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.
A recent announcement showing Teradata partnering with KXEN and Revolution Analytics for Teradata Analytics.
The Latest in Open Source Emerging Software Technologies
Teradata provides customers with two additional open source technologies – “R” technology from Revolution Analytics for analytics and GeoServer technology for spatial data offered by the OpenGeo organization – both of which are able to leverage the power of Teradata in-database processing for faster, smarter answers to business questions.
In addition to the existing world-class analytic partners, Teradata supports the use of the evolving “R” technology, an open source language for statistical computing and graphics. “R” technology is gaining popularity with data scientists who are exploiting its new and innovative capabilities, which are not readily available. The enhanced “R add-on for Teradata” has a 50 percent performance improvement, it is easier to use, and its capabilities support large data analytics. Users can quickly profile, explore, and analyze larger quantities of data directly in the Teradata Database to deliver faster answers by leveraging embedded analytics.
Teradata has partnered with Revolution Analytics, the leading commercial provider of “R” technology, because of customer interest in high-performing R applications that deliver superior performance for large-scale data. “Our innovative customers understand that big data analytics takes a smart approach to the entire infrastructure and we will enable them to differentiate their business in a cost-effective way,” said David Rich, chief executive officer, Revolution Analytics. “We are excited to partner with Teradata, because we see great affinity between Teradata and Revolution Analytics – we embrace parallel computing and the high performance offered by multi-core and multi-processor hardware.”
The Teradata Data Lab empowers business users and leading analytic partners to start building new analytics in less than five minutes, as compared to waiting several weeks for the IT department’s assistance.
“The Data Lab within the Teradata database provides the perfect foundation to enable self-service predictive analytics with KXEN InfiniteInsight,” said John Ball, chief executive officer, KXEN. “Teradata technologies, combined with KXEN’s automated modeling capabilities and in-database scoring, put the power of predictive analytics and data mining directly into the hands of business users. This powerful combination helps our joint customers accelerate insight by delivering top-quality models in orders of magnitude faster than traditional approaches.”
Read more at
Early Registration Deadline Approaches for UseR 2012
- Early Registration: Jan 23 24 – Feb 29
- Regular Registration: Mar 1 – May 12
- Late Registration: May 13 – June 4
- On-site Registration: June 12 – June 15
Vanderbilt University; Nashville, Tennessee, USA
12th-15th June 2012
Assistant to the Chair
Vanderbilt University School of Medicine
Department of Biostatistics
S-2323 Medical Center North
Nashville, TN 37232-2158
Integrates R Statistical Programming Language into Oracle Database 11g
Comprehensive In-Database Platform for Advanced Analytics
|Oracle Advanced Analytics — an option to Oracle Database 11g Enterprise Edition – extends the database into a comprehensive advanced analytics platform through two major components: Oracle R Enterprise and Oracle Data Mining. With Oracle Advanced Analytics, customers have a comprehensive platform for real-time analytic applications that deliver insight into key business subjects such as churn prediction, product recommendations, and fraud alerting.
Oracle R Enterprise tightly integrates the open source R programming language with the database to further extend the database with Rs library of statistical functionality, and pushes down computations to the database. Oracle R Enterprise dramatically advances the capability for R users, and allows them to use their existing R development skills and tools, and scripts can now also run transparently and scale against data stored in Oracle Database 11g.
Oracle Data Mining provides powerful data mining algorithms that run as native SQL functions for in-database model building and model deployment. It can be accessed through the SQL Developer extension Oracle Data Miner to build, evaluate, share and deploy predictive analytics methodologies. At the same time the high-performance Oracle-specific data mining algorithms are accessible from R.
|Oracle R Hadoop Connector||Gives R users high performance native access to Hadoop Distributed File System (HDFS) and MapReduce programming framework.|
Here is an interview with Kelci Miclaus, a researcher working with the JMP division of the SAS Institute, in which she demonstrates examples of how the R programming language is a great hit with JMP customers who like to be flexible.
Ajay- How has JMP been using integration with R? What has been the feedback from customers so far? Is there a single case study you can point out where the combination of JMP and R was better than any one of them alone?
Kelci- Feedback from customers has been very positive. Some customers are using JMP to foster collaboration between SAS and R modelers within their organizations. Many are using JMP’s interactive visualization to complement their use of R. Many SAS and JMP users are using JMP’s integration with R to experiment with more bleeding-edge methods not yet available in commercial software. It can be used simply to smooth the transition with regard to sending data between the two tools, or used to build complete custom applications that take advantage of both JMP and R.
One customer has been using JMP and R together for Bayesian analysis. He uses R to create MCMC chains and has found that JMP is a great tool for preparing the data for analysis, as well as displaying the results of the MCMC simulation. For example, the Control Chart platform and the Bubble Plot platform in JMP can be used to quickly verify convergence of the algorithm. The use of both tools together can increase productivity since the results of an analysis can be achieved faster than through scripting and static graphics alone.
I, along with a few other JMP developers, have written applications that use JMP scripting to call out to R packages and perform analyses like multidimensional scaling, bootstrapping, support vector machines, and modern variable selection methods. These really show the benefit of interactive visual analysis of coupled with modern statistical algorithms. We’ve packaged these scripts as JMP add-ins and made them freely available on our JMP User Community file exchange. Customers can download them and now employ these methods as they would a regular JMP platform. We hope that our customers familiar with scripting will also begin to contribute their own add-ins so a wider audience can take advantage of these new tools.
Ajay- Are there plans to extend JMP integration with other languages like Python?
Kelci- We do have plans to integrate with other languages and are considering integrating with more based on customer requests. Python has certainly come up and we are looking into possibilities there.
Ajay- How is R a complimentary fit to JMP’s technical capabilities?
Kelci- R has an incredible breadth of capabilities. JMP has extensive interactive, dynamic visualization intrinsic to its largely visual analysis paradigm, in addition to a strong core of statistical platforms. Since our brains are designed to visually process pictures and animated graphs more efficiently than numbers and text, this environment is all about supporting faster discovery. Of course, JMP also has a scripting language (JSL) allowing you to incorporate SAS code, R code, build analytical applications for others to leverage SAS, R and other applications for users who don’t code or who don’t want to code.
JSL is a powerful scripting language on its own. It can be used for dialog creation, automation of JMP statistical platforms, and custom graphic scripting. In other ways, JSL is very similar to the R language. It can also be used for data and matrix manipulation and to create new analysis functions. With the scripting capabilities of JMP, you can create custom applications that provide both a user interface and an interactive visual back-end to R functionality. Alternatively, you could create a dashboard using statistical and/or graphical platforms in JMP to explore the data and with the click of a button, send a portion of the data to R for further analysis.
Another JMP feature that complements R is the add-in architecture, which is similar to how R packages work. If you’ve written a cool script or analysis workflow, you can package it into a JMP add-in file and send it to your colleagues so they can easily use it.
Ajay- What is the official view on R from your organization? Do you think it is a threat, or a complimentary product or another statistical platform that coexists with your offerings?
Kelci- Most definitely, we view R as complimentary. R contributors are providing a tremendous service to practitioners, allowing them to try a wide variety of methods in the pursuit of more insight and better results. The R community as a whole is providing a valued role to the greater analytical community by focusing attention on newer methods that hold the most promise in so many application areas. Data analysts should be encouraged to use the tools available to them in order to drive discovery and JMP can help with that by providing an analytic hub that supports both SAS and R integration.
Ajay- While you do use R, are there any plans to give back something to the R community in terms of your involvement and participation (say at useR events) or sponsoring contests.
Kelci- We are certainly open to participating in useR groups. At Predictive Analytics World in NY last October, they didn’t have a local useR group, but they did have a Predictive Analytics Meet-up group comprised of many R users. We were happy to sponsor this. Some of us within the JMP division have joined local R user groups, myself included. Given that some local R user groups have entertained topics like Excel and R, Python and R, databases and R, we would be happy to participate more fully here. I also hope to attend the useR! annual meeting later this year to gain more insight on how we can continue to provide tools to help both the JMP and R communities with their work.
We are also exploring options to sponsor contests and would invite participants to use their favorite tools, languages, etc. in pursuit of the best model. Statistics is about learning from data and this is how we make the world a better place.
About- Kelci Miclaus
Kelci is a research statistician developer for JMP Life Sciences at SAS Institute. She has a PhD in Statistics from North Carolina State University and has been using SAS products and R for several years. In addition to research interests in statistical genetics, clinical trials analysis, and multivariate analysis/visualization methods, Kelci works extensively with JMP, SAS, and R integration.
In part 3 of the series for predictions for 2012, here is Jill Dyche, Baseline Consulting/DataFlux.
Part 2 was Timo Elliot, SAP at http://www.decisionstats.com/timo-elliott-on-2012/ and Part 1 was Jim Kobielus, Forrester at http://www.decisionstats.com/jim-kobielus-on-2012/
Ajay: What are the top trends you saw happening in 2011?
Well, I hate to say I saw them coming, but I did. A lot of managers committed some pretty predictable mistakes in 2011. Here are a few we witnessed in 2011 live and up close:
1. In the spirit of “size matters,” data warehouse teams continued to trumpet the volumes of stored data on their enterprise data warehouses. But a peek under the covers of these warehouses reveals that the data isn’t integrated. Essentially this means a variety of heterogeneous virtual data marts co-located on a single server. Neat. Big. Maybe even worthy of a magazine article about how many petabytes you’ve got. But it’s not efficient, and hardly the example of data standardization and re-use that everyone expects from analytical platforms these days.
2. Development teams still didn’t factor data integration and provisioning into their project plans in 2011. So we saw multiple projects spawn duplicate efforts around data profiling, cleansing, and standardization, not to mention conflicting policies and business rules for the same information. Bummer, since IT managers should know better by now. The problem is that no one owns the problem. Which brings me to the next mistake…
3. No one’s accountable for data governance. Yeah, there’s a council. And they meet. And they talk. Sometimes there’s lunch. And then nothing happens because no one’s really rewarded—or penalized for that matter—on data quality improvements or new policies. And so the reports spewing from the data mart are still fraught and no one trusts the resulting decisions.
But all is not lost since we’re seeing some encouraging signs already in 2012. And yes, I’d classify some of them as bona-fide trends.
Ajay: What are some of those trends?
Job descriptions for data stewards, data architects, Chief Data Officers, and other information-enabling roles are becoming crisper, and the KPIs for these roles are becoming more specific. Data management organizations are being divorced from specific lines of business and from IT, becoming specialty organizations—okay, COEs if you must—in their own rights. The value proposition for master data management now includes not just the reconciliation of heterogeneous data elements but the support of key business strategies. And C-level executives are holding the data people accountable for improving speed to market and driving down costs—not just delivering cleaner data. In short, data is becoming a business enabler. Which, I have to just say editorially, is better late than never!
Ajay: Anything surprise you, Jill?
I have to say that Obama mentioning data management in his State of the Union speech was an unexpected but pretty powerful endorsement of the importance of information in both the private and public sector.
I’m also sort of surprised that data governance isn’t being driven more frequently by the need for internal and external privacy policies. Our clients are constantly asking us about how to tightly-couple privacy policies into their applications and data sources. The need to protect PCI data and other highly-sensitive data elements has made executives twitchy. But they’re still not linking that need to data governance.
I should also mention that I’ve been impressed with the people who call me who’ve had their “aha!” moment and realize that data transcends analytic systems. It’s operational, it’s pervasive, and it’s dynamic. I figured this epiphany would happen in a few years once data quality tools became a commodity (they’re far from it). But it’s happening now. And that’s good for all types of businesses.
Jill Dyché has written three books and numerous articles on the business value of information technology. She advises clients and executive teams on leveraging technology and information to enable strategic business initiatives. Last year her company Baseline Consulting was acquired by DataFlux Corporation, where she is currently Vice President of Thought Leadership. Find her blog posts on www.dataroundtable.com.