@conference {538, title = {Integrating DOTS With Blockchain Can Secure Massive IoT Sensors}, booktitle = {2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)}, year = {2020}, publisher = {IEEE}, organization = {IEEE}, address = {New Orleans, LA, USA}, abstract = {

This paper presents a novel approach to securing IoT devices by leveraging DDoS Open Threat Signaling (DOTS) architecture on a Blockchain framework. Like many areas of the information technology domain, IoT sensors are also prone to attacks but on a larger scale. There are millions of devices being connected to a central domain to provide different types of services. Since these low-powered IoT devices have constrained technical requirements with less computational capabilities, they lack the capacity to judge their behavior as benign or malignant. IoT relies heavily on the higher level of intelligent nodes to decide on their status. An IoT Controller/Edge server handles the registration and the limited management of devices. Since traditional security is unable to protect the IoT environment sufficiently, we present a Blockchain-based DDoS detection approach to secure and mitigate such attacks in the IoT environment. Our test setup includes dataset from four sensors over two months. These values were tested using a threshold calculation against the variation of temperature, humidity, pressure, and wind direction on that day to find out whether an IoT sensor is under a DDoS attack. Our results show how DOTS can help in detection of attack when mapped on IoT edge computing.

}, keywords = {blockchain, Cybersecurity, DDOS Protection, distributed ledger, Edge Computing, IoT, Smart Contracts}, isbn = {978-1-7281-7445-7}, doi = {10.1109/IPDPSW50202.2020.00156}, url = {https://ieeexplore.ieee.org/abstract/document/9150345}, author = {Badruddoja, Syed and Dantu, Ram and Widick, Logan and Zaccagni, Zachary and Upadhyay, Kritagya} } @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} } @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} } @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} } @conference {36, title = {Indoor localization through dynamic time warping}, booktitle = {2011 IEEE International Conference on Systems, Man, and Cybernetics}, year = {2011}, month = {10/2011}, address = {Anchorage, AK}, abstract = {

Identifying and locating oneself in different hallways of high rise buildings forms the classic indoor localization problem. GPS does not work indoors and WiFi may not be omnipresent. This paper presents a novel approach to ambient magnetic fields based indoor localization. We present a system that classifies magnetic signatures using dynamic time warping. Specifically, by aligning similar magnetic signatures that differ in magnitude or time, we classify the signatures and infer the location irrepective of the person and his/her mode of commuting. A Nexus One smartphone was employed, utilizing its builtin magnetic field sensor to create a user friendly localization application solely on the phone. By using a variety of subjects including sighted, blindfolded and people using wheelchairs to handle the human speed variation problem, we evaluated the system across 26 and 15 hallways of two different buildings and obtained accuracies of 92.6\%, and 91.1\% respectively. With these encouraging results, we believe our proposed solution is user independent and caters to a wide range of hallways.

}, keywords = {Accuracy, Buildings, Databases, Human, Humans, Legged locomotion, Localization, Magnetometers, mobile phones, sensors, Wheelchairs}, doi = {10.1109/ICSMC.2011.6083906}, author = {Subbu, Kalyan and Gozick, Brandon and Ram Dantu} } @conference {69, title = {iKnow Where You Are}, booktitle = {Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04}, year = {2009}, publisher = {IEEE Computer Society}, organization = {IEEE Computer Society}, address = {Washington, DC, USA}, isbn = {978-0-7695-3823-5}, doi = {10.1109/CSE.2009.55}, url = {http://dx.doi.org/10.1109/CSE.2009.55}, author = {Subbu, Kalyan and Xu, Ning and Ram Dantu} } @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 {26, title = {An Impatient Evolutionary Algorithm With Probabilistic Tabu Search for Unified Solution of Some NP-Hard Problems in Graph and Set Theory via Clique Finding}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)}, volume = {38}, year = {2008}, pages = {645 - 666}, abstract = {

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.

}, keywords = {Algorithm design and analysis, Clique finding, combinatorial complexity, Complexity theory, computational complexity, evolutionary algorithm, evolutionary algorithms (EAs), evolutionary computation, genetic algorithm, Genetic algorithms, graph theory, Heuristic algorithms, maximum clique finding, metaheuristic global optimization method, NP-hard problem, NP-hard problems in set and graph theory, optimisation, Pareto analysis, Performance analysis, probabilistic tabu search, probability, search problems, set theory, simulated annealing}, doi = {10.1109/TSMCB.2008.915645}, author = {Guturu, Parthasarathy and Ram Dantu} } @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} }