@conference {518, title = {Paradigm Shift from Paper Contracts to Smart Contracts}, booktitle = {2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Atlanta, GA, USA}, abstract = {

The ambiguity and complexity of the traditional legal contracts have motivated the study and exploration of a better and advanced contract known as blockchain-based smart contracts. A smart contract is a self-executable contract where the terms of the agreement between the involved parties are directly written into the lines of code that resides in the distributed ledger technology known as the blockchain. Obtaining a better understanding of smart contracts to overcome the fundamental issues of traditional legal contracts is vital for the successful and faster dispute settlement process without the intervention of any third-party mediators like courts, banks, lawyers, etc. In this paper, we present a comprehensive overview of the key features of the paradigm shift from traditional paper contracts to smart contracts. In addition, we also discuss why smart contracts are necessary to be legally enforced and the crucial conditions that are required for them to be legally enforceable. Furthermore, we outline recent trends and emerging technologies such as Natural Language Processing, Machine Learning, and the Internet of Things that have been combined together with smart contracts.

}, keywords = {ambiguity, artificial intelligence, blockchain, electronic contract, enforceability, Ethereum, interpretations, smart contract, traditional legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00029}, url = {https://ieeexplore.ieee.org/abstract/document/9750226}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Salau, Abiola and Badruddoja, Syed} } @conference {382, title = {Performance Analysis of Elliptic Curves for Real-Time Video Encryption}, booktitle = {2018 National Cyber Summit (NCS)}, year = {2018}, month = {June}, abstract = {

The use of real-time video streaming is increasing day-by-day, and its security has become a serious issue now. Video encryption is a challenging task because of its large frame size. Video encryption can be done with symmetric key as well as asymmetric key encryption. Among different asymmetric key encryption technique, ECC performs better than other algorithms like RSA in terms of smaller key size and faster encryption and decryption operation. In this work, we have analyzed the performance of 18 different ECC curves and suggested some suitable curves for real-time video encryption.

}, keywords = {Delays, ECC, Elliptic curve cryptography, encryption, Jitter, real-time systems, Real-time video streaming, Security, Streaming media}, doi = {10.1109/NCS.2018.00015}, author = {Sen, Nilanjan and Ram Dantu and Vempati, Jagannadh and Thompson, Mark} } @conference {270, title = {Prediction of human error using eye movements patterns for unintentional insider threat detection}, booktitle = {2018 IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)}, year = {2018}, month = {Jan}, abstract = {

Threats from the inside of an organization{\textquoteright}s perimeters are a significant problem since it is difficult to distinguish them from benign activities. Recent reports indicate that the accidental/unintentional incidents account for the majority ofall insider security incidents. Human error is a major factor in unintentional insider threat. In this paper, we propose a novel approach for unintentional insider threat (UIT) detection and mitigation based on eye movement patterns. We perform experiments to capture unique characteristics of a user{\textquoteright}s eye movements as they perform several computer-based activities in different scenarios. The goal is to evaluate the effectiveness of using eye movement patterns in determining a user{\textquoteright}s subjective mental workload which is one of the main contributing factors to human error. We extract eye movement and pupil features which allow us to reliably achieve this goal. We evaluate our proposed approach using several classifiers and examine how different subsets of features affect the performance. The results show about 82\% accuracy on average for users wearing eye glasses and an average accuracy of 84.5\% for users without eye glasses. Our results demonstrate that users{\textquoteright} eye movement patterns and pupil behaviors can reveal valuable clues about their subjective mental workload and could be used in developing effective tools for unintentional insider threat detection and mitigation in real-world environments.

}, keywords = {accidental/unintentional incidents, Companies, eye, eye glasses, eye movement patterns, eye movements patterns, Feature extraction, Gaze tracking, Glass, human error prediction, insider security incidents, pupil feature extraction, Security, security of data, subjective mental workload, Task analysis, UIT detection, unintentional insider threat detection, Visualization}, doi = {10.1109/ISBA.2018.8311479}, author = {Takabi, Hassan and Hashem, Yassir and Ram Dantu} } @conference {233, title = {Pass-pic: A mobile user authentication}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

Conventional authentication methods utilizing alphanumeric username and passwords, PIN numbers, or any combination thereof have many weaknesses. On modern smart phones there are multiple ways users can authenticate. The traditional username password combination is used often as well as PIN passwords and pattern based passwords. The problem with these methods is that they are still vulnerable. A short password or to a much greater extent a PIN, or a pattern password can be defeated by various techniques such as smudge attacks, key loggers and so on. Our aim with Pass-Pic is to implement a picture based authentication system that is both more secure and easier for the user to both input and remember.

}, keywords = {alphanumeric username, Authentication, key loggers, Keyboards, message authentication, mobile authentication, Mobile communication, mobile user authentication, pass-pic, passwords, pattern based passwords, picture based authentication system, PIN numbers, Random access memory, Security, smart phones, smudge attacks, Vibrations}, doi = {10.1109/ISI.2015.7165977}, author = {Bajwa, Garima and Ram Dantu and Aldridge, Ryan} } @conference {271, title = {PhD Forum: A System Identification Approach to Monitoring Network Traffic Security}, booktitle = {2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing}, year = {2015}, month = {Nov}, abstract = {

Network security is a growing area of interest for cyber systems, especially given the increasing number of attacks on companies each year. Though there are a vast amount of tools already available, System Identification (SI) complements intrusion detection systems to help manage network traffic stability. SI is the science of building mathematical models of dynamic systems. This paper introduces the use of SI for modeling network traffic and utilizes a linear time invariant model to analyze performance of real connections and attack instances. We generated several ARX models where each represented a different threat state in the network. We utilized the KDD CUP 1999{\textquoteright}s DARPA dataset to analyze the performance when dealing with different attacks. Results show that the average model fit was 84.14\% when determining if the system was experiencing normal traffic. This value is promising because it shows how well the system is able to determine a network state in a given time when fed input.

}, keywords = {ARX, ARX model, autoregressive processes, Computational modeling, computer network security, Computer security, cyber system, dynamic system, Electronic mail, intrusion detection system, linear time invariant model, Mathematical model, Models, network traffic security, network traffic stability, Predictive models, Security, Silicon, System Identification, system identification approach, telecommunication traffic}, doi = {10.1109/CSCloud.2015.85}, author = {Mayo, Quentin and Bryce, Renee and Ram Dantu} } @conference {27, title = {Predicting social ties in mobile phone networks}, booktitle = {2010 IEEE International Conference on Intelligence and Security Informatics (ISI)}, year = {2010}, month = {05/2010}, address = {Vancouver, BC, Canada}, abstract = {

A social network dynamically changes since the social relationships (social ties) change over time. The evolution of a social network mainly depends on the evolution of the social relationships. The social-tie strengths of person-to-person are different one another even though they are in the same group. In this paper we investigate the evolution of person-to-person social relationships, quantify and predict social tie strengths based on call-detail records of mobile phones. We propose an affinity model for quantifying social-tie strengths in which a reciprocity index is integrated to measure the level of reciprocity between users and their communication partners. Since human social relationships change over time, we map the call-log data to time series of the social-tie strengths by the affinity model. Then we use ARIMA model to predict social-tie strengths. For validation of our results, we used actual call logs of 81 users collected for a period of 8 months at MIT by the Reality Mining Project group and also used call logs of 20 users collected for a period of 6 months by UNT{\textquoteright}s Network Security team. These users have around 5000 communication partners. The experimental results show that our model is effective. We achieve prediction performance with accuracy of average 95.2\% for socially close and near members. Among other applications, this work is useful for homeland security, detection of unwanted calls (e.g., spam), and marketing.

}, keywords = {ARIMA, Prediction, reciprocity index, social groups, Social networks, social relationships, Social-tie strength}, isbn = {978-1-4244-6444-9}, doi = {10.1109/ISI.2010.5484780}, author = {Zhang, Huiqi and Ram Dantu} } @conference {78, title = {Penetration Testing for Spam Filters}, booktitle = {Proceedings of the 2009 33rd Annual IEEE International Computer Software and Applications Conference - Volume 02}, year = {2009}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Seattle, WA}, abstract = {

Despite all the advances on techniques to block spam e-mail messages we still receive them on a frequent basis. This is due mainly to the ability of the spammers to modify the message and pass the filters. Therefore a testing technique that could resemble the behavior of spammers would improve the number of scenarios tested and allow filters to be developed based on the potential changes made by the spammers. An approach based on Natural Language Processing (NLP) for the penetration of spam filters is proposed here. Preliminary results using SpamAssassin are provided indicating the feasibility of the proposed approach.

}, keywords = {mutation testing, penetration testing, spam filter}, isbn = {978-0-7695-3726-9}, doi = {10.1109/COMPSAC.2009.168}, url = {http://dx.doi.org/10.1109/COMPSAC.2009.168}, author = {Madhavan, Yugesh and Cangussu, Jo{\~a}o and Ram Dantu} } @conference {30, title = {Privacy Management for Facebook}, booktitle = {4th International Conference on Information Systems Security (ICISS 2008)}, year = {2008}, month = {12/2008}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Hyderabad, India}, abstract = {

As more people adopt the Internet as a medium of communication, the Internet has developed into a virtual world and this has resulted in many online social networks (SN). MySpace and Facebook, two leading online SN sites, have a combined user base of 170 million as of 2008. SN sites started to offer developers open platforms that provide users{\textquoteright} profile information to the developers. Unfortunately, the applications can also be used to invade privacy and to harvest the users{\textquoteright} profile information without their acknowledgement. To address this vulnerability, we propose a privacy-management system that protects the accessibility of users{\textquoteright} profile. The system uses probabilistic approach based on information revelation of users. Our experimental result shows that the system can achieve high accuracy rate of 75\%.

}, keywords = {Facebook, Privacy, Privacy management, Social network}, doi = {10.1007/978-3-540-89862-7\_23}, author = {Baatarjav, Enkh-Amgalan and Ram Dantu and Phithakkitnukoon, Santi} } @conference {45, title = {Predicting Calls: New Service for an Intelligent Phone}, booktitle = {10th IFIP/IEEE International Conference on Management of Multimedia and Mobile Networks and Services, MMNS 2007}, year = {2007}, month = {11/2007}, address = { San Jos{\'e}, USA}, abstract = {

Predicting future calls can be the next advanced feature of the intelligent phone as the phone service providers are looking to offer new services to their customers. Call prediction can be useful to many applications such as planning daily schedule and attending unwanted communications (e.g. voice spam). Predicting calls is a very challenging task. We believe that this is a new area of research. In this paper, we propose a Call Predictor (CP) that computes the probability of receiving calls and makes call prediction based on caller{\textquoteright}s behavior and reciprocity. The proposed call predictor is tested with the actual call logs. The experimental results show that the call predictor performs reasonably well with false positive rate of 2.4416\%, false negative rate of 2.9191\%, and error rate of 5.3606\%.

}, keywords = {Arrival time, behavior, call matrix, Callee, Caller, Communications, Incoming calls, Inter-arrival time, Inter-arrival/departure time, Kernel density estimation, Outgoing calls, Probability density function (pdf), Receiving call probability, Reciprocity}, doi = {10.1007/978-3-540-75869-3\_3}, author = {Phithakkitnukoon, Santi and Ram Dantu} } @article {184, title = {Panel: What do you mean security VoIP}, year = {2005}, month = {06/2005}, publisher = {IEEE ENTNET conference in conjunction with Supercom{\textquoteright}2005}, author = {Ram Dantu} } @article {275, title = {Parallel algorithms for low-level vision on the homogeneous multiprocessor}, journal = {Computers \& Electrical Engineering}, volume = {20}, year = {1994}, pages = {51 - 60}, abstract = {

In this work, we present the parallel implementation of three low-level vision algorithms, namely smoothing, histogram generation and edge detection by using the Sobel operator on the Homogeneous Multiprocessor. These algorithms were run on the simulator specifically developed for the Homogeneous Multiprocessor and the simulation experiments were used to establish the performance of these algorithms on the proposed architecture.

}, keywords = {Computer architecture, image processing, MIMO architecture, Multiprocessing, pattern recognition, vision}, issn = {0045-7906}, doi = {https://doi.org/10.1016/0045-7906(94)90006-X}, url = {http://www.sciencedirect.com/science/article/pii/004579069490006X}, author = {Ram Dantu and Dimopoulos, Nikitas and Li, Kinfu and Patel, Rajni and Al-Khalili, Asim} }