@conference {575, title = {Making Smart Contracts Predict and Scale}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.

}, doi = {10.1109/BCCA55292.2022.9922480}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {572, title = {Smarter Contracts to Predict using Deep-Learning Algorithms}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

Deep learning techniques can predict cognitive intelligence from large datasets involving complex computations with activation functions. However, the prediction output needs verification for trust and reliability. Moreover, these algorithms suffer from the model{\textquoteright}s provenance to keep track of model updates and developments. Blockchain smart contracts provide a trustable ledger with consensus-based decisions that assure integrity and verifiability. In addition, the immutability feature of blockchain also supports the provenance of data that can help deep learning algorithms. Nevertheless, smart contract languages cannot predict due to the absence of floating-point operations required by activation functions of neural networks. In this paper, we derive a novel method using the Taylor series expansion to compute the floating-point equivalent output for activation functions. We train the deep learning model off-chain using a standard Python programming language. Moreover, we store models and predict on-chain with blockchain smart contracts to produce a trusted forecast. Our experiment and analysis achieved an accuracy (99\%) similar to popular Keras Python library models for the MNIST dataset. Furthermore, any blockchain platform can reproduce the activation function using our derived method. Last but not least, other deep learning algorithms can reuse the mathematical model to predict on-chain.

}, doi = {10.1109/BCCA55292.2022.9922240}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {565, title = {Trusted AI with Blockchain to Empower Metaverse}, booktitle = {2022 Fourth International Conference on Blockchain Computing and Applications (BCCA)}, year = {2022}, abstract = {

The digital experience emerging in the virtual world is a reality with the advent of the metaverse. Augmented reality(AR), virtual reality(VR), extended reality(XR), and artificial intelligence(AI) algorithms would pave the way for an immersive experience for the users in the virtual space. However, the explosion of these technologies broaches new challenges to threaten the success of metaverse due to security risks. The blockchain technology augmented with AI promises to deliver a trusted metaverse for everyone. Nevertheless, smart contracts fail to produce a cognitive prediction, dissuading users from confiding in the metaverse. We arm smart contracts with intelligence to predict using AI algorithms. Moreover, we deploy the smart contracts on the Ethereum blockchain platform and produce a prediction accuracy of 95\% compared to Python scikit-learn-based predictions. Our results show that the prediction delay can obstruct the growth of metaverse applications to accept blockchain technologies. Furthermore, the limitation of blockchain technology can make integration unreasonable. Therefore, we discuss possible scalability solutions that can be part of our future work to help more metaverse applications adopt blockchain solutions.

}, doi = {10.1109/BCCA55292.2022.9922027}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Thompson, Mark and Salau, Abiola and Upadhyay, Kritagya} } @conference {522, title = {Can{\textquoteright}t Understand SLAs? Use the Smart Contract}, 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 = {

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payment from the customers. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of a precise rules and is self-enforceable and self-executable. Since almost all legal contracts are ambiguous by nature and are complex to read and understand, we perform a novel study on how we can replace the traditional vague legal contract with the smart contact and the effect of the ambiguity on the smart contract by performing a thorough analysis on SLAs by measuring their ambiguities in various aspects. We take several samples of real SLAs from six different popular broadband vendors. We use four random SLAs to train the machine learning model to classify and then detect ambiguous words in two unseen SLAs which were the SLAs of Ziply Fiber and Century Link. As different people form different interpretations while reading the ambiguous legal contracts, we generate various human interpretations from the machine detected ambiguous words and convert all those generated interpretations into Smart Contracts to perform testing in Ethereum-based Blockchain to identify the most ambiguous as well as accurate interpretation of the SLA. From our analysis and observation, we were able to find out the most ambiguous interpretation of SLAs and we concluded that the SLA of Ziply Fiber was more ambiguous in general compared to the SLA of Century Link. Moreover, our proposed approach to detect ambiguous terms and to translate an ambiguous legal contract to a smart legal contract using a formal language to measure the degree of ambiguity can be extrapolated and replicated to legal contracts from other types of industries as well.

}, keywords = {ambiguity, ambiguity index, blockchain, clauses, complexity, Ethereum, interpretations, machine learning, service level agreement, SLA, smart contract, smart legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00015}, url = {https://ieeexplore.ieee.org/abstract/document/9750250}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Badruddoja, Syed and Salau, Abiola} } @conference {513, title = {Make Consumers Happy by Defuzzifying the Service Level Agreements}, 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 = {

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payments from the customers. However, when customers do not get the services that they have subscribed for, it becomes a laborious job for customers to contact or visit the company and claim the correct amount of compensation or service credits. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of precise rules and is self-enforceable and self-executable. In this paper, we have introduced a novel work where we use fuzzy logic inside the Ethereum-based smart contract for two significant objectives. The first objective is to make the claim of the compensation easier and faster for customers by translating the SLA into a smart contract. The second objective is to make the smart contract even smarter and intelligent by implementing fuzzy logic so that customers who have a hard time understanding the legal jargon and ambiguities of the legal contract and SLA to find out if the compensation amount they are getting when the service is poor is good enough. Since fuzzy logic models semantics of linguistic expressions by capturing vagueness in the fuzzy sets, it becomes easier to solve the problem of contractual ambiguities and expedite the process of claiming compensation when implemented in a Blockchain-based smart contract.

}, keywords = {ambiguity, blockchain, clauses, complexity, Ethereum, Fuzzy logic, interpretations, service level agreement, SLA, smart contract, smart legal contract}, isbn = {978-1-6654-1623-8}, doi = {10.1109/TPSISA52974.2021.00011}, url = {https://ieeexplore.ieee.org/abstract/document/9750258}, author = {Upadhyay, Kritagya and Dantu, Ram and He, Yanyan and Salau, Abiola and Badruddoja, Syed} } @conference {533, title = {Making Smart Contracts Smarter}, booktitle = {2021 IEEE International Conference on Blockchain and Cryptocurrency (ICBC)}, year = {2021}, publisher = {IEEE}, organization = {IEEE}, address = {Sydney, Australia}, abstract = {

Blockchain technology develops static smart contracts for decentralized business transactions, lacks dynamic decision-making capabilities that limit the possibilities of ever-increasing demands of modern business applications. Artificial intelligence, a computational prediction platform provides intelligent predictions, actions, and recognition that lacks the ability to hold on to the integrity of the prediction result and requires the help of external authorities to secure the system. Blockchain-based AI prediction can cover the gaps of individual technologies and can mutually benefit from one another to develop a decentralized machine learning architecture that promises to yield better security, automation, and dynamism of the application. This paper proposes a Naive Bayes prediction algorithm to perform prediction with inside blockchain smart contracts that promises to open up more opportunities in the field of Blockchain-AI decentralized applications.

}, keywords = {artificial intelligence, blockchain, DApp, machine learning, Naive Bayes, smart contract}, isbn = {978-1-6654-3578-9}, doi = {10.1109/ICBC51069.2021.9461148}, url = {https://ieeexplore.ieee.org/abstract/document/9461148}, author = {Badruddoja, Syed and Dantu, Ram and He, Yanyan and Upadhayay, Kritagya and Thompson, Mark} } @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} } @article {352, title = {Emerging Technologies Workshop I: The Case for AI}, year = {2018}, author = {Hurd, Bryan and Davis, Jeff and Ram Dantu} } @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 {316, title = {Insider Threat Detection Based on Users{\textquoteright} Mouse Movements and Keystrokes Behavior}, booktitle = {Secure Knowledge Management Workshop}, year = {2017}, month = {10/2017}, abstract = {

Insider threat is considered as one of the most\  serious threats in cybersecurity and has been a prime security\  concern for government and industry. Traditional approaches\  can{\textquoteright}t provide efficient solutions, and the threat keeps raising.\  In this paper, we propose a new approach to insider threat\  detection and prediction based on the user{\textquoteright}s mouse movements\  and keystrokes behavior. We conduct human subject experiments\  with 30 participants and capture their mouse movements and\  keystroke dynamics as they perform several computer-based\  activities in both benign and malicious scenarios. We extract\  features and evaluate our approach using several classifiers and\  statistical analysis measures. The results show that participants\  performing malicious tasks showed faster speed and longer mouse\  movements, and long left click and keystroke duration than the\  benign tasks. Our results suggest that users{\textquoteright} mouse movements\  and keystrokes behavior can reveal valuable knowledge about\  their malicious behavior and can be used as indicators in the\  insider threat monitoring and detection frameworks.

}, author = {Hashem, Yassir and Takabi, Hassan and Ram Dantu} } @conference {269, title = {A Multi-Modal Neuro-Physiological Study of Malicious Insider Threats}, booktitle = {Proceedings of the 2017 International Workshop on Managing Insider Security Threats}, year = {2017}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {electroencephalogram (eeg), eye tracking, insider threat, neuroscience}, isbn = {978-1-4503-5177-5}, doi = {10.1145/3139923.3139930}, url = {http://doi.acm.org/10.1145/3139923.3139930}, author = {Hashem, Yassir and Takabi, Hassan and Ram Dantu and Nielsen, Rodney} } @article {273, title = {Inside the Mind of the Insider: Towards Insider Threat Detection Using Psychophysiological Signals}, journal = {Journal of Internet Services and Information Security}, volume = {6}, year = {2016}, month = {02}, pages = {20-36}, author = {Hashem, Yassir and Takabi, Hassan and GhasemiGol, Mohammad and Ram Dantu} } @conference {268, title = {Towards Insider Threat Detection Using Psychophysiological Signals}, booktitle = {Proceedings of the 7th ACM CCS International Workshop on Managing Insider Security Threats}, year = {2015}, publisher = {ACM}, organization = {ACM}, address = {New York, NY, USA}, keywords = {brain computer interface, electroencephalograph, insider threat detection, physiological indicators}, isbn = {978-1-4503-3824-0}, doi = {10.1145/2808783.2808792}, url = {http://doi.acm.org/10.1145/2808783.2808792}, author = {Hashem, Yassir and Takabi, Hassan and GhasemiGol, Mohammad and Ram Dantu} } @article {290, title = {Method and system for managing wireless bandwidth resources}, year = {2009}, month = {05/2009}, publisher = {Cisco Technology Inc}, chapter = {US7539499B2}, address = {US}, abstract = {

A method and system for managing wireless bandwidth resources include the capability to receive at a serving node a service request for a mobile unit and to communicate with a wireless network node to determine whether sufficient wireless bandwidth is available for the requested service. The method and system also include the capability to process the service request based on the availability of wireless bandwidth.

}, keywords = {data, mobile, node, service, unit}, url = {https://patents.google.com/patent/US7539499B2/en}, author = {Holur, Balaji and Ram Dantu and Derrick, Charles} } @conference {17, title = {Behavior analysis of spam botnets}, booktitle = {2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE {\textquoteright}08)}, year = {2008}, abstract = {

Compromised computers, known as bots, are the major source of spamming and their detection helps greatly improve control of unwanted traffic. In this work we investigate the behavior patterns of spammers based on their underlying similarities in spamming. To our knowledge, no work has been reported on identifying spam botnets based on spammerspsila temporal characteristics. Our study shows that the relationship among spammers demonstrates highly clustering structures based on features such as content length, time of arrival, frequency of email, active time, inter-arrival time, and content type. Although the dimensions of the collected feature set is low, we perform principal component analysis (PCA) on feature set to identify the features which account for the maximum variance in the spamming patterns. Further, we calculate the proximity between different spammers and classify them into various groups. Each group represents similar proximity. Spammers in the same group inherit similar patterns of spamming a domain. For classification into Botnet groups, we use clustering algorithms such as Hierarchical and K-means.We identify Botnet spammers into a particular group with a precision of 90\%.

}, keywords = {behavior pattern analysis, Botnets, Classification algorithms, clustering algorithm, Clustering algorithms, Correlation, Electronic mail, feature set, Filtering, information filtering, invasive software, maximum variance, pattern classification, pattern clustering, PCA, principal component analysis, spam filtering, Time frequency analysis, unsolicited e-mail, unwanted traffic control}, doi = {10.1109/COMSWA.2008.4554418}, author = {Husna, Husain and Phithakkitnukoon, Santi and Palla, Srikanth and Ram Dantu} } @inbook {46, title = {Behavioral Entropy of a Cellular Phone User}, booktitle = {Social Computing, Behavioral Modeling, and Prediction}, year = {2008}, pages = {160{\textendash}167}, publisher = {Springer US}, organization = {Springer US}, address = {Boston, MA}, abstract = {

The increase of advanced service offered by cellular networks draws lots of interest from researchers to study the networks and phone user behavior. With the evolution of Voice over IP, cellular phone usage is expected to increase exponentially. In this paper, we analyze the behavior of cellular phone users and identify behavior signatures based on their calling patterns. We quantify and infer the relationship of a person{\textquoteright}s randomness levels using information entropy based on the location of the user, time of the call, inter-connected time, and duration of the call. We use real-life call logs of 94 mobile phone users collected at MIT by the Reality Mining Project group for a period of nine months. We are able to capture the user{\textquoteright}s calling behavior on various parameters and interesting relationship between randomness levels in individual{\textquoteright}s life and calling pattern using correlation coeffi- cients and factor analysis. This study extends our understanding of cellular phone user behavior and characterizes cellular phone users in forms of randomness level.

}, isbn = {978-0-387-77672-9}, doi = {10.1007/978-0-387-77672-9_18}, url = {http://dx.doi.org/10.1007/978-0-387-77672-9_18}, author = {Phithakkitnukoon, Santi and Husna, Husain and Ram Dantu}, editor = {Liu, Huan and Salerno, John J. and Young, Michael J.} } @conference {38, title = {Discovery of Social Groups Using Call Detail Records}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops}, year = {2008}, month = {11/2008}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Monterrey, Mexico}, abstract = {

In this paper we propose the affinity model for classifying social groups based on mobile phone call detail records. We use affinity to measure the similarity between probability distributions. Since phone calls are stochastic process, it makes more sense to use probability affinity to classify the social groups. This work is useful for enhancing homeland security, detecting unwanted calls (e.g., spam) and product marketing. For validation of our results, we used actual call logs of 100 users collected at MIT by the Reality Mining Project group for a period of 8 months. The experimental results show that our model achieves good performance with high accuracy.

}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_72}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_72}, author = {Zhang, Huiqi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @article {31, title = {Estimation of Defects Based on Defect Decay Model: ED^\{3\}M}, journal = {IEEE Transactions on Software Engineering}, volume = {34}, year = {2008}, pages = {336 - 356}, abstract = {

An accurate prediction of the number of defects in a software product during system testing contributes not only to the management of the system testing process but also to the estimation of the product{\textquoteright}s required maintenance. Here, a new approach called ED3M is presented that computes an estimate of the total number of defects in an ongoing testing process. ED3M is based on estimation theory. Unlike many existing approaches the technique presented here does not depend on historical data from previous projects or any assumptions about the requirements and/or testers{\textquoteright} productivity. It is a completely automated approach that relies only on the data collected during an ongoing testing process. This is a key advantage of the ED3M approach, as it makes it widely applicable in different testing environments. Here, the ED3M approach has been evaluated using five data sets from large industrial projects and two data sets from the literature. In addition, a performance analysis has been conducted using simulated data sets to explore its behavior using different models for the input data. The results are very promising; they indicate the ED3M approach provides accurate estimates with as fast or better convergence time in comparison to well-known alternative techniques, while only using defect data as the input.

}, keywords = {Costs, defect decay model, defect estimation, Defect prediction, estimation theory, Inspection, Metrics/Measurement, Phase estimation, Productivity, program testing, Programming, Software maintenance, software metrics, software product, Software systems, Software testing, Statistical methods, System testing, system testing process, Testing and Debugging}, doi = {10.1109/TSE.2008.23}, author = {Haider, Syed and Cangussu, Jo{\~a}o and Cooper, Kendra and Ram Dantu} } @inbook {12, title = {Group Recommendation System for Facebook}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops, Monterrey, Mexico, November 9-14, 2008. Proceedings}, year = {2008}, pages = {211{\textendash}219}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Berlin, Heidelberg}, abstract = {

Online social networking has become a part of our everyday lives, and one of the popular online social network (SN) sites on the Internet is Facebook, where users communicate with their friends, join to groups, create groups, play games, and make friends around the world. Also, the vast number of groups are created for different causes and beliefs. However, overwhelming number of groups in one category causes difficulties for users to select a right group to join. To solve this problem, we introduce group recommendation system (GRS) using combination of hierarchical clustering technique and decision tree. We believe that Facebook SN groups can be identified based on their members{\textquoteright} profiles. Number of experiment results showed that GRS can make 73\% accurate recommendation.

}, keywords = {decision tree, recommendation system, Social network}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_41}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_41}, author = {Baatarjav, Enkh-Amgalan and Phithakkitnukoon, Santi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @conference {49, title = {Inferring Social Groups Using Call Logs}, booktitle = {On the Move to Meaningful Internet Systems: OTM 2008 Workshops}, year = {2008}, month = {11/2008}, pages = {200{\textendash}210}, publisher = {Springer Berlin Heidelberg}, organization = {Springer Berlin Heidelberg}, address = {Monterrey, Mexico}, abstract = {

Recent increase in population of mobile phone users makes it a valuable source of information for social network analysis. For a given call log, how much can we tell about the person{\textquoteright}s social group? Unnoticeably, phone user{\textquoteright}s calling personality and habit has been concealed in the call logs from which we believe that it can be extracted to infer its user{\textquoteright}s social group information. In this paper, we present an end-to-end system for inferring social networks based on {\textquotedblleft}only{\textquotedblright} call logs using kernel-based na{\"\i}ve Bayesian learning. We also introduce normalized mutual information for feature selection process. Our model is evaluated with real-life call logs where it performs at high accuracy rate of 81.82\%.

}, isbn = {978-3-540-88875-8}, doi = {10.1007/978-3-540-88875-8_40}, url = {http://dx.doi.org/10.1007/978-3-540-88875-8_40}, author = {Phithakkitnukoon, Santi and Ram Dantu}, editor = {Meersman, Robert and Tari, Zahir and Herrero, Pilar} } @conference {60, title = {Quantifying presence using calling patterns}, booktitle = {Communication Systems Software and Middleware and Workshops, 2008. COMSWARE 2008. 3rd International Conference on}, year = {2008}, month = {Jan}, abstract = {

Presence technology is going to be an integral part of the next generation of communication technology. It can eliminate telephone tag between two parties (caller and callee), which will increase productivity of the parties and reduce unnecessary bandwidth usage of unwanted calls. In this paper, we propose a Willingness Estimator that computes willingness level of a callee for receiving calls from a specified caller. By knowing the willingness value of the callee the caller can decide on proceeding with the call or not. The proposed Willingness Estimator is tested with real mobile user data, and these results are highly accurate. We measure willingness based on calling patterns (arrival time, location, day) of a caller to a callee and this could serve as one of the future presence based services. The results can be used in telecommunication networks such as PSTN, Cellular networks, and Voice over IP networks.

}, keywords = {cellular networks, Communications technology, Computer science, Computer security, Intrusion detection, Laboratories, Mobile handsets, mobile user data, Network servers, next generation communication, Next generation networking, presence technology, Productivity, PSTN, telecommunication networks, telephony, user calling pattern, voice over IP networks, willingness estimator}, doi = {10.1109/COMSWA.2008.4554403}, author = {Husna, Husain and Phithakkitnukoon, Santi and Baatarjav, Enkh-Amgalan and Ram Dantu} } @conference {32, title = {Traffic Shaping of Spam Botnets}, booktitle = {2008 5th IEEE Consumer Communications and Networking Conference}, year = {2008}, month = {01/2008}, address = {Las Vegas, NV}, abstract = {

Compromised computers, known as bots, are the major source of spamming. Detecting them can help greatly improve control of unwanted traffic. In this paper, we develop a traffic control mechanism to detect and delay the traffic of suspicious senders and bots. By delaying spammer{\textquoteright}s traffic, it has been reported that 90\% of spam emails can be eliminated. In our proposed mechanism, we group spammers based on their behavior and transmission patterns. These patterns of spammers show high correlation between group members irrespective of geographic location, network ID, content, and kind of receivers. After identification of these botnet groups we applied traffic shaping techniques a pre-filtering analysis to avoid use of automated machines(bots) to spam a particular domain. Thus the source for majority of spam is blocked before reaching email servers. We also identify how randomly the botnets behave and how easy it is to capture a botnet behavior, based on Information theory. To our knowledge, there is no work reported on detecting and mitigating botnets based on their behavior and in particular transmission patterns.

}, keywords = {automated machine, compromised computer, E-mail server, Internet, prefiltering analysis, spam botnets, spammer traffic, spamming, suspicious senders, telecommunication network management, telecommunication traffic, traffic control mechanism, traffic shaping, unsolicited e-mail}, doi = {10.1109/ccnc08.2007.181}, author = {Husna, Husain and Phithakkitnukoon, Santi and Ram Dantu} } @article {47, title = {HIPAA: Securing Medical Networks}, journal = {Network Security}, volume = {2007}, year = {2007}, pages = {13{\textendash}16}, abstract = {

The Health Information Portability and Accountability Act of 1996 (HIPAA) imposes strict regulations on healthcare institutions and commercial vendors to indemnify clinical data against unscrupulous users. Security vulnerabilities concerning hospital information systems not only negatively impact patient healthcare, but may also represent a potential federal violation. For a comprehensive understanding of the security of a radiology communication network, a detailed survey of the Picture Archiving and Communication Systems (PACS) was compiled. In this paper, we present survey results and a set of recommendations for implementing PACS security.

}, issn = {1353-4858}, doi = {10.1016/S1353-4858(07)70055-7}, url = {http://dx.doi.org/10.1016/S1353-4858(07)70055-7}, author = {Ram Dantu and Oosterwijk, Herman and Kolan, Prakash and Husna, Husain} } @article {292, title = {Method and system for providing services for wireless data calls}, year = {2007}, month = {05/2007}, publisher = {Cisco Technology Inc}, address = {US}, abstract = {

A method and system for providing services for wireless data calls includes monitoring a wireless data call for a predefined event associated with a service for the wireless data call. The service is initiated for the wireless data call in response to detecting the predefined event for the data call. The predefined event may comprise a uniform resource locator (URL) match or change, excess use of transmission or time resources, a location change of a mobile device for the wireless data call, a network event, or a combination of any or all suitable events.

}, keywords = {call, data, network, service, wireless}, issn = {US7225238B1}, url = {https://patents.google.com/patent/US7225238/en}, author = {Ram Dantu and Patel, Pulin and Patel, Pravir and Garbuz, Alexander and Miernik, Jerzy and Patel, Achal and Holur, Balaji} } @article {295, title = {Quality indicator and method for frame selection in wireless network}, year = {2006}, month = {07/2006}, publisher = {Cisco Technology Inc}, chapter = {US7079512B1}, address = {US}, abstract = {

A method and system for indicating quality of a radio frame transmitted over a wireless link includes receiving a radio frame from the wireless link. A power indicator for the radio frame is determined based on a power control trend of the wireless link. A quality indicator for the radio frame is generated based on the power indicator.

}, keywords = {frame, indicator, link, quality, radio}, url = {https://patents.google.com/patent/US7079512/en}, author = {Alam, Mahbubul and Ram Dantu and Holur, Balaji and Eyceoz, Tugay} } @article {296, title = {Wireless router and method for processing traffic in a wireless communications network}, year = {2006}, month = {06/2006}, publisher = {Cisco Technology Inc}, address = {US}, abstract = {

A wireless communications network includes a wireless-specific router topology layer that connects cellular sites to a wireline topology. The wireless-specific router topology provides a distributed architecture in which call processing including call setup, resource preservation, air bandwidth allocation, switching, soft handoff, and micromobility is performed at the cell level. The wireless routers are technology independent to support various cellular technologies. The wireless router may include a first interface operable to communicate wireless packets for a call with a remote device and a second interface operable to communicate wireline packets for the call with the wireline network. A traffic controller is coupled to the first and second interfaces and operable to convert traffic between the wireless and wireline packets and to route packets to a destination mobile or wireline device. A selection and distribution unit is operable to select and distribute traffic to support soft handoff for calls in the wireless communications network.

}, keywords = {network, router, routers, traffic, wireless}, issn = {US7068624B1}, url = {https://patents.google.com/patent/US7068624B1/en}, author = {Ram Dantu and Patel, Pulin and Choksi, Ojas and Patel, Achal and Ali, Mohammad and Miernik, Jerzy and Holur, Balajl} } @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} } @article {280, title = {Signaling System 7 (SS7) Message Transfer Part 2 (MTP2) - User Adaptation Layer}, year = {2002}, url = {https://tools.ietf.org/html/rfc3331}, author = {Morneault, Ken and Ram Dantu and Sidebottom, Greg and Bidulock, Brian and Heitz, Jacob} }