In a world where the internet is ruled by funny cats the cat is the sole redeeming feature. Nearly all top actors are wasted in a movie which will make Wonder Woman seem like Casablanca. Special effects are toonish and the direction is a set back for director. Almost all your money is redeemed in the two after credit scenes. One of which has a 🐱
going being sponsored to a Government of India sponsored talk on Big Data Analytics at Bangalore on Friday the 13 th of July. If you are in Bangalore, India you may drop in for a dekko. Schedule and Abstracts (i am on page 7 out 9) .
Your tax payer money is hard at work- (hassi majak only if you are a desi. hassi to fassi.)
13 July 2012 (9.30 – 11.00 & 11.30 – 1.00)
Big Data Big Analytics
The talk will showcase using open source technologies in statistical computing for big data, namely the R programming language and its use cases in big data analysis. It will review case studies using the Amazon Cloud, custom packages in R for Big Data, tools like Revolution Analytics RevoScaleR package, as well as the newly launched SAP Hana used with R. We will also review Oracle R Enterprise. In addition we will show some case studies using BigML.com (using Clojure) , and approaches using PiCloud. In addition it will showcase some of Google APIs for Big Data Analysis.
Lastly we will talk on social media analysis ,national security use cases (i.e. cyber war) and privacy hazards of big data analytics.
Amazon gets some competition, and customers should see some relief, unless Google withdraws commitment on these products after a few years of trying (like it often does now!)
|Machine Type Pricing|
|Configuration||Virtual Cores||Memory||GCEU *||Local disk||Price/Hour||$/GCEU/hour|
|n1-standard-1-d||1||3.75GB ***||2.75||420GB ***||$0.145||0.053|
|n1-standard-8-d||8||30GB||22||2 x 1770GB||$1.16||0.053|
|Egress to the same Zone.||Free|
|Egress to a different Cloud service within the same Region.||Free|
|Egress to a different Zone in the same Region (per GB)||$0.01|
|Egress to a different Region within the US||$0.01 ****|
|Inter-continental Egress||At Internet Egress Rate|
|Internet Egress (Americas/EMEA destination) per GB|
|0-1 TB in a month||$0.12|
|Internet Egress (APAC destination) per GB|
|0-1 TB in a month||$0.21|
|Persistent Disk Pricing|
|Provisioned space||$0.10 GB/month|
|Snapshot storage**||$0.125 GB/month|
|IO Operations||$0.10 per million|
|IP Address Pricing|
|Static IP address (assigned but unused)||$0.01 per hour|
|Ephemeral IP address (attached to instance)||Free|
** coming soon
*** 1GB is defined as 2^30 bytes
**** promotional pricing; eventually will be charged at internet download rates
Google Prediction API
Tap into Google’s machine learning algorithms to analyze data and predict future outcomes.
Leverage machine learning without the complexity
Use the familiar RESTful interface
Enter input in any format – numeric or text
Build smart apps
Learn how you can use Prediction API to build customer sentiment analysis, spam detection or document and email classification.
Google Translation API
Use Google Translate API to build multilingual apps and programmatically translate text in your webpage or application.
Translate text into other languages programmatically
Use the familiar RESTful interface
Take advantage of Google’s powerful translation algorithms
Build multilingual apps
Learn how you can use Translate API to build apps that can programmatically translate text in your applications or websites.
Analyze Big Data in the cloud using SQL and get real-time business insights in seconds using Google BigQuery. Use a fully-managed data analysis service with no servers to install or maintain.
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Google App Engine
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Here is an interview with Jason Kuo who works with SAP Analytics as Group Solutions Marketing Manager. Jason answers questions on SAP Analytics and it’s increasing involvement with R statistical language.
Ajay- What made you choose R as the language to tie important parts of your technology platform like HANA and SAP Predictive Analysis. Did you consider other languages like Julia or Python.
Jason- It’s the most popular. Over 50% of the statisticians and data analysts use R. With 3,500+ algorithms its arguably the most comprehensive statistical analysis language. That said,we are not closing the door on others.
Ajay- When did you first start getting interested in R as an analytics platform?
Jason- SAP has been tracking R for 5+ years. With R’s explosive growth over the last year or two, it made sense for us to dramatically increase our investment in R.
Ajay- Can we expect SAP to give back to the R community like Google and Revolution Analytics does- by sponsoring Package development or sponsoring user meets and conferences?
Will we see SAP’s R HANA package in this year’s R conference User 2012 in Nashville
Jason- Yes. We plan to provide a specific driver for HANA tables for input of the data to native R. This planned for end of 2012. We’ll then review our event strategy. SAP has been a sponsor of Predictive Analytics World for several years and was indeed a founding sponsor. We may be attending the year’s R conference in Nashville.
Ajay- What has been some of the initial customer feedback to your analytics expansion and offerings.
Jason- We have completed two very successful Pilots of the R Integration for HANA with two of SAP’s largest customers.
Jason has over 15 years of BI and Data Warehousing industry experience. Having worked at Oracle, Business Objects, and now SAP, Jason has been involved in numerous technical marketing roles involving performance management dashboards, information management, text analysis, predictive analytics, and now big data. He has a bachelor’s of science in operations research from the University of Michigan.
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.
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
If you want more free training – hang around this website
OWASP Cheat Sheet Series
- OWASP Top Ten Cheat Sheet
- Authentication Cheat Sheet
- Cross-Site Request Forgery (CSRF) Prevention Cheat Sheet
- Transport Layer Protection Cheat Sheet
- Cryptographic Storage Cheat Sheet
- Input Validation Cheat Sheet
- XSS Prevention Cheat Sheet
- DOM based XSS Prevention Cheat Sheet
- Forgot Password Cheat Sheet
- Query Parameterization Cheat Sheet
- SQL Injection Prevention Cheat Sheet
- Session Management Cheat Sheet
- HTML5 Security Cheat Sheet
- Web Service Security Cheat Sheet
- Application Security Architecture Cheat Sheet
- Logging Cheat Sheet
- JAAS Cheat Sheet
Draft OWASP Cheat Sheets
- Access Control Cheat Sheet
- REST Security Cheat Sheet
- Abridged XSS Prevention Cheat Sheet
- PHP Security Cheat Sheet
- Password Storage Cheat Sheet
- Secure Coding Cheat Sheet
- Threat Modeling Cheat Sheet
- Clickjacking Cheat Sheet
- Virtual Patching Cheat Sheet
- Secure SDLC Cheat Sheet
3) SPEND SOME MONEY on TRAINING
Module 1 – The x86 environment
- System Architecture
- Windows Memory Management
- 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
- 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
- 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
ALSO GET CERTIFIED http://www.offensive-security.com/information-security-training/penetration-testing-with-backtrack/ ($950 cost)
the syllabus is here at
4) HANG AROUND OTHER HACKERS
or The Noir Hat Conferences-
or read this website
5) GET A DEGREE
Yes it is possible
The Johns Hopkins University Information Security Institute (JHUISI) is the University’s focal point for research and education in information security, assurance and privacy.
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.
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.
This is review of the fashionably named book Machine Learning for Hackers by Drew Conway and John Myles White (O’Reilly ). The book is about hacking code in R.
The preface introduces the reader to the authors conception of what machine learning and hacking is all about. If the name of the book was machine learning for business analytsts or data miners, I am sure the content would have been unchanged though the popularity (and ambiguity) of the word hacker can often substitute for its usefulness. Indeed the many wise and learned Professors of statistics departments through out the civilized world would be mildly surprised and bemused by their day to day activities as hacking or teaching hackers. The book follows a case study and example based approach and uses the GGPLOT2 package within R programming almost to the point of ignoring any other native graphics system based in R. It can be quite useful for the aspiring reader who wishes to understand and join the booming market for skilled talent in statistical computing.
Chapter 1 has a very useful set of functions for data cleansing and formatting. It walks you through the basics of formatting based on dates and conditions, missing value and outlier treatment and using ggplot package in R for graphical analysis. The case study used is an Infochimps dataset with 60,000 recordings of UFO sightings. The case study is lucid, and done at a extremely helpful pace illustrating the powerful and flexible nature of R functions that can be used for data cleansing.The chapter mentions text editors and IDEs but fails to list them in a tabular format, while listing several other tables like Packages used in the book. It also jumps straight from installation instructions to functions in R without getting into the various kinds of data types within R or specifying where these can be referenced from. It thus assumes a higher level of basic programming understanding for the reader than the average R book.
Chapter 2 discusses data exploration, and has a very clear set of diagrams that explain the various data summary operations that are performed routinely. This is an innovative approach and will help students or newcomers to the field of data analysis. It introduces the reader to type determination functions, as well different kinds of encoding. The introduction to creating functions is quite elegant and simple , and numerical summary methods are explained adequately. While the chapter explains data exploration with the help of various histogram options in ggplot2 , it fails to create a more generic framework for data exploration or rules to assist the reader in visual data exploration in non standard data situations. While the examples are very helpful for a reader , there needs to be slightly more depth to step out of the example and into a framework for visual data exploration (or references for the same). A couple of case studies however elaborately explained cannot do justice to the vast field of data exploration and especially visual data exploration.
Chapter 3 discussed binary classification for the specific purpose for spam filtering using a dataset from SpamAssassin. It introduces the reader to the naïve Bayes classifier and the principles of text mining suing the tm package in R. Some of the example codes could have been better commented for easier readability in the book. Overall it is quite a easy tutorial for creating a naïve Bayes classifier even for beginners.
Chapter 4 discusses the issues in importance ranking and creating recommendation systems specifically in the case of ordering email messages into important and not important. It introduces the useful grepl, gsub, strsplit, strptime ,difftime and strtrim functions for parsing data. The chapter further introduces the reader to the concept of log (and affine) transformations in a lucid and clear way that can help even beginners learn this powerful transformation concept. Again the coding within this chapter is sparsely commented which can cause difficulties to people not used to learn reams of code. ( it may have been part of the code attached with the book, but I am reading an electronic book and I did not find an easy way to go back and forth between the code and the book). The readability of the chapters would be further enhanced by the use of flow charts explaining the path and process followed than overtly verbose textual descriptions running into multiple pages. The chapters are quite clearly written, but a helpful visual summary can help in both revising the concepts and elucidate the approach taken further.A suggestion for the authors could be to compile the list of useful functions they introduce in this book as a sort of reference card (or Ref Card) for R Hackers or atleast have a chapter wise summary of functions, datasets and packages used.
Chapter 5 discusses linear regression , and it is a surprising and not very good explanation of regression theory in the introduction to regression. However the chapter makes up in practical example what it oversimplifies in theory. The chapter on regression is not the finest chapter written in this otherwise excellent book. Part of this is because of relative lack of organization- correlation is explained after linear regression is explained. Once again the lack of a function summary and a process flow diagram hinders readability and a separate section on regression metrics that help make a regression result good or not so good could be a welcome addition. Functions introduced include lm.
Chapter 6 showcases Generalized Additive Model (GAM) and Polynomial Regression, including an introduction to singularity and of over-fitting. Functions included in this chapter are transform, and poly while the package glmnet is also used here. The chapter also introduces the reader formally to the concept of cross validation (though examples of cross validation had been introduced in earlier chapters) and regularization. Logistic regression is also introduced at the end in this chapter.
Chapter 7 is about optimization. It describes error metric in a very easy to understand way. It creates a grid by using nested loops for various values of intercept and slope of a regression equation and computing the sum of square of errors. It then describes the optim function in detail including how it works and it’s various parameters. It introduces the curve function. The chapter then describes ridge regression including definition and hyperparameter lamda. The use of optim function to optimize the error in regression is useful learning for the aspiring hacker. Lastly it describes a case study of breaking codes using the simplistic Caesar cipher, a lexical database and the Metropolis method. Functions introduced in this chapter include .Machine$double.eps .
Chapter 8 deals with Principal Component Analysis and unsupervised learning. It uses the ymd function from lubridate package to convert string to date objects, and the cast function from reshape package to further manipulate the structure of data. Using the princomp functions enables PCA in R.The case study creates a stock market index and compares the results with the Dow Jones index.
Chapter 9 deals with Multidimensional Scaling as well as clustering US senators on the basis of similarity in voting records on legislation .It showcases matrix multiplication using %*% and also the dist function to compute distance matrix.
Chapter 10 has the subject of K Nearest Neighbors for recommendation systems. Packages used include class ,reshape and and functions used include cor, function and log. It also demonstrates creating a custom kNN function for calculating Euclidean distance between center of centroids and data. The case study used is the R package recommendation contest on Kaggle. Overall a simplistic introduction to creating a recommendation system using K nearest neighbors, without getting into any of the prepackaged packages within R that deal with association analysis , clustering or recommendation systems.
Chapter 11 introduces the reader to social network analysis (and elements of graph theory) using the example of Erdos Number as an interesting example of social networks of mathematicians. The example of Social Graph API by Google for hacking are quite new and intriguing (though a bit obsolete by changes, and should be rectified in either the errata or next edition) . However there exists packages within R that should be atleast referenced or used within this chapter (like TwitteR package that use the Twitter API and ROauth package for other social networks). Packages used within this chapter include Rcurl, RJSONIO, and igraph packages of R and functions used include rbind and ifelse. It also introduces the reader to the advanced software Gephi. The last example is to build a recommendation engine for whom to follow in Twitter using R.
Chapter 12 is about model comparison and introduces the concept of Support Vector Machines. It uses the package e1071 and shows the svm function. It also introduces the concept of tuning hyper parameters within default algorithms . A small problem in understanding the concepts is the misalignment of diagram pages with the relevant code. It lastly concludes with using mean square error as a method for comparing models built with different algorithms.
Overall the book is a welcome addition in the library of books based on R programming language, and the refreshing nature of the flow of material and the practicality of it’s case studies make this a recommended addition to both academic and corporate business analysts trying to derive insights by hacking lots of heterogeneous data.
Have a look for yourself at-
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