models to characterize the applications platform and then verify the protocol through analysis of the network traffic tied to the models. To test this method, the popular protocol OAuth is considered. Currently, formal models of OAuth do not take into consideration the mobile environment, and implementation verification is largely based on code analysis. Our preliminary results are two fold; we sketch an extension to a formal model that incorporates the specifics of the Android platform and classify OAuth device types using machine learning on encrypted VPN traffic.

}, keywords = {Android, authorization, formal models, network analysis, oauth}, isbn = {9781450367530}, doi = {10.1145/3322431.3326449}, url = {https://doi.org/10.1145/3322431.3326449}, author = {Talkington, Josh and Dantu, Ram and Morozov, Kirill} } @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 {74, title = {Context-aware Multimedia Encryption in Mobile Platforms}, booktitle = {Proceedings of the 9th Annual Cyber and Information Security Research Conference}, year = {2014}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, abstract = {

Voice over IP (VoIP) is a part of multimedia content and this communication is growing rapidly in portable devices due to its attractive features. Secure communication is critical as most of such communications are routed through public channels. Establishing secure VoIP communication is computationally expensive. Modern high strength encryption algorithms such as AES, RSA, Serpent, etc. need high computational power for encryption. On the mobile platform this is a significant factor due to its constrained power resources. In this work we are presenting a novel idea of encrypting VoIP speech data while conserving resources without compromising the security of the VoIP communication. First, the algorithm detects the content of the speech data and identifies its context. Then sensitive sections of the conversation are separated. In the last step, these sections are encrypted with a high strength cryptographic algorithm while other parts are encrypted with a less strength algorithm for better performance. In this way unnecessary encryption power is spared. This will conserve computational power while ensuring the security of sensitive information. Also, we discuss some of the crucial potential attack vectors and their defense mechanisms. Finally, we implemented a prototype to test our concept and we observed an average of 39\% reduction in computational time.

}, keywords = {Android, context-aware, encryption, speech recognition, VoIP}, isbn = {978-1-4503-2812-8}, doi = {10.1145/2602087.2602115}, url = {http://doi.acm.org/10.1145/2602087.2602115}, author = {Fazeen, Mohamed and Bajwa, Garima and Ram Dantu} } @conference {35, title = {Are You a Safe Driver?}, booktitle = {2009 International Conference on Computational Science and Engineering}, year = {2009}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Washington, DC, USA}, abstract = {

As the world continues to enhance and strengthen its emergency services, mobile phones may be used to aid in safe driving practices and detection of emergencies. To our knowledge, no work has been reported in understanding vehicle motion using accelerometers/compass in cell phones. In this paper, we used the multiple sensors in a Google phone to classify safe versus unsafe driving. In particular, we used breaking distance, acceleration, and deceleration for detecting safe verses unsafe braking. Next, we calculated the displacement in the axis perpendicular to the trajectory of vehicle and used it to classify safe and unsafe lane changes. The direction of the phone with respect to the motion of the vehicle is important during calibration of the above measurements, so we used 2D and 3D rotation matrices for transforming device orientation. Future work includes calibration of braking distance, lane changes, and reliable transformation of phone orientation with respect to trajectory of the vehicle.

}, keywords = {Accelerometer, Android, driving, emergency, mobile phone, orientation, safe, unsafe}, isbn = {978-0-7695-3823-5}, doi = {10.1109/CSE.2009.331}, url = {http://dx.doi.org/10.1109/CSE.2009.331}, author = {Langle, Lonnie and Ram Dantu} }