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Libros de Programación

Libros de Programación

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Libros en pdf sobre programación, informática, hacking, carding, etc. Apoya por PayPal https://paypal.me/jctc01 @openve

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Libros en pdf sobre programación, informática, hacking, carding, etc. Apoya por PayPal https://paypal.me/jctc01 @openve

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19 383
Obunachilar
-324 soatlar
+157 kunlar
-10130 kunlar
Postlar arxiv
http://jdebp.eu./FGA/operating-system-books.html A book list for operating system kernel developers and device driver writers

https://www.math.auckland.ac.nz/~sgal018/crypto-book/main.pdf Mathematics of Public Key Cryptography. Version 2.0 Steven D Galbraith October 31, 2018 696 pp

Michael_Kerrisk_The_Linux_programming.pdf6.92 MB

Michael Kerrisk-The Linux programming interface_ a Linux and UNIX system programming handbook-No Starch Press (2010)
Michael Kerrisk-The Linux programming interface_ a Linux and UNIX system programming handbook-No Starch Press (2010)

ANSI_Common_Lisp_-_Paul_Graham.pdf5.85 MB

On LISP Advanced Techniques for Common LISP - Paul Graham.pdf1.06 MB

Edmund_Weitz_Common_Lisp_Recipes_.pdf8.04 MB

photo content

paip.pdf17.45 MB

Common_lisp_touretzky.pdf1.08 MB

Land of Lisp.epub8.29 MB

https://ins.jku.at/sites/default/files/thesis/MasterThesis_Hengstberger_2016.pdf Steganography in file systems for mobile environments with plausible deniability

Linux Device Drivers Development(2017).epub1.80 MB

Clojure for the Brave and True(2015).pdf6.63 MB

https://arxiv.org/pdf/1811.04288.pdf IP Geolocation through Reverse DNS Ovidiu Dan, Vaibhav Parikh, Brian D. Davison (Submitted on 10 Nov 2018) IP Geolocation databases are widely used in online services to map end user IP addresses to their geographical locations. However, they use proprietary geolocation methods and in some cases they have poor accuracy. We propose a systematic approach to use publicly accessible reverse DNS hostnames for geolocating IP addresses. Our method is designed to be combined with other geolocation data sources. We cast the task as a machine learning problem where for a given hostname, we generate and rank a list of potential location candidates. We evaluate our approach against three state of the art academic baselines and two state of the art commercial IP geolocation databases. We show that our work significantly outperforms the academic baselines, and is complementary and competitive with commercial databases. To aid reproducibility, we open source our entire approach.

Statistics for Machine Learning_with Python and R-(2017).epub12.06 MB