37770250
A Theoretical Analysis of Query Selection for Collaborative Filtering
We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple and fast algorithm for a theoretical model of this problem with a provable approximation guarantee, and prove that solving the problem exactly is NP-Hard.
12294439
Learning to Separate Text Content and Style for Classification
Many text documents naturally have two kinds of labels. For example, we may label web pages from universities according to their categories, such as “student” or “faculty”, or according the source universities, such as “Cornell” or “Texas”. We call one kind of labels the content and the other kind the style. Given a set of documents, each with both content and style labels, we seek to effectively learn to classify a set of documents in a new style with no content labels into its content classes. Assuming that every document is generated using words drawn from a mixture of two multinomial component models, one content model and one style model, we propose a method named Cartesian EM that constructs content models and style models through Expectation Maximization and performs classification of the unknown content classes transductively. Our experiments on real-world datasets show the proposed method to be effective for style independent text content classification.
573509
Boosting the margin: A new explanation for the effectiveness of voting methods
One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated classifier usually does not increase as its size becomes very large, and often is observed to decrease even after the training error reaches zero. In this paper, we show that this phenomenon is related to the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label. We show that techniques used in the analysis of Vapnik's support vector classifiers and of neural networks with small weights can be applied to voting methods to relate the margin distribution to the test error. We also show theoretically and experimentally that boosting is especially effective at increasing the margins of the training examples. Finally, we compare our explanation to those based on the bias-variance
2748729
Partially Supervised Classification of Text Documents
We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of documents, identify the documents from class P in M . The key feature of this problem is that there is no labeled nonP document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation.
1176859
RankCut - A Domain Independent Forward Pruning Method for Games
Forward pruning, also known as selective search, is now employed in many strong game-playing programs. In this paper, we introduce RankCut – a domain independent forward pruning technique which makes use of move ordering, and prunes once no better move is likely to be available. Since gameplaying programs already perform move ordering to improve the performance of αβ search, this information is available at no extra cost. As RankCut uses additional information untapped by current forward pruning techniques, RankCut is a complementary forward pruning method that can be used with existing methods, and is able to achieve improvements even when conventional pruning techniques are simultaneously employed. We implemented RankCut in a modern open-source chess program, CRAFTY. RankCut reduces the game-tree size by approximately 10%-40% for search depths 8-12 while retaining tactical reliability, when implemented alongside CRAFTY’s existing forward pruning techniques.
9023584
Text classification with kernels on the multinomial manifold
Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space.
2400440
Protecting Secret Data from Insider Attacks
We consider defenses against confidentiality and integrity attacks on data following break-ins, or so-called intrusion resistant storage technologies. We investigate the problem of protecting secret data, assuming an attacker is inside a target network or has compromised a system. 
 
We give a definition of the problem area, and propose a solution, VAST, that uses large, structured files to improve the secure storage of valuable or secret data. Each secret has its multiple shares randomly distributed in an extremely large file. Random decoy shares and the lack of usable identification information prevent selective copying or analysis of the file. No single part of the file yields useful information in isolation from the rest. The file’s size and structure therefore present an enormous additional hurdle to attackers attempting to transfer, steal or analyze the data. The system also has the remarkable property of healing itself after malicious corruption, thereby preserving both the confidentiality and integrity of the data.
13874800
A Data Mining and CIDF Based Approach for Detecting Novel and Distributed Intrusions
As the recent distributed Denial-of-Service (DDOS) attacks on several major Internet sites have shown us, no open computer network is immune from intrusions. Furthermore, intrusion detection systems (IDSs) need to be updated timely whenever a novel intrusion surfaces; and geographically distributed IDSs need to cooperate to detect distributed and coordinated intrusions. In this paper, we describe an experimental system, based on the Common Intrusion Detection Framework (CIDF), where multiple IDSs can exchange attack information to detect distributed intrusions. The system also includes an ID model builder, where a data mining engine can receive audit data of a novel attack from an IDS, compute a new detection model, and then distribute it to other IDSs. We describe our experiences in implementing such system and the preliminary results of deploying the system in an experimental network.
7965599
q-gram matching using tree models
q-gram matching is used for approximate substring matching problems in a wide range of application areas, including intrusion detection. In this paper, we present a tree-based model to perform fast linear time q-gram matching. All q-grams present in the text are stored in a tree structure similar to trie. We use a tree redundancy pruning algorithm to reduce the size of the tree without losing any information. We also use suffix links for fast q-gram search during query matching. We compare our work with the Rabin-Karp-based hash-table technique, commonly used for multiple q-gram search. We present results of experiments on system call sequence data used for intrusion detection.
5681818
Anomalous path detection with hardware support
Embedded systems are being deployed as a part of critical infrastructures and are vulnerable to malicious attacks due to internet accessibility. Intrusion detection systems have been proposed to protect computer systems from unauthorized penetration. Detecting an attack early on pays off since further damage is avoided and in some cases, resilient recovery could be adopted. This is especially important for embedded systems deployed in critical infrastructures such as Power Grids etc. where a timely intervention could save catastrophes. An intrusion detection system monitors dynamic program behavior against normal program behavior and raises an alert when an anomaly is detected. The normal behavior is learnt by the system through training and profiling.However, all current intrusion detection systems are purely software based and thus suffer from large performance degradation due to constant monitoring operations inserted in application code. Due to the potential performance overheads, software based solutions cannot monitor program behavior at a very fine level of granularity, thus leaving potential security holes as shown in the literature. Another important drawback of such methods is that they are unable to detect intrusions in near real time and the time lag could prove disastrous in real time embedded systems. In this paper, we propose a hardware-based approach to verify program execution paths of target applications dynamically and to detect anomalous executions. With hardware support, our approach offers multiple advantages over software based solutions including minor performance degradation, much stronger detection capability (a larger variety of attacks get detected) and zero-latency reaction upon an anomaly for near real time detection and thus much better security.
15867299
Supporting XML Security Models Using Relational Databases: A Vision
As the secure distribution and sharing of information over the World Wide Web becomes increasingly important, the needs for flexible and efficient support of access control systems naturally arise. Since the eXtensible Markup Language (XML) is emerging as the format of the Internet era for storing and exchanging information, there have been, recently, many proposals to extend the XML model to incorporate security aspects. To the lesser or greater extent, however, such proposals neglect the fact that the data for XML documents will most likely reside in relational databases, and consequently do not utilize various security models proposed for and implemented in relational databases.