@article {415, title = {Detecting driver distraction using stimuli-response EEG analysis}, journal = {CoRR}, volume = {abs/1904.09100}, year = {2019}, url = {http://arxiv.org/abs/1904.09100}, author = {Bajwa, Garima and Fazeen, Mohamed and Dantu, Ram} } @conference {100, title = {Unintentional bugs to vulnerability mapping in Android applications}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

The intention of an Android application, determined by the source code analysis is used to identify potential maliciousness in that application (app). Similarly, it is possible to analyze the unintentional behaviors of an app to identify and reduce the window of vulnerabilities. Unintentional behaviors of an app can be any developmental loopholes such as software bugs overlooked by a developer or introduced by an adversary intentionally. FindBugsTM and Android Lint are a couple of tools that can detect such bugs easily. A software bug can cause many security vulnerabilities (known or unknown) and vice-versa, thus, creating a many-to-many mapping. In our approach, we construct a matrix of mapping between the bugs and the potential vulnerabilities. A software bug detection tool is used to identify a list of bugs and create an empirical list of the vulnerabilities in an app. The many-to-many mapping matrix is obtained by two approaches - severity mapping and probability mapping. These mappings can be used as tools to measure the unknown vulnerabilities and their strength. We believe our study is the first of its kind and it can enhance the security of Android apps in their development phase itself. Also, the reverse mapping matrix (vulnerabilities to bugs) could be used to improve the accuracy of malware detection in Android apps.

}, keywords = {Android (operating system), Android applications, Android Lint, Androids, bugs, Computer bugs, Conferences, development phase, developmental loopholes, FindBugsTM, Humanoid robots, Indexes, invasive software, malware detection, many-to-many mapping matrix, mapping, matrix algebra, probability, probability mapping, program debugging, reverse mapping matrix, Security, security vulnerabilities, severity mapping, Software, software bug detection tool, software bugs, software tools, source code (software), source code analysis, unintentional behavior analysis, unintentional bugs, vulnerability, vulnerability mapping}, doi = {10.1109/ISI.2015.7165966}, author = {Bajwa, Garima and Fazeen, Mohamed and Ram Dantu and Tanpure, Sonal} } @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 {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 {235, title = {Android Sensor Data Anonymization}, booktitle = {Research in Attacks, Intrusions, and Defenses: 16th International Symposium, RAID 2013, Rodney Bay, St. Lucia, October 23-25, 2013, Proceedings}, year = {2013}, publisher = {Springer}, organization = {Springer}, author = {Claiborne, Cynthia and Fazeen, Mohamed and Ram Dantu} } @booklet {234, title = {Self-Tracking via Brain-Mobile-Cloud Interface}, year = {2013}, abstract = {

Abnormalities in the brain are one of the leading causes of disability amongst people. There is a significant delay between monitoring the onset of these disorders and their treatments. This paper presents a brain-mobile-cloud interface (BMCI) to integrate the mobile platform, cloud computing technology and existing brain monitoring systems to remotely monitor the brain signals of an individual using their electroencephalograms (EEGs) in unconventional environments. Further, we discuss the potential of our proposed framework in applications like tracking mental activities, identifying distracted driving behavior and their corresponding changes in cerebral blood flow (CBF).

}, keywords = {Cerebral Blood Flow (CBF), Electroencephalography (EEG), Mobile cloud Interface, smart phone}, url = {http://www.aaai.org/ocs/index.php/SSS/SSS13/paper/view/5803}, author = {Bajwa, Garima and Ram Dantu and Fazeen, Mohamed and Joseph, Rajiv} } @article {8, title = {Safe Driving Using Mobile Phones}, journal = {Trans. Intell. Transport. Sys.}, year = {2012}, abstract = {

As vehicle manufacturers continue to increase their emphasis on safety with advanced driver-assistance systems (ADASs), we propose a device that is not only already in abundance but portable enough as well to be one of the most effective multipurpose devices that are able to analyze and advise on safety conditions. Mobile smartphones today are equipped with numerous sensors that can help to aid in safety enhancements for drivers on the road. In this paper, we use the three-axis accelerometer of an Android-based smartphone to record and analyze various driver behaviors and external road conditions that could potentially be hazardous to the health of the driver, the neighboring public, and the automobile. Effective use of these data can educate a potentially dangerous driver on how to safely and efficiently operate a vehicle. With real-time analysis and auditory alerts of these factors, we can increase a driver{\textquoteright}s overall awareness to maximize safety.

}, keywords = {Accelerometer, mobile phone, road conditions, sensors, vehicle safety}, doi = {10.1109/TITS.2012.2187640}, url = {http://dx.doi.org/10.1109/TITS.2012.2187640}, author = {Fazeen, Mohamed and Gozick, Brandon and Ram Dantu and Bhukhiya, Moiz and Gonz{\'a}lez, Marta C.} } @article {13, title = {Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches}, journal = {Social Network Analysis and Mining}, volume = {1}, year = {2011}, pages = {241{\textendash}254}, abstract = {

In this paper, we present two methods for classification of different social network actors (individuals or organizations) such as leaders (e.g., news groups), lurkers, spammers and close associates. The first method is a two-stage process with a fuzzy-set theoretic (FST) approach to evaluation of the strengths of network links (or equivalently, actor-actor relationships) followed by a simple linear classifier to separate the actor classes. Since this method uses a lot of contextual information including actor profiles, actor-actor tweet and reply frequencies, it may be termed as a context-dependent approach. To handle the situation of limited availability of actor data for learning network link strengths, we also present a second method that performs actor classification by matching their short-term (say, roughly 25\ days) tweet patterns with the generic tweet patterns of the prototype actors of different classes. Since little contextual information is used here, this can be called a context-independent approach. Our experimentation with over 500 randomly sampled records from a twitter database consists of 441,234 actors, 2,045,804 links, 6,481,900 tweets, and 2,312,927 total reply messages indicates that, in the context-independent analysis, a multilayer perceptron outperforms on both on classification accuracy and a new F-measure for classification performance, the Bayes classifier and Random Forest classifiers. However, as expected, the context-dependent analysis using link strengths evaluated using the FST approach in conjunction with some actor information reveals strong clustering of actor data based on their types, and hence can be considered as a superior approach when data available for training the system is abundant.

}, keywords = {Context dependent and context independent data analysis, Fuzzy logic, MLP, Naive Bayesian classifier, Random Forest, Social networks, Twitter}, issn = {1869-5469}, doi = {10.1007/s13278-011-0017-9}, url = {http://dx.doi.org/10.1007/s13278-011-0017-9}, author = {Fazeen, Mohamed and Ram Dantu and Guturu, Parthasarathy} } @article {16, title = {Issues and challenges in securing VoIP}, journal = {Computers \& Security}, volume = {28}, year = {2009}, pages = {743-753}, abstract = {

Voice over the Internet protocol (VoIP) is being rapidly deployed, and the convergence of the voice and data worlds is introducing exciting opportunities. Lower cost and greater flexibility are the key factors luring enterprises to transition to VoIP. Some security problems may surface with the widespread deployment of VoIP. In this article, we discuss these security problems and propose a high-level security architecture that captures required features at each boundary-network-element in the VoIP infrastructure. We describe mechanisms to efficiently integrate information between distributed security components in the architecture.

}, keywords = {Feedback, Security solutions, Threats, VoIP attacks, VoIP security}, doi = {10.1016/j.cose.2009.05.003}, author = {Ram Dantu and Fahmy, Sonia and Schulzrinne, Henning and Cangussu, Jo{\~a}o} } @article {65, title = {A Virtual Environment for Network Testing}, journal = {J. Netw. Comput. Appl.}, volume = {32}, year = {2009}, pages = {184{\textendash}214}, abstract = {

The testing of network-based solutions demands a series of tedious tasks such as the deployment of the solution at different nodes and the configuration of different topologies. The manual execution of these tasks is very time consuming and a configurable environment to facilitate these tasks and consequently improve testing performance is desired. In this paper a virtual network environment that can be easily re-configured is presented to address this problem. The environment has been evaluated by a series of case studies: one dealing with the deployment and containment of a worm propagation attack and one dealing with detecting a denial of service attack. Three smaller case studies have also been developed. The results are a clear indication of the flexibility and usefulness of the virtual network environment.

}, keywords = {Re-configuration, Software testing, Virtual network}, issn = {1084-8045}, doi = {10.1016/j.jnca.2008.03.008}, url = {http://dx.doi.org/10.1016/j.jnca.2008.03.008}, author = {Fagen, Wade and Cangussu, Jo{\~a}o and Ram Dantu} } @conference {62, title = {Goliath: A Configurable Approach for Network Testing}, booktitle = {Testbeds and Research Infrastructure for the Development of Networks and Communities, 2007. TridentCom 2007. 3rd International Conference on}, year = {2007}, month = {May}, abstract = {

When testing a network environment and/or application many aspects need to be taken into consideration. For example, different software needs to be deployed at different nodes and different topologies may also need to be tested. The manual execution of these tasks are very time consuming and a configurable environment to facilitate these tasks and consequently improve testing performance is desired. In this paper a virtual network environment that can be easily reconfigured is presented to address this problem. Also presented is a case study with the results of deployment and containing a worm propagation attack.

}, keywords = {automated network testing, Computational modeling, computer networks, Computer science, Computer worms, Distributed computing, Goliath, Intrusion detection, network topologies, Network topology, Protocols, security of data, Software testing, System testing, telecommunication computing, telecommunication network topology, telecommunication security, virtual network environment, virtual private networks, worm propagation attack}, doi = {10.1109/TRIDENTCOM.2007.4444667}, author = {Fagen, Wade and Cangussu, Jo{\~a}o and Ram Dantu} } @article {279, title = {Constraint-Based Label Switched Path Setup using Label Distribution Protocol}, year = {2002}, url = {https://tools.ietf.org/html/rfc3212}, author = {Andersson, Loa and Callon, Ross and Ram Dantu and Doolan, Paul and Feldman, Nancy and Fredette, Andre and Gray, Eric and Heinanen, Juha and Jamoussi, Bilel and Kilty, Timothy and Malis, Andrew and Girish, Muckai and Worster, Tom and Wu, Liwen} }