Sql prevention techniques

SQL prevention techniques
Byte Code Review
This method seeks to attack probable causes of SQL injection at the very root of the application program. In the java byte code of the application by specifying the vulnerabilities in PQL. PQL is a program query language which they use to define the likely vulnerabilities using a java-like syntax.
Parameterized Queries
These attempt to prevent SQL Injection by allowing the application developer to more accurately specify the structure of an SQL query, and pass the value parameters to it separately such that any unsanitary user-input is not allowed to modify the query structure. While the most popular of these, PrepareStatement in Java and SQLParameter C#, has been built into main-stream application development languages and is in use, the other proposals attempt to provide a less languagespecific parameterization method. Prepared Statements are a parameterized database access API provided by the development platform, including PrepareStatement in Java and SQLParameter for .NET. Instead of composing SQL by concatenating strings, each parameter in a SQL query is declared using a place holder and the input is provided separately.
Rather than treat SQL database connectivity through a call level interface such as JDBC, SQL DOM is an attempt to move database connectivity to a set of classes in strong correlation with the database schema, and generate SQL statements using those classes rather than string manipulation.
Anomaly-Based SQL Injection Detection and Prevention
This approach defines a normal behavior pattern and looks for any deviations from this behavior in order to classify it as an attack or intrusion. The approach consists of three stages : parametrization, training and detection where the good traffic needs to be defined, the system trained to detect abnormal traffic and the process of detection and prevention of the intrusion. Further, it can be classified into three types: Statistical, Knowledge-based and machine-learning based methods
• Statistical: A specification based approach, where a model is generated based on a set of profiling rules for SQL Statements. Then, the SQL statements are intercepted by the model, and only if the query lexically and syntactically matches the model is it declared valid.
• Machine Learning-Based: IT uses a parse tree for the validation of query. In this technique a parse tree as a model and every query entering the database is checked against that tree. All queries found at odds with the parse tree were deemed malicious.
Static-analysis of code is often used only for Testing for SQL Injection Attacks. However, AMNESIA use it for detection/prevention. The approach is based on the idea that web-application code implicitly distinguishes good and bad queries based on the way the SQL query is constructed. AMNESIA, implemented for java server pages, generates a static NDFA model through string analysis techniques on the application source code, and then during runtime checks all generated queries for conformance with the statically built model. All queries that do not match the model are identified as SQL injection attacks, blocked and reported. The problem with AMNESIA is it does not scale well, expensive and language specific.
Detection by Feature of Single Character:
This technique use sigmoid function for detecting SQL injection attacks. This detection algorithm of SQL injection attack is based on single character. When the SQL character string is the SQL Injection, it call an attack character string. This approach minimizes the predictive error in SQL injection attack detection.
This tool is useful for identifYing the SQL Injection attacks and XSS vulnerabilities. This technique works on unmodified existing code, generate concrete input that expose vulnerabilities and operate before software is deployed. ARDILLA is an automated tool for creating attacks. It is white box testing tool means that it requires source code of the application. It is based on the input generation, taint propagation and input mutation to find variants of an execution that exploit vulnerability.
Web hardening:
This provides automated solution for detecting and preventing SQL Injection attacks without developer interaction. And it uses modified interpreter which identifies SQL Injection attacks which is tainted with user supplied input. It also prevent the generation of scripting code with the aid of un trusted input.
WASP (Web Applications Using Positive Tainting and Syntax-Aware Evaluation) solution which works based on the dynamic tainting. Their approach is the improved version of classic tainting. The first improvement is the using of positive tainting which is based on making trust. Classic tainting is based on untrusted data. Next improvement is the accuracy and efficiency of the tainting by tracing of trust making at character level. Third improvement is the blocking of queries which contains SQL keywords and operators without trust making. The last one is the minimal implementation requirements which make this method practical.
A technique for SQL injection prevention which use randomized SQL query to detect malicious statements and abort them. For this purpose they made randomized instances of the SQL query by randomizing the template query inside the CGI script and the database parser. For example the SELECT keyword will replace by SELECT921. SELECT921 is a random name which generated for the current execution. Later the developer by using a proxy will intercept the traffic between the application and the database, and if any keywords without randomization found that is a SQL injection. In result attacker cannot do the SQL injection without knowing the random key. The positive point about this solution is that it will not affect the performance.
String Analyzer
This proposed a grammar based algorithm which model the string values as context free grammars and string operations as language transducers following Minamide. This solution labels those strings that come from the user side as nonterminal. It will assign the “direct label” to those strings that are come directly from the user side such as GET requests. And assign “indirect label” to strings that are come from database side. Next they summarize the labeled strings to find the contexts and afterward by using regular languages and context free languages check the security of each string in aspect of syntax.
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