Questões de Concurso

January 23, 2009
Worm Infects Millions of Computers Worldwide
By JOHN MARKOFF

    A new digital plague has hit the Internet, infecting millions of personal and business computers in what seems to be the first step of a multistage attack. The world’s leading computer security experts do not yet know who programmed the infection, or what the next stage will be.
    In recent weeks a worm, a malicious software program, has swept through corporate, educational and public computer networks around the world. Known as Conficker or Downandup, it is spread by a recently discovered Microsoft Windows vulnerability, by guessing network passwords and by hand-carried consumer gadgets like USB keys.
    Experts say it is the K1K1 infection since the Slammer worm exploded through the Internet in January 2003, and it may have infected as many as nine million personal computers around the world.
    Worms like Conficker not only ricochet around the Internet at lightning speed, they harness infected computers into unified systems called botnets, which can then accept programming instructions from their clandestine masters.
    Many computer users may not notice that their machines have been infected, and computer security researchers said they were waiting for the instructions to materialize, to determine what impact the botnet will have on PC users. It might operate in the background, using the infected computer to send spam or infect other computers, or it might steal the PC user’s personal information.
    Microsoft rushed an emergency patch to defend the Windows operating systems against this vulnerability in October, yet the worm has continued to spread even as the level of warnings has grown in recent weeks.
    Earlier this week, security researchers at Qualys, a Silicon Valley security firm, estimated that about 30 percent of Windows-based computers attached to the Internet remain vulnerable to infection because they have not been updated with the patch, despite the fact that it was made available in October.
    Unraveling the program has been particularly challenging because it comes with encryption mechanisms that hide its internal workings from those seeking to disable it.
    The program uses an elaborate shell-game-style technique to permit someone to command it remotely. Each day it generates a new list of 250 domain names. Instructions from any one of these domain names would be obeyed. To control the botnet, an attacker would need only to register a single domain to send instructions to the botnet globally, greatly complicating the task of law enforcement and security companies trying to intervene and block the activation of the botnet.
    Several computer security firms said that although Conficker appeared to have been written from scratch, it had parallels to the work of a suspected Eastern European criminal gang that has profited by sending programs known as “scareware” to personal computers that seem to warn users of an infection and ask for credit card numbers to pay for bogus antivirus software that actually further infects their computer.
    One intriguing clue left by the malware authors is that the first version of the program checked to see if the computer had a Ukrainian keyboard layout. If it found it had such a keyboard, it would not infect the machine, according to Phillip Porras, a security investigator at SRI International who has disassembled the program to determine how it functioned.

(Adapted from The New York Times)

January 23, 2009
Worm Infects Millions of Computers Worldwide
By JOHN MARKOFF

    A new digital plague has hit the Internet, infecting millions of personal and business computers in what seems to be the first step of a multistage attack. The world’s leading computer security experts do not yet know who programmed the infection, or what the next stage will be.
    In recent weeks a worm, a malicious software program, has swept through corporate, educational and public computer networks around the world. Known as Conficker or Downandup, it is spread by a recently discovered Microsoft Windows vulnerability, by guessing network passwords and by hand-carried consumer gadgets like USB keys.
    Experts say it is the K1K1 infection since the Slammer worm exploded through the Internet in January 2003, and it may have infected as many as nine million personal computers around the world.
    Worms like Conficker not only ricochet around the Internet at lightning speed, they harness infected computers into unified systems called botnets, which can then accept programming instructions from their clandestine masters.
    Many computer users may not notice that their machines have been infected, and computer security researchers said they were waiting for the instructions to materialize, to determine what impact the botnet will have on PC users. It might operate in the background, using the infected computer to send spam or infect other computers, or it might steal the PC user’s personal information.
    Microsoft rushed an emergency patch to defend the Windows operating systems against this vulnerability in October, yet the worm has continued to spread even as the level of warnings has grown in recent weeks.
    Earlier this week, security researchers at Qualys, a Silicon Valley security firm, estimated that about 30 percent of Windows-based computers attached to the Internet remain vulnerable to infection because they have not been updated with the patch, despite the fact that it was made available in October.
    Unraveling the program has been particularly challenging because it comes with encryption mechanisms that hide its internal workings from those seeking to disable it.
    The program uses an elaborate shell-game-style technique to permit someone to command it remotely. Each day it generates a new list of 250 domain names. Instructions from any one of these domain names would be obeyed. To control the botnet, an attacker would need only to register a single domain to send instructions to the botnet globally, greatly complicating the task of law enforcement and security companies trying to intervene and block the activation of the botnet.
    Several computer security firms said that although Conficker appeared to have been written from scratch, it had parallels to the work of a suspected Eastern European criminal gang that has profited by sending programs known as “scareware” to personal computers that seem to warn users of an infection and ask for credit card numbers to pay for bogus antivirus software that actually further infects their computer.
    One intriguing clue left by the malware authors is that the first version of the program checked to see if the computer had a Ukrainian keyboard layout. If it found it had such a keyboard, it would not infect the machine, according to Phillip Porras, a security investigator at SRI International who has disassembled the program to determine how it functioned.

(Adapted from The New York Times)

Atenção: As questões de números 53 a 56 referem-se ao texto abaixo.

    Managing government has never been more challenging. Public sector leaders face increasing expectations from citizens, businesses, elected officials, and employees. Pressed to provide services and information more quickly and cost effectively than [ADVERB], they are often saddled with legacy information systems that fail to leverage current technology or provide an enterprise-wide view of government.
    To meet these challenges, Oracle introduces Oracle iGovernment, a platform for innovative, integrated, and intelligent operations that lets you tear down silos and link frontand back-office operations while reducing costs and increasing responsiveness. With Oracle iGovernment you can modernize your IT infrastructure, increase efficiency and transparency, and improve service delivery.
    Oracle is the world's largest business software company, with more than 345,000 customers. And only Oracle delivers a complete platform of database, middleware, and applications—all based on open standards—to transform your organization.
    Oracle has reinforced and extended its public sector offerings with the acquisition of Sun, the leading provider of open-standards based Solaris and Linux platforms, architect of the award-winning SPARC processor family, and a key founding member of the Java community. Oracle's Sun solutions for the public sector provide a comprehensive set of capabilities for mainframe rehosting and migration and enterprise consolidation, enhancing our complete, open, and integrated solutions that support Oracle's iGovernment vision. Sun's advanced systems, storage, and services are designed to give you the value you expect, the quality you need, and the expertise you require to fully modernize your IT infrastructure.

Atenção: As questões de números 53 a 60 referem-se ao texto abaixo.

Data mining
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Not to be confused with information extraction.

    Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.
    The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques on sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

    [edit] Background

    The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns. It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)
    A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and [CONJUNCTION] may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for humangenerated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.

Atenção: As questões de números 53 a 60 referem-se ao texto abaixo.

Data mining
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Not to be confused with information extraction.

    Data mining is the process of extracting patterns from data. Data mining is seen as an increasingly important tool by modern business to transform data into an informational advantage. It is currently used in a wide range of profiling practices, such as marketing, surveillance, fraud detection, and scientific discovery.
    The related terms data dredging, data fishing and data snooping refer to the use of data mining techniques on sample portions of the larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered (see also data-snooping bias). These techniques can, however, be used in the creation of new hypotheses to test against the larger data populations.

    [edit] Background

    The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection and storage. As data sets have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks, clustering, genetic algorithms (1950s), decision trees (1960s) and support vector machines (1980s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns. It has been used for many years by businesses, scientists and governments to sift through volumes of data such as airline passenger trip records, census data and supermarket scanner data to produce market research reports. (Note, however, that reporting is not always considered to be data mining.)
    A primary reason for using data mining is to assist in the analysis of collections of observations of behaviour. Such data are vulnerable to collinearity because of unknown interrelations. An unavoidable fact of data mining is that the (sub-)set(s) of data being analysed may not be representative of the whole domain, and [CONJUNCTION] may not contain examples of certain critical relationships and behaviours that exist across other parts of the domain. To address this sort of issue, the analysis may be augmented using experiment-based and other approaches, such as Choice Modelling for humangenerated data. In these situations, inherent correlations can be either controlled for, or removed altogether, during the construction of the experimental design.