Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Networking together hundreds or thousands of cheap microsensor nodes allows users to accurately monitor a remote environment by intelligently combining the data from the individual nodes. These networks require robust wireless communication protocols that are energy efficient and provide low latency. We develop and analyze low-energy adaptive clustering hierarchy (LEACH), a protocol architecture for microsensor networks that combines the ideas of energy-efficient cluster-based routing and media access together with application-specific data aggregation to achieve good performance in terms of system lifetime, latency, and application-perceived quality. LEACH includes a new, distributed cluster formation technique that enables self-organization of large numbers of nodes, algorithms for adapting clusters and rotating cluster head positions to evenly distribute the energy load among all the nodes, and techniques to enable distributed signal processing to save communication resources. Our results show that LEACH can improve system lifetime by an order of magnitude compared with general-purpose multihop approaches.

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An application-specific protocol architecture for wireless microsensor networks

Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These

Phd by thesis

Data Mining - Concepts and Techniques.

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

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A survey of approaches to automatic schema matching

This paper presents a systematic solution to the persistent problem of buffer overflow attacks. Buffer overflow attacks gained notoriety in 1988 as part of the Morris Worm incident on the Internet. While it is fairly simple to fix individual buffer overflow vulnerabilities, buffer overflow attacks continue to this day. Hundreds of attacks have been discovered, and while most of the obvious vulnerabilities have now been patched, more sophisticated buffer overflow attacks continue to emerge.

We describe StackGuard: a simple compiler technique that virtually eliminates buffer overflow vulnerabilities with only modest performance penalties. Privileged programs that are recompiled with the StackGuard compiler extension no longer yield control to the attacker, but rather enter a fail-safe state. These programs require no source code changes at all, and are binary-compatible with existing operating systems and libraries. We describe the compiler technique (a simple patch to gcc), as well as a set of variations on the technique that trade-off between penetration resistance and performance. We present experimental results of both the penetration resistance and the performance impact of this technique.

/pdf/stackguard-automatic-adaptive-detection-and-prevention-of-59g5hygw3s.pdf

StackGuard: automatic adaptive detection and prevention of buffer-overflow attacks

The paper describes the methodology and the software development of XWRAP, an XML-enabled wrapper construction system for semi-automatic generation of wrapper programs. By XML-enabled we mean that the metadata about information content that are implicit in the original Web pages will be extracted and encoded explicitly as XML tags in the wrapped documents. In addition, the query based content filtering process is performed against the XML documents. The XWRAP wrapper generation framework has three distinct features. First, it explicitly separates tasks of building wrappers that are specific to a Web source from the tasks that are repetitive for any source, and uses a component library to provide basic building blocks for wrapper programs. Second, it provides a user friendly interface program to allow wrapper developers to generate their wrapper code with a few mouse clicks. Third and most importantly, we introduce and develop a two-phase code generation framework. The first phase utilizes an interactive interface facility to encode the source-specific metadata knowledge identified by individual wrapper developers as declarative information extraction rules. The second phase combines the information extraction rules generated at the first phase with the XWRAP component library to construct an executable wrapper program for the given Web source. We report the initial experiments on performance of the XWRAP code generation system and the wrapper programs generated by XWRAP.

XWRAP: an XML-enabled wrapper construction system for Web information sources

We introduce the concept of continual queries, describe the design of a distributed event-driven continual query system-OpenCQ, and outline the initial implementation of OpenCQ on top of the distributed interoperable information mediation system DIOM. Continual queries are standing queries that monitor update of interest and return results whenever the update reaches specified thresholds. In OpenCQ, users may specify to the system the information they would like to monitor (such as the events or the update thresholds they are interested in). Whenever the information of interest becomes available, the system immediately delivers it to the relevant users; otherwise, the system continually monitors the arrival of the desired information and pushes it to the relevant users as it meets the specified update thresholds. In contrast to conventional pull-based data management systems such as DBMSs and Web search engines, OpenCQ exhibits two important features: it provides push-enabled, event-driven, content-sensitive information delivery capabilities; and it combines pull and push services in a unified framework. By event-driven we mean that the update events of interest to be monitored are specified by users or applications. By content-sensitive, we mean the evaluation of the trigger condition happens only when a potentially interesting change occurs. By push-enabled, we mean the active delivery of query results or triggering of actions without user intervention.

Continual queries for Internet scale event-driven information delivery

Buffer overflows have been the most common form of security vulnerability for the last ten years. Moreover, buffer overflow vulnerabilities dominate the area of remote network penetration vulnerabilities, where an anonymous Internet user seeks to gain partial or total control of a host. If buffer overflow vulnerabilities could be effectively eliminated, a very large portion of the most serious security threats would also be eliminated. We survey the various types of buffer overflow vulnerabilities and attacks and survey the various defensive measures that mitigate buffer overflow vulnerabilities, including our own StackGuard method. We then consider which combinations of techniques can eliminate the problem of buffer overflow vulnerabilities, while preserving the functionality and performance of existing systems.

Buffer overflows: attacks and defenses for the vulnerability of the decade

In this paper we propose changing the decades-old practice of allocating CPU to threads based on priority to a scheme based on proportion and period. Our scheme allocates to each thread a percentage of CPU cycles over a period of time, and uses a feedback-based adaptive scheduler to assign automatically both proportion and period. Applications with known require-ments, such as isochronous software devices, can bypass the adaptive scheduler by specifying their desired proportion and/or period. As a result, our scheme provides reservations to applica-tions that need them, and the benefits of proportion and period to applications that do not need reservations. Adaptive scheduling using proportion and period has several distinct benefits over either fixed or adaptive priority based schemes: finer grain control of allocation, lower variance in the amount of cycles allocated to a thread, and avoidance of accidental priority inversion and starvation, including defense against denial-of-service attacks. This paper describes our design of an adaptive controller and proportion-period scheduler, its implementation in Linux, and presents experimental validation of our approach.

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Calton Pu

Papers

StackGuard: automatic adaptive detection and prevention of buffer-overflow attacks

XWRAP: an XML-enabled wrapper construction system for Web information sources

Continual queries for Internet scale event-driven information delivery

Buffer overflows: attacks and defenses for the vulnerability of the decade

A feedback-driven proportion allocator for real-rate scheduling