Project: Wearable Sensor Data Classification for Human Activity Recognition (HAR)
Key Personnel: Kandethody Ramachandran, Yicheng Tu, Ming Ji

Sensor based human activity recognition has applications in areas such as healthcare monitoring, sports, physical fitness, etc. Major purpose of such efforts is to achieve noninvasive and mobile activity monitoring. Using ubiquitous, cheap and widely available technology is the key requirement for human activity recognition.  One of the projects we are looking at involves creation of a platform to combine off-the-shelf sensors of smartphones and smartwatches for recognizing human activities. We will develop novel computational algorithms so as to identify the activities in real time. In order to achieve the best tradeoff between the system’s computational complexity and recognition accuracy,  we will develop multiplel evaluations criteria to determine which classification algorithm and features to be used.

  • Some Publications
    · Shuang Na, Kandethody M Ramachandran, Ming Ji, and Yicheng Tu,  Real-time Activity Recognition using Smartphone Accelerometer, Submitted to 7th Annual International Conference on Computational Mathematics, Computational Geometry & Statistics (CMCGS 2018)
    · Shuang Na, Kandethody M. Ramachandran, and Ming Ji, Online Bayesian Kernel Segmentation and a application, Preprint, 2017.

Project: Cyber Security

Key Personnel: Kandethody Ramachandran, Zheni Stefanova

Some Publications

• Zheni Stefanova, and Kandethody Ramachandran, Network Attribute Selection, Classification and Accuracy (NASCA) Procedure for Intrusion Detection Systems, IEEE Explore, 2017 IEEE International Symposium on Technologies for Homeland Security (HST), 2017.
• Kandethody Ramachandran and Zheni Stefanova, “Dynamic Game Theories in Cyber Security” Proceedings of Dynamic Systems and Applications 7 (2016) 303–310

Key Personnel: Distinguished University Prof. Chris P. TSOKOS  & Research Team: Cybersecurity

Title: Cybersecurity: A Statistical Predictive Model for the Expected Path Length
Abstract: The object of this study is to propose a statistical model for predicting the Expected Path Length (expected number of steps the attacker will take, starting from the initial state to compromise the security goal—EPL) in a cyber-attack. The model we developed is based on utilizing vulnerability information along with having host centric attack graph. Utilizing the developed model, one can identify the interaction among the vulnerabilities and individual variables (risk factors) that drive the Expected Path Length. Gaining a better understanding of the relationship between vulnerabilities and their interactions can provide security administrators a better view and an understanding of their security status. In addition, we have also ranked the attributable variables and their contribution in estimating the subject length. Thus, one can utilize the ranking process to take precautions and actions to minimize Expected Path Length.


Title: Stochastic Modelling of Vulnerability Life Cycle and Security Risk Evaluation
Abstract: The objective of the present study is to propose a risk evaluation statistical model for a given vulnerability by examining the Vulnerability Life Cycle and the CVSS score. Having a better understanding of the behavior of vulnerability with respect to time will give us a great advantage. Such understanding will help us to avoid exploitations and introduce patches for a particular vulnerability before the attacker takes the advantage. Utilizing the proposed model one can identify the risk factor of a specific vulnerability being exploited as a function of time. Measuring of the risk factor of a given vulnerability will also help to improve the security level of software and to make appropriate decisions to patch the vulnerability before an exploitation takes place.

Title: Cyber Security: Nonlinear Stochastic Models for Predicting the Exploitability
Abstract: Obtaining complete information regarding discovered vulnerabilities looks extremely difficult. Yet, developing statistical models requires a great deal of such complete information about the vulnerabilities. In our previous studies, we introduced a new concept of “Risk Factor” of vulnerability which was calculated as a function of time. We introduced the use of Markovian approach to estimate the probability of a particular vulnerability being at a particular “state” of the vulnerability life cycle. In this study, we further develop our models, use available data sources in a probabilistic foundation to enhance the reliability and also introduce some useful new modeling strategies for vulnerability risk estimation. Finally, we present a new set of Non-Linear Statistical Models that can be used in estimating the probability of being exploited as a function of time. Our study is based on the typical security system and vulnerability data that are available. However, our methodology and system structure can be applied to a specific security  system by any software engineer and using their own vulnerabilities to obtain their probability of being exploited as a function of time. This information is very important to a company’s security system in its strategic plan to monitor and improve its process for not being exploited.

Title: Non-Homogeneous Stochastic Model for Cyber Security Predictions
Abstract: Any computer system with known vulnerabilities can be presented using attack graphs. An attacker generally has a mission to reach a goal state that he expects to achieve. Expected Path Length (EPL) [1] in the context of an attack graph describes the length or number of steps that the attacker has to take in achieving the goal state. However, EPL varies and it is based on the “state of vulnerabilities” [2] [3] in a given computer system. Any vulnerability throughout its life cycle passes through several stages that we identify as “states of the vulnerability life cycle” [2] [3]. In our previous studies we have developed mathematical models using Markovian theory to estimate the probability of a given vulnerability being in a particular state of its life cycle. There, we have considered a typical model of a computer network system with two computers subject to three vulnerabilities, and developed a method driven by an algorithm to estimate the EPL of this network system as a function of time. This approach is important because it allows us to monitor a computer system during the process of being exploited. Proposed non-homogeneous model in this study estimates the behavior of theEPL as a function of time and therefore act as an index of the risk associated with the network system getting exploited.

Title: Cybersecurity: A Stochastic Predictive Model to Determine Overall Network Security Risk Using Markovian Process
Abstract: There are several security metrics developed to protect the computer networks. In general, common security metrics focus on qualitative and subjective aspects of networks lacking formal statistical models. In the present study, we propose a stochastic model to quantify the risk associated with the overall network using Markovian process in conjunction with Common Vulnerability Scoring System (CVSS) framework. The model we developed uses host access graph to represent the network environment. Utilizing the developed model, one can filter the large amount of information available by making a priority list of vulnerable nodes existing in the network. Once a priority list is prepared, network administrators can make software patch decisions. Gaining in depth understanding of the risk and priority level of each host helps individuals to implement decisions like deployment of security products and to design network topologies.

Electricity Price Forecasting Model for Real time Energy Markets

Key Personnel: Tapas Das, Silva Sotillo, W

Some Publications:

Feijoo, F., Silva Sotillo, W., and Das, T. K. 2016. A Computationally Efficient Electricity Price Forecasting Model for Real time Energy Markets. Energy Conversion and Management 113 (2016) 27–35. DOI:10.1016/j.enconman.2016.01.043

Possible project areas

  • Energy forecasting
  • model for tourism what (which variable) contributes most

Research on the impacts of tourism in the state of Florida

– with 87.3 million visitors in 2011, Florida is the top travel destination in the world. The tourism industry has an economic impact of $67 billion on Florida’s economy.

  • Health related

Clinical, community-based or practice-based research in areas of tobacco and alcohol prevention, cancer, ageing, obesity, mental health, air and water quality etc.

  • Other
  • Homeless what causes homeless develop a model
  • Environmental based
  • Real time data and dynamical systems (relevance)
  • Individualized medicine
  • Madicaid loses $65 billion fraud, Create a fraud detection syste
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