By Fred Kaplan
"A e-book that grips, informs, and alarms, finely researched and lucidly related." —John le Carré
As cyber-attacks dominate front-page information, as hackers sign up for terrorists at the record of worldwide threats, and as best generals warn of a coming cyber conflict, few books are extra well timed and enlightening than Dark Territory: the key historical past of Cyber War, by way of Slate columnist and Pulitzer Prize–winning journalist Fred Kaplan.
Kaplan probes the internal corridors of the nationwide safeguard organisation, the beyond-top-secret cyber devices within the Pentagon, the "information warfare" squads of the army companies, and the nationwide protection debates within the White condo, to inform this never-before-told tale of the officials, policymakers, scientists, and spies who devised this new type of battle and who've been planning—and (more frequently than humans be aware of) fighting—these wars for decades.
From the 1991 Gulf conflict to conflicts in Haiti, Serbia, Syria, the previous Soviet republics, Iraq, and Iran, the place cyber conflict performed an important position, Dark Territory chronicles, in interesting element, a little-known prior that shines an unsettling mild on our destiny.
Read Online or Download Dark Territory: The Secret History of Cyber War PDF
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Additional info for Dark Territory: The Secret History of Cyber War
From 34 | Chapter 3: K-Nearest Neighbors Classification there, we’ll delve deeper into how to convert a raw image into an avatar. Then we’ll extract features from those avatar images. When we have enough features, we’ll then use KNN to build a neighborhood of faces that will help us determine attributes of input images. Setup notes All of the code for this example can be found on GitHub. Because Ruby is constantly changing, the README file should include the most up-to-date instructions on getting these code examples working.
The Neighborhood Class At this point, we have enough information to build a Neighborhood class, which will find us the closest features, and tally attributes associated with the pictures in which those features are contained. As you can see, we want to match each feature of the image to a big set of features. As soon as we find the closest K of those features, each one of those close features will have an image associated with it. And for all of those faces, there are associated attributes that we’ll use to calculate the eventual classification.
K in the K-Nearest Neighbors algorithm is an arbitrary number generally ranging from 1 to the number of data points. With that much of a range, you might think that it’s difficult to pick out the optimal K, but in practice it’s not really such a vast deci‐ sion. You have three primary options for choosing K: • Guessing • Using a heuristic • Optimizing using an algorithm Guessing K Guessing is the easiest solution. In the case of classifying brands into groups, we just pick 11 as a good guess. We know that 11 orders is probably sufficient to get a good idea of how a person shops.