Bishop, Matt (Matthew A.) Introduction to computer security / Matt Bishop. p. cm. Includes bibliographical references and index. ISBN (hardcover. IS / TEL Introduction to Computer Security. 2. IS Computer Security: Art and Science, Matt Bishop, Addison- Wesley, Computer Security: Art and Science, Matt Bishop (available at co-op). In these 1 Introduction. 1 aracer.mobi˜wagner/lawsbookcolor/aracer.mobi
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𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗣𝗗𝗙 on ResearchGate | Introduction to Computer Security / M. Because the effectiveness of a firewall depends on its rules (Bishop, ). Course Objectives. To develop an understanding of basic computer security Title: Introduction to Computer Security. ◦. Author: Matt Bishop. ◦. Publisher: Pearson . The syllabus for this course can be downloaded here in PDF format. Introduction to Computer Security book. Read reviews from world's largest community for readers. In this authoritative book, widely respected practitione.
Future research advances in machine learning are focused on two areas. The first is in enhancing how these systems distinguish between variations of an algorithm. As different versions of an algorithm are run over the training data, there needs to be some way of deciding which version is "better.
Getting functions that can automatically and accurately distinguish between two algorithms based on minor differences in the outputs is an art form that no amount of data can improve.
The second is in the machine learning algorithms themselves. While much of machine learning depends on trying different variations of an algorithm on large amounts of data to see which is most successful, the initial formulation of the algorithm is still vitally important. The way the algorithms interact, the types of variations attempted, and the mechanisms used to test and redirect the algorithms are all areas of active research.
An overview of some of this work can be found here ; even trying to limit the research to 20 papers oversimplifies the work being done in the field.
None of these problems can be solved by throwing more data at the problem. Its AlphaGo computer program became a grandmaster in two steps. First, it was fed some enormous number of human-played games.
Then, the game played itself an enormous number of times, improving its own play along the way. In , AlphaGo beat the grandmaster Lee Sedol four games to one. While the training data in this case, the human-played games, was valuable, even more important was the machine learning algorithm used and the function that evaluated the relative merits of different game positions.
Just one year later, DeepMind was back with a follow-on system: AlphaZero. This go-playing computer dispensed entirely with the human-played games and just learned by playing against itself over and over again.
It plays like an alien. It also became a grandmaster in chess and shogi. These are abstract games, so it makes sense that a more abstract training process works well. But even something as visceral as facial recognition needs more than just a huge database of identified faces in order to work successfully. It needs the ability to separate a face from the background in a two-dimensional photo or video and to recognize the same face in spite of changes in angle, lighting, or shadows. Just adding more data may help, but not nearly as much as added research into what to do with the data once we have it.
Meanwhile, foreign-policy and defense experts are talking about AI as if it were the next nuclear arms race, with the country that figures it out best or first becoming the dominant superpower for the next century. But that didn't happen with nuclear weapons, despite research only being conducted by governments and in secret.
It certainly won't happen with AI, no matter how much data different nations or companies scoop up. It is true that China is investing a lot of money into artificial intelligence research: The Chinese government believes this will allow it to leapfrog other countries and companies in those countries and become a major force in this new and transformative area of computing -- and it may be right.
On the other hand, much of this seems to be a wasteful boondoggle. Slapping "AI" on pretty much anything is how to get funding. The Chinese Ministry of Education, for instance, promises to produce "50 world-class AI textbooks," with no explanation of what that means.
In the democratic world, the government is neither the leading researcher nor the leading consumer of AI technologies. AI research is much more decentralized and academic, and it is conducted primarily in the public eye. Research teams keep their training data and models proprietary but freely publish their machine learning algorithms. If you wanted to work on machine learning right now, you could download Microsoft's Cognitive Toolkit , Google's Tensorflow , or Facebook's Pytorch.
These aren't toy systems; these are the state-of-the art machine learning platforms.
AI is not analogous to the big science projects of the previous century that brought us the atom bomb and the moon landing. AI is a science that can be conducted by many different groups with a variety of different resources, making it closer to computer design than the space race or nuclear competition. It doesn't take a massive government-funded lab for AI research, nor the secrecy of the Manhattan Project.
The research conducted in the open science literature will trump research done in secret because of the benefits of collaboration and the free exchange of ideas.
While the United States should certainly increase funding for AI research, it should continue to treat it as an open scientific endeavor. Surveillance is not justified by the needs of machine learning, and real progress in AI doesn't need it. YOU might like it ;. Mariam Karaki rated it it was amazing Mar 11, Curtis Brinkman rated it it was ok Jun 04, Leo Victor Barcenas rated it it was amazing Aug 05, Kirby Flake rated it it was amazing Oct 07, Chris J rated it did not like it Jan 26, Juan rated it really liked it Oct 09, Camila Morelli rated it liked it Nov 23, Pratap rated it really liked it Mar 11, Bhume Bhumiratana rated it it was amazing Jun 27, Patrick Nelmida rated it it was amazing Jan 08, D rated it really liked it Sep 03, Vivek Agrawal rated it really liked it Apr 28, Tiffany Carruthers rated it did not like it Dec 18, Steve rated it liked it Nov 05, Ezekiel Jere rated it it was amazing Oct 13, Mia Cravin rated it did not like it Jun 19, Andrew rated it really liked it Dec 24, Ausberto Castro rated it really liked it Sep 24, Craig rated it really liked it Oct 22, Konstantin Ivanov rated it really liked it Jan 10, Matt Balthazar rated it it was ok Dec 26, Lori rated it liked it Jan 21, Paul J Carroll rated it it was amazing Dec 08, Ahmad Hajja rated it liked it Oct 09, Smw rated it really liked it Nov 06, Justin Hickman rated it liked it Aug 30, Han Xin rated it really liked it Jun 25, There are no discussion topics on this book yet.
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