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How to learn Hacking Part 2

Now that you have read the basics here at http://www.decisionstats.com/how-to-learn-to-be-a-hacker-easily/ (please do read this before reading the below)

 

Here is a list of tutorials that you should study (in order of ease)

1) LEARN BASICS – enough to get you a job maybe if that’s all you wanted.

http://www.offensive-security.com/metasploit-unleashed/Main_Page

2) READ SOME MORE-

Lena’s Reverse Engineering Tutorial-“Use Google.com  for finding the Tutorial

Lena’s Reverse Engineering tutorial. It includes 36 parts of individual cracking techniques and will teach you the basics of protection bypassing

01. Olly + assembler + patching a basic reverseme
02. Keyfiling the reverseme + assembler
03. Basic nag removal + header problems
04. Basic + aesthetic patching
05. Comparing on changes in cond jumps, animate over/in, breakpoints
06. “The plain stupid patching method”, searching for textstrings
07. Intermediate level patching, Kanal in PEiD
08. Debugging with W32Dasm, RVA, VA and offset, using LordPE as a hexeditor
09. Explaining the Visual Basic concept, introduction to SmartCheck and configuration
10. Continued reversing techniques in VB, use of decompilers and a basic anti-anti-trick
11. Intermediate patching using Olly’s “pane window”
12. Guiding a program by multiple patching.
13. The use of API’s in software, avoiding doublechecking tricks
14. More difficult schemes and an introduction to inline patching
15. How to study behaviour in the code, continued inlining using a pointer
16. Reversing using resources
17. Insights and practice in basic (self)keygenning
18. Diversion code, encryption/decryption, selfmodifying code and polymorphism
19. Debugger detected and anti-anti-techniques
20. Packers and protectors : an introduction
21. Imports rebuilding
22. API Redirection
23. Stolen bytes
24. Patching at runtime using loaders from lena151 original
25. Continued patching at runtime & unpacking armadillo standard protection
26. Machine specific loaders, unpacking & debugging armadillo
27. tElock + advanced patching
28. Bypassing & killing server checks
29. Killing & inlining a more difficult server check
30. SFX, Run Trace & more advanced string searching
31. Delphi in Olly & DeDe
32. Author tricks, HIEW & approaches in inline patching
33. The FPU, integrity checks & loader versus patcher
34. Reversing techniques in packed software & a S&R loader for ASProtect
35. Inlining inside polymorphic code
36. Keygenning

If you want more free training – hang around this website

http://www.owasp.org/index.php/Cheat_Sheets

OWASP Cheat Sheet Series

Draft OWASP Cheat Sheets

3) SPEND SOME MONEY on TRAINING

http://www.corelan-training.com/index.php/training/corelan-live/

Course overview

Module 1 – The x86 environment

  • System Architecture
  • Windows Memory Management
  • Registers
  • Introduction to Assembly
  • The stack

Module 2 – The exploit developer environment

  • Setting up the exploit developer lab
  • Using debuggers and debugger plugins to gather primitives

Module 3 – Saved Return Pointer Overwrite

  • Functions
  • Saved return pointer overwrites
  • Stack cookies

Module 4 – Abusing Structured Exception Handlers

  • Abusing exception handler overwrites
  • Bypassing Safeseh

Module 5 – Pointer smashing

  • Function pointers
  • Data/object pointers
  • vtable/virtual functions

Module 6 – Off-by-one and integer overflows

  • Off-by-one
  • Integer overflows

Module 7 – Limited buffers

  • Limited buffers, shellcode splitting

Module 8 – Reliability++ & reusability++

  • Finding and avoiding bad characters
  • Creative ways to deal with character set limitations

Module 9 – Fun with Unicode

  • Exploiting Unicode based overflows
  • Writing venetian alignment code
  • Creating and Using venetian shellcode

Module 10 – Heap Spraying Fundamentals

  • Heap Management and behaviour
  • Heap Spraying for Internet Explorer 6 and 7

Module 11 – Egg Hunters

  • Using and tweaking Egg hunters
  • Custom egghunters
  • Using Omelet egghunters
  • Egghunters in a WoW64 environment

Module 12 – Shellcoding

  • Building custom shellcode from scratch
  • Understanding existing shellcode
  • Writing portable shellcode
  • Bypassing Antivirus

Module 13 – Metasploit Exploit Modules

  • Writing exploits for the Metasploit Framework
  • Porting exploits to the Metasploit Framework

Module 14 – ASLR

  • Bypassing ASLR

Module 15 – W^X

  • Bypassing NX/DEP
  • Return Oriented Programming / Code Reuse (ROP) )

Module 16 – Advanced Heap Spraying

  • Heap Feng Shui & heaplib
  • Precise heap spraying in modern browsers (IE8 & IE9, Firefox 13)

Module 17 – Use After Free

  • Exploiting Use-After-Free conditions

Module 18 – Windows 8

  • Windows 8 Memory Protections and Bypass
TRAINING SCHEDULES AT

ALSO GET CERTIFIED http://www.offensive-security.com/information-security-training/penetration-testing-with-backtrack/ ($950 cost)

the syllabus is here at

http://www.offensive-security.com/documentation/penetration-testing-with-backtrack.pdf

4) HANG AROUND OTHER HACKERS

At http://attrition.org/attrition/

or The Noir  Hat Conferences-

http://blackhat.com/html/bh-us-12/training/bh-us-12-training_complete.html

or read this website

http://software-security.sans.org/developer-how-to/

5) GET A DEGREE

Yes it is possible

 

See http://web.jhu.edu/jhuisi/

The Johns Hopkins University Information Security Institute (JHUISI) is the University’s focal point for research and education in information security, assurance and privacy.

Scholarship Information

 

The Information Security Institute is now accepting applications for the Department of Defense’s Information Assurance Scholarship Program (IASP).  This scholarship includes full tuition, a living stipend, books and health insurance. In return each student recipient must work for a DoD agency at a competitive salary for six months for every semester funded. The scholarship is open to American citizens only.

http://web.jhu.edu/jhuisi/mssi/index.html

MASTER OF SCIENCE IN SECURITY INFORMATICS PROGRAM

The flagship educational experience offered by Johns Hopkins University in the area of information security and assurance is represented by the Master of Science in Security Informatics degree.  Over thirty courses are available in support of this unique and innovative graduate program.

———————————————————–

Disclaimer- I havent done any of these things- This is just a curated list from Quora  so I am open to feedback.

You use this at your own risk of conscience ,local legal jurisdictions and your own legal liability.

 

 

 

 

 

 

Chrome

If you are new to using Chrome, there are many delightful features just beneath the surface.

If you are an Internet Explorer or Firefox or Safari or Arora or Opera or Sea Monkey browser user- this is one more reason to test, just test Chrome.

Ok so who Made chrome- (note the link i.e about:credits is what you type in chrome to see features)

about:credits

Credits

David M. Gay’s floating point routines
dynamic annotations
Netscape Portable Runtime (NSPR)
Network Security Services (NSS)
purify headers
google-glog’s symbolization library
valgrind
xdg-mime
xdg-user-dirs
google-jstemplateshow licensehomepage
Launchpad Translationsshow licensehomepage
Mozilla Personal Security Managershow licensehomepage
Google Toolbox for Macshow licensehomepage
ActiveX Scripting SDKshow licensehomepage
Almost Native Graphics Layer Engineshow licensehomepage
Apple sample codeshow licensehomepage
Google Cache Invalidation APIshow licensehomepage
Compact Language Detectionshow licensehomepage
OpenGL ES 2.0 Programming Guideshow licensehomepage
OpenGL ES 2.0 Conformance Testsshow licensehomepage
hunspell dictionariesshow licensehomepage
IAccessible2 COM interfaces for accessibilityshow licensehomepage
Chinese and Japanese Word Listshow licensehomepage
ISimpleDOM COM interfaces for accessibilityshow licensehomepage
modp base64 decodershow licensehomepage
NSBezierPath additions from Sean Patrick O’Brienshow licensehomepage
Cocoa extension code from Caminoshow licensehomepage
OTS (OpenType Sanitizer)show licensehomepage
Google Safe Browsingshow licensehomepage
XUL Runner SDKshow licensehomepage
and of course
so thats who made chrome.
  • Will Google be able to monetize Chrome the way it has monetized Android (Atleast by locking in both search,computing and browsing platforms)? I like the Adblock extension- and I would be happy to see more paid extensions. or even two versions one free and other freer (in choice) browsers for ads /security etc. maybe even a premium paid browser which has tor embedded in it , adblock enabled in it, and encrypted chat (like Waste Again) as an extension…. Hmm Hmm Hmm There is a SOCIAL version of Chromium called Rockmelt used ironically by Google Social Nemesis -Facebook (see http://blogs.ft.com/fttechhub/2011/06/facebook-partners-with-rockmelt-on-building-a-social-web-browser/)
  • Will Google share more revenue with open source contributors and thus create a new path in open source revenue generation just like it did with online advertising as an industry? Hmm Hmm Hmm. or Will Facebook continue to lead the way with extensions and applications (which did predate the mobile app place- so thats one innovation u gotta give to Zuk’s boys ;)
Back to Chrome-
To change settings- chrome://settings/browser
but to check what Autofill Data is stored within chrome (thats your credit card and your web form information)
chrome://settings/autofill and chrome://settings/content has all your content settings
Well Chrome is very very secure, or as secure as a browser can be in 2011.
You can set up Google Sync to keep all your data in the cloud, and it has an application specific password as well.
So hopefully you will have much more fun enjoying hacking Chromium ;)
See these

Using Views in R and comparing functions across multiple packages

Some RDF hacking relating to updating probabil...

Image via Wikipedia

R has almost 2923 available packages

This makes the task of searching among these packages and comparing functions for the same analytical task across different packages a bit tedious and prone to manual searching (of reading multiple Pdfs of help /vignette of packages) or sending an email to the R help list.

However using R Views is a slightly better way of managing all your analytical requirements for software rather than the large number of packages (see Graphics view below).

CRAN Task Views allow you to browse packages by topic and provide tools to automatically install all packages for special areas of interest. Currently, 28 views are available. http://cran.r-project.org/web/views/

Bayesian Bayesian Inference
ChemPhys Chemometrics and Computational Physics
ClinicalTrials Clinical Trial Design, Monitoring, and Analysis
Cluster Cluster Analysis & Finite Mixture Models
Distributions Probability Distributions
Econometrics Computational Econometrics
Environmetrics Analysis of Ecological and Environmental Data
ExperimentalDesign Design of Experiments (DoE) & Analysis of Experimental Data
Finance Empirical Finance
Genetics Statistical Genetics
Graphics Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization
gR gRaphical Models in R
HighPerformanceComputing High-Performance and Parallel Computing with R
MachineLearning Machine Learning & Statistical Learning
MedicalImaging Medical Image Analysis
Multivariate Multivariate Statistics
NaturalLanguageProcessing Natural Language Processing
OfficialStatistics Official Statistics & Survey Methodology
Optimization Optimization and Mathematical Programming
Pharmacokinetics Analysis of Pharmacokinetic Data
Phylogenetics Phylogenetics, Especially Comparative Methods
Psychometrics Psychometric Models and Methods
ReproducibleResearch Reproducible Research
Robust Robust Statistical Methods
SocialSciences Statistics for the Social Sciences
Spatial Analysis of Spatial Data
Survival Survival Analysis
TimeSeries Time Series Analysis

To automatically install these views, the ctv package needs to be installed, e.g., via

install.packages("ctv")
library("ctv")
Created by Pretty R at inside-R.org


and then the views can be installed via install.views or update.views (which first assesses which of the packages are already installed and up-to-date), e.g.,

install.views("Econometrics")
 update.views("Econometrics")
 Created by Pretty R at inside-R.org

CRAN Task View: Graphic Displays & Dynamic Graphics & Graphic Devices & Visualization

Maintainer: Nicholas Lewin-Koh
Contact: nikko at hailmail.net
Version: 2009-10-28

R is rich with facilities for creating and developing interesting graphics. Base R contains functionality for many plot types including coplots, mosaic plots, biplots, and the list goes on. There are devices such as postscript, png, jpeg and pdf for outputting graphics as well as device drivers for all platforms running R. lattice and grid are supplied with R’s recommended packages and are included in every binary distribution. lattice is an R implementation of William Cleveland’s trellis graphics, while grid defines a much more flexible graphics environment than the base R graphics.

R’s base graphics are implemented in the same way as in the S3 system developed by Becker, Chambers, and Wilks. There is a static device, which is treated as a static canvas and objects are drawn on the device through R plotting commands. The device has a set of global parameters such as margins and layouts which can be manipulated by the user using par() commands. The R graphics engine does not maintain a user visible graphics list, and there is no system of double buffering, so objects cannot be easily edited without redrawing a whole plot. This situation may change in R 2.7.x, where developers are working on double buffering for R devices. Even so, the base R graphics can produce many plots with extremely fine graphics in many specialized instances.

One can quickly run into trouble with R’s base graphic system if one wants to design complex layouts where scaling is maintained properly on resizing, nested graphs are desired or more interactivity is needed. grid was designed by Paul Murrell to overcome some of these limitations and as a result packages like latticeggplot2vcd or hexbin (on Bioconductor ) use grid for the underlying primitives. When using plots designed with grid one needs to keep in mind that grid is based on a system of viewports and graphic objects. To add objects one needs to use grid commands, e.g., grid.polygon() rather than polygon(). Also grid maintains a stack of viewports from the device and one needs to make sure the desired viewport is at the top of the stack. There is a great deal of explanatory documentation included with grid as vignettes.

The graphics packages in R can be organized roughly into the following topics, which range from the more user oriented at the top to the more developer oriented at the bottom. The categories are not mutually exclusive but are for the convenience of presentation:

  • Plotting : Enhancements for specialized plots can be found in plotrix, for polar plotting, vcd for categorical data, hexbin (on Bioconductor ) for hexagon binning, gclus for ordering plots and gplots for some plotting enhancements. Some specialized graphs, like Chernoff faces are implemented in aplpack, which also has a nice implementation of Tukey’s bag plot. For 3D plots latticescatterplot3d and misc3d provide a selection of plots for different kinds of 3D plotting. scatterplot3d is based on R’s base graphics system, while misc3d is based on rgl. The package onion for visualizing quaternions and octonions is well suited to display 3D graphics based on derived meshes.
  • Graphic Applications : This area is not much different from the plotting section except that these packages have tools that may not for display, but can aid in creating effective displays. Also included are packages with more esoteric plotting methods. For specific subject areas, like maps, or clustering the excellent task views contributed by other dedicated useRs is an excellent place to start.
    • Effect ordering : The gclus package focuses on the ordering of graphs to accentuate cluster structure or natural ordering in the data. While not for graphics directly cba and seriation have functions for creating 1 dimensional orderings from higher dimensional criteria. For ordering an array of displays, biclust can be useful.
    • Large Data Sets : Large data sets can present very different challenges from moderate and small datasets. Aside from overplotting, rendering 1,000,000 points can tax even modern GPU’s. For univariate datalvplot produces letter value boxplots which alleviate some of the problems that standard boxplots exhibit for large data sets. For bivariate data ash can produce a bivariate smoothed histogram very quickly, and hexbin, on Bioconductor , can bin bivariate data onto a hexagonal lattice, the advantage being that the irregular lines and orientation of hexagons do not create linear artifacts. For multivariate data, hexbin can be used to create a scatterplot matrix, combined with lattice. An alternative is to use scagnostics to produce a scaterplot matrix of “data about the data”, and look for interesting combinations of variables.
    • Trees and Graphs ape and ade4 have functions for plotting phylogenetic trees, which can be used for plotting dendrograms from clustering procedures. While these packages produce decent graphics, they do not use sophisticated algorithms for node placement, so may not be useful for very large trees. igraph has the Tilford-Rheingold algorithm implementead and is useful for plotting larger trees. diagram as facilities for flow diagrams and simple graphs. For more sophisticated graphs Rgraphviz and igraph have functions for plotting and layout, especially useful for representing large networks.
  • Graphics Systems lattice is built on top of the grid graphics system and is an R implementation of William Cleveland’s trellis system for S-PLUS. lattice allows for building many types of plots with sophisticated layouts based on conditioning. ggplot2 is an R implementation of the system described in “A Grammar of Graphics” by Leland Wilkinson. Like latticeggplot (also built on top of grid) assists in trellis-like graphics, but allows for much more. Since it is built on the idea of a semantics for graphics there is much more emphasis on reshaping data, transformation, and assembling the elements of a plot.
  • Devices : Whereas grid is built on top of the R graphics engine, many in the R community have found the R graphics engine somewhat inflexible and have written separate device drivers that either emphasize interactivity or plotting in various graphics formats. R base supplies devices for PostScript, PDF, JPEG and other formats. Devices on CRAN include cairoDevice which is a device based libcairo, which can actually render to many device types. The cairo device is desgned to work with RGTK2, which is an interface to the Gimp Tool Kit, similar to pyGTK2. GDD provides device drivers for several bitmap formats, including GIF and BMP. RSvgDevice is an SVG device driver and interfaces well with with vector drawing programs, or R web development packages, such as Rpad. When SVG devices are for web display developers should be aware that internet explorer does not support SVG, but has their own standard. Trust Microsoft. rgl provides a device driver based on OpenGL, and is good for 3D and interactive development. Lastly, the Augsburg group supplies a set of packages that includes a Java-based device, JavaGD.
  • Colors : The package colorspace provides a set of functions for transforming between color spaces and mixcolor() for mixing colors within a color space. Based on the HCL colors provided in colorspacevcdprovides a set of functions for choosing color palettes suitable for coding categorical variables ( rainbow_hcl()) and numerical information ( sequential_hcl()diverge_hcl()). Similar types of palettes are provided in RColorBrewer and dichromat is focused on palettes for color-impaired viewers.
  • Interactive Graphics : There are several efforts to implement interactive graphics systems that interface well with R. In an interactive system the user can interactively query the graphics on the screen with the mouse, or a moveable brush to zoom, pan and query on the device as well as link with other views of the data. rggobi embeds the GGobi interactive graphics system within R, so that one can display a data frame or several in GGobi directly from R. The package has functions to support longitudinal data, and graphs using GGobi’s edge set functionality. The RoSuDA repository maintained and developed by the University of Augsburg group has two packages, iplots and iwidgets as well as their Java development environment including a Java device, JavaGD. Their interactive graphics tools contain functions for alpha blending, which produces darker shading around areas with more data. This is exceptionally useful for parallel coordinate plots where many lines can quickly obscure patterns. playwith has facilities for building interactive versions of R graphics using the cairoDevice and RGtk2. Lastly, the rgl package has mechanisms for interactive manipulation of plots, especially 3D rotations and surfaces.
  • Development : For development of specialized graphics packages in R, grid should probably be the first consideration for any new plot type. rgl has better tools for 3D graphics, since the device is interactive, though it can be slow. An alternative is to use Java and the Java device in the RoSuDA packages, though Java has its own drawbacks. For porting plotting code to grid, using the package gridBase presents a nice intermediate step to embed base graphics in grid graphics and vice versa.

Google stuck on Gears

Google has launched support for Droid the mobile operating system but forgot to include support for their own browser- Chromium. Atleast if you can support Windows Explorer and Firefox for Gears, surely you can add support for Gears for Chromium.Maybe with an Ad or two ;) .Since Al Gore invented the internet and he sits as a consultant for the California boys, maybe he can advise them as well on the anti trust investigations with Apple (cough).

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