On the Machine Learning Techniques for Side-channel Analysis

Side-channel attacks represent one of the most powerful category of attacks on cryptographic devices with profiled attacks in a prominent place as the most powerful among them. Indeed, for instance, template attack is a well-known real-world attack that is also the most powerful attack from the information theoretic perspective. On the other hand, machine learning techniques have proven their quality in a numerous applications where one is definitely side-channel analysis, but they come with a price. Selecting the appropriate algorithm as well as the parameters can sometimes be a difficult and time consuming task. Nevertheless, the results obtained until now justify such an effort. However, a large part of those results use simplification of the data relation from the one perspective and extremely powerful machine learning techniques from the other side. In this paper, we concentrate first on the tuning part, which we show to be of extreme importance. Furthermore, since tuning represents a task that is time demanding, we discuss how to use hyperheuristics to obtain good results in a relatively short amount of time. Next, we provide an extensive comparison between various machine learning techniques spanning from extremely simple ones ( even without any parameters to tune), up to methods where previous experience is a must if one wants to obtain competitive results. To support our claims, we give extensive experimental results and discuss the necessary conditions to conduct a proper machine learning analysis. Besides the machine learning algorithms' results, we give results obtained with the template attack. Finally, we investigate the influence of the feature (in)dependence in datasets with varying amount of noise as well as the influence of feature noise and classification noise. In order to strengthen our findings, we also discuss provable machine learning algorithms, i.e., PAC learning algorithms

Heart disease detection based on machine learning algorithms

openCardiovascular diseases are the first cause of death all over the world. By using artificial intelligence algorithms and, in particular, machine learning approaches it is possible to predict risky situations due to heart disease. Various approaches are investigated in this thesis such as Neural Network, Support Vector Machine, Decision Tree, Naive Bayes, Logistic Regression and Stochastic Gradient Descent to extract predictive models in order to test for the presence or absence of heart disease. Thanks to the public dataset from UCI, it is possible to take advantage of medical data to train the proposed models. A comparison among the different approaches based on the performance was included in this thesis. The tests of the proposed models revealed performances in terms of accuracy in the range 77\%-90.6\%. The Naive Bayes model has been the model with the highest accuracy (90.6\%), highest precision (96.4\%) and shortest time for classification (0.003 seconds).Cardiovascular diseases are the first cause of death all over the world. By using artificial intelligence algorithms and, in particular, machine learning approaches it is possible to predict risky situations due to heart disease. Various approaches are investigated in this thesis such as Neural Network, Support Vector Machine, Decision Tree, Naive Bayes, Logistic Regression and Stochastic Gradient Descent to extract predictive models in order to test for the presence or absence of heart disease. Thanks to the public dataset from UCI, it is possible to take advantage of medical data to train the proposed models. A comparison among the different approaches based on the performance was included in this thesis. The tests of the proposed models revealed performances in terms of accuracy in the range 77\%-90.6\%. The Naive Bayes model has been the model with the highest accuracy (90.6\%), highest precision (96.4\%) and shortest time for classification (0.003 seconds)

A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets

We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex. While standard stochastic gradient methods converge at sublinear rates for this problem, the proposed method incorporates a memory of previous gradient values in order to achieve a linear convergence rate. In a machine learning context, numerical experiments indicate that the new algorithm can dramatically outperform standard algorithms, both in terms of optimizing the training error and reducing the test error quickly.Comment: The notable changes over the current version: - worked example of convergence rates showing SAG can be faster than first-order methods - pointing out that the storage cost is O(n) for linear models - the more-stable line-search - comparison to additional optimal SG methods - comparison to rates of coordinate descent methods in quadratic cas

Arctic Sea Ice Thickness Estimation from CryoSat-2 Satellite Data Using Machine Learning-Based Lead Detection

Satellite altimeters have been used to monitor Arctic sea ice thickness since the early 2000s. In order to estimate sea ice thickness from satellite altimeter data, leads (i.e., cracks between ice floes) should first be identified for the calculation of sea ice freeboard. In this study, we proposed novel approaches for lead detection using two machine learning algorithms: decision trees and random forest. CryoSat-2 satellite data collected in March and April of 2011-2014 over the Arctic region were used to extract waveform parameters that show the characteristics of leads, ice floes and ocean, including stack standard deviation, stack skewness, stack kurtosis, pulse peakiness and backscatter sigma-0. The parameters were used to identify leads in the machine learning models. Results show that the proposed approaches, with overall accuracy >90%, produced much better performance than existing lead detection methods based on simple thresholding approaches. Sea ice thickness estimated based on the machine learning-detected leads was compared to the averaged Airborne Electromagnetic (AEM)-bird data collected over two days during the CryoSat Validation experiment (CryoVex) field campaign in April 2011. This comparison showed that the proposed machine learning methods had better performance (up to r = 0.83 and Root Mean Square Error (RMSE) = 0.29 m) compared to thickness estimation based on existing lead detection methods (RMSE = 0.86-0.93 m). Sea ice thickness based on the machine learning approaches showed a consistent decline from 2011-2013 and rebounded in 2014.open0