Zubin Abraham

Securing Sensor Nodes Against Side Channel Attacks

Side channel attacks are non-invasive attacks in which adversaries gain confidential information by passively observing the target computing device. Sensor nodes are particularly vulnerable to side channel attacks due to the lack of protective physical shielding and their deployment in open environments. As sensor nodes are increasingly being deployed in safety critical applications such as power grid, volcano monitoring, and even military applications, protecting sensor nodes from side channel attacks is critical. However, side channel attacks on sensor nodes have not been investigated in previous work. In this paper, we present a taxonomy of side channel attacks on sensor nodes. For each type of the attacks, we provide guidelines and approaches to thwart the attack. We also propose a new technique, called process obfuscation, which can be used as a countermeasure for a variety of side channel attacks on sensor nodes. Furthermore, to demonstrate the feasibility of side channel attacks, we conducted electromagnetic leakage attacks, a type of side channel attack, on popular Tmote-sky sensor nodes using commercially available equipment.

Position preserving multi-output prediction

There is a growing demand for multiple output prediction methods capable of both minimizing residual errors and capturing the joint distribution of the response variables in a realistic and consistent fashion. Unfortunately, current methods are designed to optimize one of the two criteria, but not both. This paper presents a framework for multiple output regression that preserves the relationships among the response variables including possible non-linear associations while minimizing the residual errors of prediction by coupling regression methods with geometric quantile mapping. We demonstrate the effectiveness of the framework in modeling daily temperature and precipitation for climate stations in the Great Lakes region. We showed that, in all climate stations evaluated, the proposed framework achieves low residual errors comparable to standard regression methods while preserving the joint distribution of the response variables.

A Semi-supervised Framework for Simultaneous Classification and Regression of Zero-Inflated Time Series Data with Application to Precipitation Prediction

Time series data with abundant number of zeros are common in many applications, including climate and ecological modeling, disease monitoring, manufacturing defect detection, and traffic accident monitoring. Classical regression models are inappropriate to handle data with such skewed distribution because they tend to underestimate the frequency of zeros and the magnitude of non-zero values in the data. This paper presents a hybrid framework that simultaneously perform classification and regression to accurately predict future values of a zero-inflated time series. A classifier is initially used to determine whether the value at a given time step is zero while a regression model is invoked to estimate its magnitude only if the predicted value has been classified as nonzero. The proposed framework is extended to a semi-supervised learning setting via graph regularization. The effectiveness of the framework is demonstrated via its application to the precipitation prediction problem for climate impact assessment studies.

Contour regression: A distribution-regularized regression framework for climate modeling

Regression methods are commonly used to learn the mapping from a set of predictor variables to a continuous-valued target variable such that their prediction errors are minimized. However, minimizing the errors alone may not be sufficient for some applications, such as climate modeling, which require the overall predicted distribution to resemble the actual observed distribution. On the other hand, histogram equalization methods, such as quantile mapping, are often used in climate modeling to alter the distribution of input data to fit the distribution of observed data, but they provide no guarantee of accurate predictions. This paper presents a flexible regression framework known as contour regression that simultaneously minimizes the prediction error and removes biases in the predicted distribution. The framework is applicable to linear, nonlinear, and conditional quantile models and can utilize data from heterogenous sources. We demonstrate the effectiveness of the framework in fitting the daily minimum and maximum temperatures as well as precipitation for 14 climate stations in Michigan. The framework showed marked improvement over standard regression methods in terms of minimizing their distribution bias.

Extreme value prediction for zero-inflated data

Depending on the domain, there may be significant ramifications associated with the occurrence of an extreme event (for e.g., the occurrence of a flood from a climatological perspective). However, due to the relative low occurrence rate of extreme events, the accurate prediction of extreme values is a challenging endeavor. When it comes to zero-inflated time series, standard regression methods such as multiple linear regression and generalized linear models, which emphasize estimating the conditional expected value, are not best suited for inferring extreme values. And so is the case when the the conditional distribution of the data does not conform to the parametric distribution assumed by the regression model. This paper presents a coupled classification and regression framework that focuses on reliable prediction of extreme value events in a zero-inflated time series. The framework was evaluated by applying it on a real-world problem of statistical downscaling of precipitation for the purpose of climate impact assessment studies. The results suggest that the proposed framework is capable of detecting the timing and magnitude of extreme precipitation events effectively compared with several baseline methods.