Gideon Dror

Modeling attacks on physical unclonable functions

We show in this paper how several proposed Physical Unclonable Functions (PUFs) can be broken by numerical modeling attacks. Given a set of challenge-response pairs (CRPs) of a PUF, our attacks construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. This algorithm can subsequently impersonate the PUF, and can be cloned and distributed arbitrarily. This breaks the security of essentially all applications and protocols that are based on the respective PUF. The PUFs we attacked successfully include standard Arbited PUFs and Ring Oscillator PUFs of arbitrary sizes, and XO Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs of up to a given size and complexity. Our attacks are based upon various machine learning techniques including Logistic Regression and Evolution Strategies. Our work leads to new design requirements for secure electrical PUFs, and will be useful to PUF designers and attackers alike.

Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy

In the past decade large scale recommendation datasets were published and extensively studied. In this work we describe a detailed analysis of a sparse, large scale dataset, specifically designed to push the envelope of recommender system models. The Yahoo! Music dataset consists of more than a million users, 600 thousand musical items and more than 250 million ratings, collected over a decade. It is characterized by three unique features: First, rated items are multi-typed, including tracks, albums, artists and genres; Second, items are arranged within a four level taxonomy, proving itself effective in coping with a severe sparsity problem that originates from the unusually large number of items (compared to, e.g., movie ratings datasets). Finally, fine resolution timestamps associated with the ratings enable a comprehensive temporal and session analysis. We further present a matrix factorization model exploiting the special characteristics of this dataset. In particular, the model incorporates a rich bias model with terms that capture information from the taxonomy of items and different temporal dynamics of music ratings. To gain additional insights of its properties, we organized the KddCup-2011 competition about this dataset. As the competition drew thousands of participants, we expect the dataset to attract considerable research activity in the future.

PUF Modeling Attacks on Simulated and Silicon Data

We discuss numerical modeling attacks on several proposed strong physical unclonable functions (PUFs). Given a set of challenge-response pairs (CRPs) of a Strong PUF, the goal of our attacks is to construct a computer algorithm which behaves indistinguishably from the original PUF on almost all CRPs. If successful, this algorithm can subsequently impersonate the Strong PUF, and can be cloned and distributed arbitrarily. It breaks the security of any applications that rest on the Strong PUFs unpredictability and physical unclonability. Our method is less relevant for other PUF types such as Weak PUFs. The Strong PUFs that we could attack successfully include standard Arbiter PUFs of essentially arbitrary sizes, and XOR Arbiter PUFs, Lightweight Secure PUFs, and Feed-Forward Arbiter PUFs up to certain sizes and complexities. We also investigate the hardness of certain Ring Oscillator PUF architectures in typical Strong PUF applications. Our attacks are based upon various machine learning techniques, including a specially tailored variant of logistic regression and evolution strategies. Our results are mostly obtained on CRPs from numerical simulations that use established digital models of the respective PUFs. For a subset of the considered PUFs-namely standard Arbiter PUFs and XOR Arbiter PUFs-we also lead proofs of concept on silicon data from both FPGAs and ASICs. Over four million silicon CRPs are used in this process. The performance on silicon CRPs is very close to simulated CRPs, confirming a conjecture from earlier versions of this work. Our findings lead to new design requirements for secure electrical Strong PUFs, and will be useful to PUF designers and attackers alike.

Facial Attractiveness: Beauty and the Machine

This work presents a novel study of the notion of facial attractiveness in a machine learning context. To this end, we collected human beauty ratings for data sets of facial images and used various techniques for learning the attractiveness of a face. The trained predictor achieves a significant correlation of 0.65 with the average human ratings. The results clearly show that facial beauty is a universal concept that a machine can learn. Analysis of the accuracy of the beauty prediction machine as a function of the size of the training data indicates that a machine producing human-like attractiveness rating could be obtained given a moderately larger data set.

Upright orientation of man-made objects

Humans usually associate an upright orientation with objects, placing them in a way that they are most commonly seen in our surroundings. While it is an open challenge to recover the functionality of a shape from its geometry alone, this paper shows that it is often possible to infer its upright orientation by analyzing its geometry. Our key idea is to reduce the two-dimensional (spherical) orientation space to a small set of orientation candidates using functionality-related geometric properties of the object, and then determine the best orientation using an assessment function of several functional geometric attributes defined with respect to each candidate. Specifically we focus on obtaining the upright orientation for man-made objects that typically stand on some flat surface (ground, floor, table, etc.), which include the vast majority of objects in our everyday surroundings. For these types of models orientation candidates can be defined according to static equilibrium. For each candidate, we introduce a set of discriminative attributes linking shape to function. We learn an assessment function of these attributes from a training set using a combination of Random Forest classifier and Support Vector Machine classifier. Experiments demonstrate that our method generalizes well and achieves about 90% prediction accuracy for both a 10-fold cross-validation over the training set and a validation with an independent test set.

Large-scale learning of word relatedness with constraints

Prior work on computing semantic relatedness of words focused on representing their meaning in isolation, effectively disregarding inter-word affinities. We propose a large-scale data mining approach to learning word-word relatedness, where known pairs of related words impose constraints on the learning process. We learn for each word a low-dimensional representation, which strives to maximize the likelihood of a word given the contexts in which it appears. Our method, called CLEAR, is shown to significantly outperform previously published approaches. The proposed method is based on first principles, and is generic enough to exploit diverse types of text corpora, while having the flexibility to impose constraints on the derived word similarities. We also make publicly available a new labeled dataset for evaluating word relatedness algorithms, which we believe to be the largest such dataset to date.

Churn prediction in new users of Yahoo! answers

One of the important targets of community-based question answering (CQA) services, such as Yahoo! Answers, Quora and Baidu Zhidao, is to maintain and even increase the number of active answerers, that is the users who provide answers to open questions. The reasoning is that they are the engine behind satisfied askers, which is the overall goal behind CQA. Yet, this task is not an easy one. Indeed, our empirical observation shows that many users provide just one or two answers and then leave. In this work we try to detect answerers that are about to quit, a task known as churn prediction, but unlike prior work, we focus on new users. To address the task of churn prediction in new users, we extract a variety of features to model the behavior of \YA{} users over the first week of their activity, including personal information, rate of activity, and social interaction with other users. Several classifiers trained on the data show that there is a statistically significant signal for discriminating between users who are likely to churn and those who are not. A detailed feature analysis shows that the two most important signals are the total number of answers given by the user, closely related to the motivation of the user, and attributes related to the amount of recognition given to the user, measured in counts of best answers, thumbs up and positive responses by the asker.

Performance Prediction Challenge

A major challenge for machine learning algorithms in real world applications is to predict their performance. We have approached this question by organizing a challenge in performance prediction for WCCI 2006. The class of problems addressed are classification problems encountered in pattern recognition (classification of images, speech recognition), medical diagnosis, marketing (customer categorization), text categorization (filtering of spam). Over 100 participants have been trying to build the best possible classifier from training data and guess their generalization error on a large unlabeled test set. The challenge scores indicate that cross-validation yields good results both for model selection and performance prediction. Alternative model selection strategies were also sometimes employed with success. The challenge web site keeps open for post-challenge submissions: http://www.modelselect.inf.ethz.ch/.

Predicting web searcher satisfaction with existing community-based answers

Community-based Question Answering (CQA) sites, such as Yahoo! Answers, Baidu Knows, Naver, and Quora, have been rapidly growing in popularity. The resulting archives of posted answers to questions, in Yahoo! Answers alone, already exceed in size 1 billion, and are aggressively indexed by web search engines. In fact, a large number of search engine users benefit from these archives, by finding existing answers that address their own queries. This scenario poses new challenges and opportunities for both search engines and CQA sites. To this end, we formulate a new problem of predicting the satisfaction of web searchers with CQA answers. We analyze a large number of web searches that result in a visit to a popular CQA site, and identify unique characteristics of searcher satisfaction in this setting, namely, the effects of query clarity, query-to-question match, and answer quality. We then propose and evaluate several approaches to predicting searcher satisfaction that exploit these characteristics. To the best of our knowledge, this is the first attempt to predict and validate the usefulness of CQA archives for external searchers, rather than for the original askers. Our results suggest promising directions for improving and exploiting community question answering services in pursuit of satisfying even more Web search queries.

Feature Selection via Coalitional Game Theory

We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can optimize various performance measures over unseen data such as accuracy, balanced error rate, and area under receiver-operator-characteristic curve. Empirical comparison with several other existing feature selection methods shows that the backward elimination variant of CSA leads to the most accurate classification results on an array of data sets.