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Preview- Google Cloud SQL

From -http://code.google.com/apis/sql/

What is Google Cloud SQL?

Google Cloud SQL is web service that allows you to create, configure, and use relational databases with your App Engine applications. It is a fully-managed service that maintains, manages, and administers your databases, allowing you to focus on your applications and services.

By offering the capabilities of a MySQL database, the service enables you to easily move your data, applications, and services into and out of the cloud. This allows for high data portability and helps in faster time-to-market because you can quickly leverage your existing database (using JDBC and/or DB-API) in your App Engine application.

Here is where you can get an invite to the beta only Google Cloud SQL

Sign up for Limited Preview

Google Cloud SQL is available to a limited number of users. To sign up for the service:

  1. Visit the Google APIs Console. The console opens the All services pane.
  2. Find the SQL Service line in the Services table and click Request access…
  3. Fill out the enrollment form.
  4. Our team will review your enrollment information and respond by email to the address associated with your Google Account.
  5. Follow the link in the email to view the Terms of Service. Please read these carefully before accepting.
  6. Sign up for the google-cloud-sql-announce group to receive important announcements and product news. (NOTE- Members: 384)
and after all that violence and double talk, a walk in the clouds with SQL.
1. There are three kinds of instances in the beta view
2. Wait for the Instance to be created note- the Design of the Interface uptil now is much better than Amazon’s.  
Note you need to have an appspot application from Google Apps and can choose between the Python and Java versions. Quite clearly there is a play for other languages too. I think GO is also supported.
3. You can import your data from your Google Storage bucket
4. I am not that hot at coding or maybe the interface was too pretty. Anyways- the log tells me that import of the text file has failed from Google Storage to Google Cloud SQL 
5. Incidentally the Google Cloud Storage interface is also much better than the Amazon GUI for transferring data- Note I was using the classical statistical dataset Boston Housing Data as the test case. 
6. The SQL prompt is the weakest part of the design process of the Interphase. There is no Query builder and the SELECT FROM WHERE prompt is slightly amusing/ insulting . I mean guys either throw in a fully fledged GUI for query builder similar to the MYSQL Workbench , than create a pretty white command prompt.
7. You can also export your data back to your Google Storage bucket 
These are early days, and I am trying to see if there is a play for some cloud kind of ODBC action between R, Prediction API , and the cloud SQL… so try it out yourself at http://code.google.com/apis/sql/ and see if there is any juice you can build  here.

Google Cloud SQL

Another xing bang API from the boyz in Mountain View. (entry by invite only) But it is free and you can test your stuff on a MySQL db =10 GB

Database as a service ? (Maybe)— while Amazon was building fires (and Fire)

—————————————————————–

https://code.google.com/apis/sql/index.html

What is Google Cloud SQL?

Google Cloud SQL is a web service that provides a highly available, fully-managed, hosted SQL storage solution for your App Engine applications.

What are the benefits of using Google Cloud SQL?

You can access a familiar, highly available SQL database from your App Engine applications, without having to worry about provisioning, management, and integration with other Google services.

How much does Google Cloud SQL cost?

We will not be billing for this service in 2011. We will give you at least 30 days’ advance notice before we begin billing in the future. Other services such as Google App Engine, Google Cloud Storage etc. that you use with Google Cloud SQL may have their own payment terms, and you need to pay for them. Please consult their documentation for details.

Currently you are limited to the three instance sizes. What if I need to store more data or need better performance?

In the Limited Preview period, we only have three sizes available. If you have specific needs, we would like to hear from you on our google-cloud-sqldiscussion board.

When is Google Cloud SQL be out of Limited Preview?

We are working hard to make the service generally available.We don’t have a firm date that we can announce right now.

Do you support all the features of MySQL?

In general, Google Cloud SQL supports all the features of MySQL. The following are lists of all the unsupported features and notable differences that Google Cloud SQL has from MySQL.

Unsupported Features:

  • User defined functions
  • MySql replication

Unsupported MySQL statements:

  • LOAD DATA INFILE
  • SELECT ... INTO OUTFILE
  • SELECT ... INTO DUMPFILE
  • INSTALL PLUGIN .. SONAME ...
  • UNINSTALL PLUGIN
  • CREATE FUNCTION ... SONAME ...

Unsupported SQL Functions:

  • LOAD_FILE()

Notable Differences:

  • If you want to import databases with binary data into your Google Cloud SQL instance, you must use the --hex-blob option with mysqldump.Although this is not a required flag when you are using a local MySQL server instance and the MySQL command line, it is required if you want to import any databases with binary data into your Google Cloud SQL instance. For more information, see Importing Data.
How large a database can I use with Google Cloud SQL?
Currently, in this limited preview period, your database instance must be no larger than 10GB.
How can I be notified when there are any changes to Google Cloud SQL?
You can sign up for the sql-announcements forum where we post announcements and news about the Google Cloud SQL.
How can I cancel my Google Cloud SQL account?
To remove all data from your Google Cloud SQL account and disable the service:

  1. Delete all your data. You can remove your tables, databases, and indexes using the drop command. For more information, see SQL DROP statement.
  2. Deactivate the Google Cloud SQL by visiting the Services pane and clicking the On button next to Google Cloud SQL. The button changes from Onto Off.
How do I report a bug, request a feature, or ask a question?
You can report bugs and request a feature on our project page.You can ask a question in our discussion forum.

Getting Started

Can I use languages other than Java or Python?
Only Java and Python are supported for Google Cloud SQL.
Can I use Google Cloud SQL outside of Google App Engine?
The Limited Preview is primarily focused on giving Google App Engine customers the ability to use a familiar relational database environment. Currently, you cannot access Google Cloud SQL from outside Google App Engine.
What database engine are we using in the Google Cloud SQL?
MySql Version 5.1.59
Do I need to install a local version of MySQL to use the Development Server?
Yes.

Managing Your Instances

Do I need to use the Google APIs Console to use Google Cloud SQL?
Yes. For basic tasks like granting access control to applications, creating instances, and deleting instances, you need to use the Google APIs Console.
Can I import or export specific databases?
No, currently it is not possible to export specific databases. You can only export your entire instance.
Do I need a Google Cloud Storage account to import or export my instances?
Yes, you need to sign up for a Google Cloud Storage account or have access to a Google Cloud Storage account to import or export your instances. For more information, see Importing and Exporting Data.
If I delete my instance, can I reuse the instance name?
Yes, but not right away. The instance name is reserved for up to two months before it can be reused.

Tools & Resources

Can I use Django with Google Cloud SQL?
No, currently Google Cloud SQL is not compatible with Django.
What is the best tool to use for interacting with my instance?
There are a variety of tools available for Google Cloud SQL. For executing simple statements, you can use the SQL prompt. For executing more complicated tasks, you might want to use the command line tool. If you want to use a tool with a graphical interface, the SQuirrel SQL Client provides an interface you can use to interact with your instance.

Common Technical Questions

Should I use InnoDB for my tables?
Yes. InnoDB is the default storage engine in MySQL 5.5 and is also the recommended storage engine for Google Cloud SQL. If you do not need any features that require MyISAM, you should use InnoDB. You can convert your existing tables using the following SQL command, replacing tablename with the name of the table to convert:

ALTER tablename ENGINE = InnoDB;

If you have a mysqldump file where all your tables are in MyISAM format, you can convert them by piping the file through a sed script:

mysqldump --databases database_name [-u username -p  password] --hex-blob database_name | sed 's/ENGINE=MyISAM/ENGINE=InnoDB/g' > database_file.sql

Warning: You should not do this if your mysqldump file contains the mysql schema. Those files must remain in MyISAM.

Are there any size or QPS limits?
Yes, the following limits apply to Google Cloud SQL:

Resource Limits from External Requests Limits from Google App Engine
Queries Per Second (QPS) 5 QPS No limit
Maximum Request Size 16 MB
Maximum Response Size 16 MB

Google App Engine Limits

Google App Engine applications are also subject to additional Google App Engine quotas and limits. Requests from Google App Engine applications to Google Cloud SQL are subject to the following time limits:

  • All database requests must finish within the HTTP request timer, around 60 seconds.
  • Offline requests like cron tasks have a time limit of 10 minutes.
  • Backend requests to Google Cloud SQL have a time limit of 10 minutes.

App Engine-specific quotas and access limits are discussed on the Google App Engine Quotas page.

Should I use Google Cloud SQL with my non-High Replication App Engine application?
We recommend that you use Google Cloud SQL with High Replication App Engine applications. While you can use use Google Cloud SQL with applications that do not use high replication, doing so might impact performance.
Source-
https://code.google.com/apis/sql/faq.html#supportmysqlfeatures

Knowledge Discovery in Databases -KDD using PostgreSQL and #Rstats

Here is a small brief primer for beginners on configuring an open source database and using an open source analytics package.

All you need to know – is to read!

 

1. download PostgreSQL from
http://www.postgresql.org/download/windowsInstall PostgreSQL

Remember to store /memorize the password for the user postgres!

Create a connection using pgAdmin feature in Start Menu

2. download ODBC driver from
http://www.postgresql.org/ftp/odbc/versions/msi/
and the Win 64 edition from
http://wwwmaster.postgresql.org/download/mirrors-ftp/odbc/versions/msi/psqlodbc_09_00_0310-x64.zip

install ODBC driver

3. Go to

Start Menu\Control Panel\All Control Panel Items\Administrative Tools\Data Sources (ODBC)

4. Configure the following details in System DSN and  User DSN using the ADD tabs .Test connection to check if connection is working

5. Start R and install and load library RODBC

6. Use following initial code for R- if you know SQL you can  do the rest
> library(RODBC)

> odbcDataSources(type = c(“all”, “user”, “system”))
SQLServer              PostgreSQL30             PostgreSQL35W
“SQL Server”    “PostgreSQL ANSI(x64)” “PostgreSQL Unicode(x64)”

> ajay=odbcConnect(“PostgreSQL30″, uid = “postgres”, pwd = “XX”)

> sqlTables(ajay)
TABLE_QUALIFIER TABLE_OWNER TABLE_NAME TABLE_TYPE REMARKS
1        postgres      public      names      TABLE

> crimedat <- sqlFetch(ajay, “names”)

Using Google Fusion Tables from #rstats

But after all that- I was quite happy to see Google Fusion Tables within Google Docs. Databases as a service ? Not quite but still quite good, and lets see how it goes.

https://www.google.com/fusiontables/DataSource?dsrcid=implicit&hl=en_US&pli=1

http://googlesystem.blogspot.com/2011/09/fusion-tables-new-google-docs-app.html

 

But what interests me more is

http://code.google.com/apis/fusiontables/docs/developers_guide.html

The Google Fusion Tables API is a set of statements that you can use to search for and retrieve Google Fusion Tables data, insert new data, update existing data, and delete data. The API statements are sent to the Google Fusion Tables server using HTTP GET requests (for queries) and POST requests (for inserts, updates, and deletes) from a Web client application. The API is language agnostic: you can write your program in any language you prefer, as long as it provides some way to embed the API calls in HTTP requests.

The Google Fusion Tables API does not provide the mechanism for submitting the GET and POST requests. Typically, you will use an existing code library that provides such functionality; for example, the code libraries that have been developed for the Google GData API. You can also write your own code to implement GET and POST requests.

Also see http://code.google.com/apis/fusiontables/docs/sample_code.html

 

Google Fusion Tables API Sample Code

Libraries

SQL API

Language Library Public repository Samples
Python Fusion Tables Python Client Library fusion-tables-client-python/ Samples
PHP Fusion Tables PHP Client Library fusion-tables-client-php/ Samples

Featured Samples

An easy way to learn how to use an API can be to look at sample code. The table above provides links to some basic samples for each of the languages shown. This section highlights particularly interesting samples for the Fusion Tables API.

SQL API

Language Featured samples API version
cURL
  • Hello, cURLA simple example showing how to use curl to access Fusion Tables.
SQL API
Google Apps Script SQL API
Java
  • Hello, WorldA simple walkthrough that shows how the Google Fusion Tables API statements work.
  • OAuth example on fusion-tables-apiThe Google Fusion Tables team shows how OAuth authorization enables you to use the Google Fusion Tables API from a foreign web server with delegated authorization.
SQL API
Python
  • Docs List ExampleDemonstrates how to:
    • List tables
    • Set permissions on tables
    • Move a table to a folder
Docs List API
Android (Java)
  • Basic Sample ApplicationDemo application shows how to create a crowd-sourcing application that allows users to report potholes and save the data to a Fusion Table.
SQL API
JavaScript – FusionTablesLayer Using the FusionTablesLayer, you can display data on a Google Map

Also check out FusionTablesLayer Builder, which generates all the code necessary to include a Google Map with a Fusion Table Layer on your own website.

FusionTablesLayer, Google Maps API
JavaScript – Google Chart Tools Using the Google Chart Tools, you can request data from Fusion Tables to use in visualizations or to display directly in an HTML page. Note: responses are limited to 500 rows of data.

Google Chart Tools

External Resources

Google Fusion Tables is dedicated to providing code examples that illustrate typical uses, best practices, and really cool tricks. If you do something with the Google Fusion Tables API that you think would be interesting to others, please contact us at googletables-feedback@google.com about adding your code to our Examples page.

  • Shape EscapeA tool for uploading shape files to Fusion Tables.
  • GDALOGR Simple Feature Library has incorporated Fusion Tables as a supported format.
  • Arc2CloudArc2Earth has included support for upload to Fusion Tables via Arc2Cloud.
  • Java and Google App EngineODK Aggregate is an AppEngine application by the Open Data Kit team, uses Google Fusion Tables to store survey data that is collected through input forms on Android mobile phones. Notable code:
  • R packageAndrei Lopatenko has written an R interface to Fusion Tables so Fusion Tables can be used as the data store for R.
  • RubySimon Tokumine has written a Ruby gem for access to Fusion Tables from Ruby.

 

Updated-You can use Google Fusion Tables from within R from http://andrei.lopatenko.com/rstat/fusion-tables.R

 

ft.connect <- function(username, password) {
  url = "https://www.google.com/accounts/ClientLogin";
  params = list(Email = username, Passwd = password, accountType="GOOGLE", service= "fusiontables", source = "R_client_API")
 connection = postForm(uri = url, .params = params)
 if (length(grep("error", connection, ignore.case = TRUE))) {
 	stop("The wrong username or password")
 	return ("")
 }
 authn = strsplit(connection, "\nAuth=")[[c(1,2)]]
 auth = strsplit(authn, "\n")[[c(1,1)]]
 return (auth)
}

ft.disconnect <- function(connection) {
}

ft.executestatement <- function(auth, statement) {
      url = "http://tables.googlelabs.com/api/query"
      params = list( sql = statement)
      connection.string = paste("GoogleLogin auth=", auth, sep="")
      opts = list( httpheader = c("Authorization" = connection.string))
      result = postForm(uri = url, .params = params, .opts = opts)
      if (length(grep("<HTML>\n<HEAD>\n<TITLE>Parse error", result, ignore.case = TRUE))) {
      	stop(paste("incorrect sql statement:", statement))
      }
      return (result)
}

ft.showtables <- function(auth) {
   url = "http://tables.googlelabs.com/api/query"
   params = list( sql = "SHOW TABLES")
   connection.string = paste("GoogleLogin auth=", auth, sep="")
   opts = list( httpheader = c("Authorization" = connection.string))
   result = getForm(uri = url, .params = params, .opts = opts)
   tables = strsplit(result, "\n")
   tableid = c()
   tablename = c()
   for (i in 2:length(tables[[1]])) {
     	str = tables[[c(1,i)]]
   	    tnames = strsplit(str,",")
   	    tableid[i-1] = tnames[[c(1,1)]]
   	    tablename[i-1] = tnames[[c(1,2)]]
   	}
   	tables = data.frame( ids = tableid, names = tablename)
    return (tables)
}

ft.describetablebyid <- function(auth, tid) {
   url = "http://tables.googlelabs.com/api/query"
   params = list( sql = paste("DESCRIBE", tid))
   connection.string = paste("GoogleLogin auth=", auth, sep="")
   opts = list( httpheader = c("Authorization" = connection.string))
   result = getForm(uri = url, .params = params, .opts = opts)
   columns = strsplit(result,"\n")
   colid = c()
   colname = c()
   coltype = c()
   for (i in 2:length(columns[[1]])) {
     	str = columns[[c(1,i)]]
   	    cnames = strsplit(str,",")
   	    colid[i-1] = cnames[[c(1,1)]]
   	    colname[i-1] = cnames[[c(1,2)]]
   	    coltype[i-1] = cnames[[c(1,3)]]
   	}
   	cols = data.frame(ids = colid, names = colname, types = coltype)
    return (cols)
}

ft.describetable <- function (auth, table_name) {
   table_id = ft.idfromtablename(auth, table_name)
   result = ft.describetablebyid(auth, table_id)
   return (result)
}

ft.idfromtablename <- function(auth, table_name) {
    tables = ft.showtables(auth)
	tableid = tables$ids[tables$names == table_name]
	return (tableid)
}

ft.importdata <- function(auth, table_name) {
	tableid = ft.idfromtablename(auth, table_name)
	columns = ft.describetablebyid(auth, tableid)
	column_spec = ""
	for (i in 1:length(columns)) {
		column_spec = paste(column_spec, columns[i, 2])
		if (i < length(columns)) {
			column_spec = paste(column_spec, ",", sep="")
		}
	}
	mdata = matrix(columns$names,
	              nrow = 1, ncol = length(columns),
	              dimnames(list(c("dummy"), columns$names)), byrow=TRUE)
	select = paste("SELECT", column_spec)
	select = paste(select, "FROM")
	select = paste(select, tableid)
	result = ft.executestatement(auth, select)
    numcols = length(columns)
    rows = strsplit(result, "\n")
    for (i in 3:length(rows[[1]])) {
    	row = strsplit(rows[[c(1,i)]], ",")
    	mdata = rbind(mdata, row[[1]])
   	}
   	output.frame = data.frame(mdata[2:length(mdata[,1]), 1])
   	for (i in 2:ncol(mdata)) {
   		output.frame = cbind(output.frame, mdata[2:length(mdata[,i]),i])
   	}
   	colnames(output.frame) = columns$names
    return (output.frame)
}

quote_value <- function(value, to_quote = FALSE, quote = "'") {
	 ret_value = ""
     if (to_quote) {
     	ret_value = paste(quote, paste(value, quote, sep=""), sep="")
     } else {
     	ret_value = value
     }
     return (ret_value)
}

converttostring <- function(arr, separator = ", ", column_types) {
	con_string = ""
	for (i in 1:(length(arr) - 1)) {
		value = quote_value(arr[i], column_types[i] != "number")
		con_string = paste(con_string, value)
	    con_string = paste(con_string, separator, sep="")
	}

    if (length(arr) >= 1) {
    	value = quote_value(arr[length(arr)], column_types[length(arr)] != "NUMBER")
    	con_string = paste(con_string, value)
    }
}

ft.exportdata <- function(auth, input_frame, table_name, create_table) {
	if (create_table) {
       create.table = "CREATE TABLE "
       create.table = paste(create.table, table_name)
       create.table = paste(create.table, "(")
       cnames = colnames(input_frame)
       for (columnname in cnames) {
         create.table = paste(create.table, columnname)
    	 create.table = paste(create.table, ":string", sep="")
    	   if (columnname != cnames[length(cnames)]){
    		  create.table = paste(create.table, ",", sep="")
           }
       }
      create.table = paste(create.table, ")")
      result = ft.executestatement(auth, create.table)
    }
    if (length(input_frame[,1]) > 0) {
    	tableid = ft.idfromtablename(auth, table_name)
	    columns = ft.describetablebyid(auth, tableid)
	    column_spec = ""
	    for (i in 1:length(columns$names)) {
		   column_spec = paste(column_spec, columns[i, 2])
		   if (i < length(columns$names)) {
			  column_spec = paste(column_spec, ",", sep="")
		   }
	    }
    	insert_prefix = "INSERT INTO "
    	insert_prefix = paste(insert_prefix, tableid)
    	insert_prefix = paste(insert_prefix, "(")
    	insert_prefix = paste(insert_prefix, column_spec)
    	insert_prefix = paste(insert_prefix, ") values (")
    	insert_suffix = ");"
    	insert_sql_big = ""
    	for (i in 1:length(input_frame[,1])) {
    		data = unlist(input_frame[i,])
    		values = converttostring(data, column_types  = columns$types)
    		insert_sql = paste(insert_prefix, values)
    		insert_sql = paste(insert_sql, insert_suffix) ;
    		insert_sql_big = paste(insert_sql_big, insert_sql)
    		if (i %% 500 == 0) {
    			ft.executestatement(auth, insert_sql_big)
    			insert_sql_big = ""
    		}
    	}
        ft.executestatement(auth, insert_sql_big)
    }
}

Interview Eberhard Miethke and Dr. Mamdouh Refaat, Angoss Software

Here is an interview with Eberhard Miethke and Dr. Mamdouh Refaat, of Angoss Software. Angoss is a global leader in delivering business intelligence software and predictive analytics solutions that help businesses capitalize on their data by uncovering new opportunities to increase sales and profitability and to reduce risk. (more…)

Cloud Computing by Windows , Amazon and Google for free

Some ways to test and use cloud computing for free for yourself-

  1. Windows Azure
  2. Amazon Ec2
  3. Google Storage

The folks at Microsoft Azure announced a 90 day free trial (more…)

Top 25 Errors in Programming that lead to hacker attacks

I am elaborating an earlier article on http://decisionstats.com/top-25-most-dangerous-software-errors/ based on my continued research into cyber conflict and strategy. My inputs are in italics – the rest is a condensed article for further thought.

This is thus a very useful initiative for the world to follow and upgrade their cyber security.

It is in accordance with the US policy to secure its cyber infrastructure (http://www.whitehouse.gov/the-press-office/remarks-president-securing-our-nations-cyber-infrastructure)  and countries like India, and even Europe as well as other nations could do well to atleast benchmark their own security practices in software and digital infrastructure with it. There seems to much better technical coordination between rogue hackers than patriotic hackers imho ;)


The Department of Homeland Security of the United States of America has just launched a list of top 25 errors in programming or creating software that increase vulnerability to hacking attacks. The list which is available at http://cwe.mitre.org/top25/index.html lists down a methodology fo measuring vulnerability called Common Weakness Scoring System (CWSS) and uses that score to rank the various errors as well as suggestions to eliminate these weaknesses or errors.
Measuring Weaknesses

The importance of a weakness (that arises due to software bugs) may vary depending on business usage or project implementation, the technologies , operating systems and computing environments in use, and the risk or threat perception.The Common Weakness Scoring System (CWSS) provides a mechanism for scoring weaknesses. and provides a framework for prioritizing security errors (“weaknesses”) that are discovered in software applications.
Identifying Weaknesses
For example the number 1 weakness is shown with
1CWE-89: Improper Neutralization of Special Elements used in an SQL Command (‘SQL Injection’).
The rest of the weaknesses are

RANK SCORE ID NAME
[1] 93.8 CWE-89 Improper Neutralization of Special Elements used in an SQL Command (‘SQL Injection’)
[2] 83.3 CWE-78 Improper Neutralization of Special Elements used in an OS Command (‘OS Command Injection’)
[3] 79.0 CWE-120 Buffer Copy without Checking Size of Input (‘Classic Buffer Overflow’)
[4] 77.7 CWE-79 Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’)
[5] 76.9 CWE-306 Missing Authentication for Critical Function
[6] 76.8 CWE-862 Missing Authorization
[7] 75.0 CWE-798 Use of Hard-coded Credentials
[8] 75.0 CWE-311 Missing Encryption of Sensitive Data
[9] 74.0 CWE-434 Unrestricted Upload of File with Dangerous Type
[10] 73.8 CWE-807 Reliance on Untrusted Inputs in a Security Decision
[11] 73.1 CWE-250 Execution with Unnecessary Privileges
[12] 70.1 CWE-352 Cross-Site Request Forgery (CSRF)
[13] 69.3 CWE-22 Improper Limitation of a Pathname to a Restricted Directory (‘Path Traversal’)
[14] 68.5 CWE-494 Download of Code Without Integrity Check
[15] 67.8 CWE-863 Incorrect Authorization
[16] 66.0 CWE-829 Inclusion of Functionality from Untrusted Control Sphere
[17] 65.5 CWE-732 Incorrect Permission Assignment for Critical Resource
[18] 64.6 CWE-676 Use of Potentially Dangerous Function
[19] 64.1 CWE-327 Use of a Broken or Risky Cryptographic Algorithm
[20] 62.4 CWE-131 Incorrect Calculation of Buffer Size
[21] 61.5 CWE-307 Improper Restriction of Excessive Authentication Attempts
[22] 61.1 CWE-601 URL Redirection to Untrusted Site (‘Open Redirect’)
[23] 61.0 CWE-134 Uncontrolled Format String
[24] 60.3 CWE-190 Integer Overflow or Wraparound
[25] 59.9 CWE-759 Use of a One-Way Hash without a Salt
Details of each weakness is given by http://cwe.mitre.org/top25/index.html#Details
It includes Summary , Weakness Prevalence, Consequences, Remediation Cost, Ease of Detection ,Attacker Awareness and Attack Frequency .In addition the following sections describe each software vulnerability in detail- Technical Details ,Code Examples ,Detection Methods ,References,Prevention and Mitigation, Related CWEs and Related Attack Patterns.
Other important software weaknesses are -

[26] CWE-770: Allocation of Resources Without Limits or Throttling
[27] CWE-129: Improper Validation of Array Index
[28] CWE-754: Improper Check for Unusual or Exceptional Conditions
[29] CWE-805: Buffer Access with Incorrect Length Value
[30] CWE-838: Inappropriate Encoding for Output Context
[31] CWE-330: Use of Insufficiently Random Values
[32] CWE-822: Untrusted Pointer Dereference
[33] CWE-362: Concurrent Execution using Shared Resource with Improper Synchronization (‘Race Condition’)
[34] CWE-212: Improper Cross-boundary Removal of Sensitive Data
[35] CWE-681: Incorrect Conversion between Numeric Types
[36] CWE-476: NULL Pointer Dereference
[37] CWE-841: Improper Enforcement of Behavioral Workflow
[38] CWE-772: Missing Release of Resource after Effective Lifetime
[39] CWE-209: Information Exposure Through an Error Message
[40] CWE-825: Expired Pointer Dereference
[41] CWE-456: Missing Initialization
Mitigating Weaknesses
Here is an example of the new matrix for migrations that also list the top 25 errors . This thus shows a way to fix the weaknesses and relative impact on each weakness by the following mitigations.

http://cwe.mitre.org/top25/mitigations.html#MitigationMatrix

Effectiveness ratings include:

  • High: The mitigation has well-known, well-understood strengths and limitations; there is good coverage with respect to variations of the weakness.
  • Moderate: The mitigation will prevent the weakness in multiple forms, but it does not have complete coverage of the weakness.
  • Limited: The mitigation may be useful in limited circumstances, only be applicable to a subset of this weakness type, require extensive training/customization, or give limited visibility.
  • Defense in Depth (DiD): The mitigation may not necessarily prevent the weakness, but it may help to minimize the potential impact when an attacker exploits the weakness.

Within the matrix, the following mitigations are identified:

 

  • M1: Establish and maintain control over all of your inputs.
  • M2: Establish and maintain control over all of your outputs.
  • M3: Lock down your environment.
  • M4: Assume that external components can be subverted, and your code can be read by anyone.
  • M5: Use industry-accepted security features instead of inventing your own.

The following general practices are omitted from the matrix:

  • GP1: Use libraries and frameworks that make it easier to avoid introducing weaknesses.
  • GP2: Integrate security into the entire software development lifecycle.
  • GP3: Use a broad mix of methods to comprehensively find and prevent weaknesses.
  • GP4: Allow locked-down clients to interact with your software.

 

M1 M2 M3 M4 M5 CWE
High DiD Mod CWE-22: Improper Limitation of a Pathname to a Restricted Directory (‘Path Traversal’)
Mod High DiD Ltd CWE-78: Improper Neutralization of Special Elements used in an OS Command (‘OS Command Injection’)
Mod High Ltd CWE-79: Improper Neutralization of Input During Web Page Generation (‘Cross-site Scripting’)
Mod High DiD Ltd CWE-89: Improper Neutralization of Special Elements used in an SQL Command (‘SQL Injection’)
Mod DiD Ltd CWE-120: Buffer Copy without Checking Size of Input (‘Classic Buffer Overflow’)
Mod DiD Ltd CWE-131: Incorrect Calculation of Buffer Size
High DiD Mod CWE-134: Uncontrolled Format String
Mod DiD Ltd CWE-190: Integer Overflow or Wraparound
High CWE-250: Execution with Unnecessary Privileges
Mod Mod CWE-306: Missing Authentication for Critical Function
Mod CWE-307: Improper Restriction of Excessive Authentication Attempts
DiD CWE-311: Missing Encryption of Sensitive Data
High CWE-327: Use of a Broken or Risky Cryptographic Algorithm
Ltd CWE-352: Cross-Site Request Forgery (CSRF)
Mod DiD Mod CWE-434: Unrestricted Upload of File with Dangerous Type
DiD CWE-494: Download of Code Without Integrity Check
Mod Mod Ltd CWE-601: URL Redirection to Untrusted Site (‘Open Redirect’)
Mod High DiD CWE-676: Use of Potentially Dangerous Function
Ltd DiD Mod CWE-732: Incorrect Permission Assignment for Critical Resource
High CWE-759: Use of a One-Way Hash without a Salt
DiD High Mod CWE-798: Use of Hard-coded Credentials
Mod DiD Mod Mod CWE-807: Reliance on Untrusted Inputs in a Security Decision
High High High CWE-829: Inclusion of Functionality from Untrusted Control Sphere
DiD Mod Mod CWE-862: Missing Authorization
DiD Mod CWE-863: Incorrect Authorization
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