Detection Of SQL Injection Attacks Research Articles

A Hybrid Technique for SQL Injection Attacks Detection and Prevention

SQL injection is a type of attacks used to gain, manipulate, or delete information in any data-driven system whether this system is online or offline and whether this system is a web or non-web-based. It is distinguished by the multiplicity of its performing methods, so defense techniques could not detect or prevent such attacks. The main objective of this paper is to create a reliable and accurate hybrid technique that secure systems from being exploited by SQL injection attacks. This hybrid technique combines static and runtime SQL queries analysis to create a defense strategy that can detect and prevent various types of SQL injection attacks. To evaluate this suggested technique, a large set of SQL queries have been executed through a simulation that had been developed. The results indicate that the suggested technique is reliable and more effective in capturing more SQL injection types compared to other SQL injection detection methods.

A Hybrid Technique for SQL Injection Attacks Detection and Prevention

SQL injection is a type of attacks used to gain, manipulate, or delete information in any data-driven system whether this system is online or offline and whether this system is a web or non -web -based. It is distinguished by the multiplicity of its performing methods, so defense techniques could not detect or prevent such attacks. The main objective of this paper is to create a reliable and accurate hybrid technique that secure systems from being exploited by SQL injection attacks. This hybrid technique combines static and runtime SQL queries analysis to create a defense strategy that can detect and prevent various types of SQL injection attacks. To evaluate this suggested technique, a large set of SQL queries have been executed through a simulation that had been developed. The results indicate that the suggested technique is reliable and more effective in capturing more SQL injection types compared to other SQL injection detection methods.

A Survey of SQL Injection Attack Detection and Prevention

Structured Query Language Injection Attack (SQLIA) is the most exposed to attack on the Internet. From this attack, the attacker can take control of the database therefore be able to interpolate the data from the database server for the website. Hence, the big challenge became to secure such website against attack via the Internet. We have presented different types of attack methods and prevention techniques of SQLIA which were used to aid the design and implementation of our model. In the paper, work is separated into two parts. The first aims to put SQLIA into perspective by outlining some of the materials and researches that have already been completed. The section suggesting methods of mitigating SQLIA aims to clarify some misconceptions about SQLIA prevention and provides some useful tips to software developers and database administrators. The second details the creation of a filtering proxy server used to prevent a SQL injection attack and analyses the performance impact of the filtering process on web application.

A Simple and Fast Technique for Detection and Prevention of SQL Injection Attacks (SQLIAs)

In SQLIA, attacker injects an input in the query in order to change the structure of the query intended by the programmer and therefore, gain access to the data in the underlying database. Due to the significance of the stored data, web application’s security against SQLIA is vital. In this paper we propose a new technique based on static analysis and runtime validation for detection and prevention of SQLIAs. In this technique user inputs in SQL queries are removed and some information is gathered in order to make the detection easier and faster at runtime. Our experiments show that our proposed technique is fast, it has a low error rate and its detection rate is nearly 100%.

Early Detection of SQL Injection Attacks

SQL Injection (SQLI) is a common vulnerability found in web applications . The starting point of SQLI attack is the client-side (browser). If attack inputs can be detected early at the browse side, then it could be thwarted early by not forwarding th e malicious inputs to the server-side for further processing.This paper presents a client-side approach to detect SQLI attacks 1 . The client-side accepts shadow SQL queries from the server-side and checks any deviation betweenshadow queries with dynamic q ueries generated with user supplied inputs. We measure the deviation of shadow query and dynamic query based on conditional entropy metrics and propose four metrics in this direction. We evaluate the approach with three PHP applications containing SQLI vul nerabilities. The evaluation results indicate that our approach can detect well-known SQLI attacks early at the client -side and impose negligible overhead.

The Multi-Tier Architecture for Developing Secure Website with Detection and Prevention of SQL-Injection Attacks

injection is an attack methodology that targets the data residing in a database. The attack takes advantage of poor input validation in code and website administration. SQL Injection Attacks occur when an attacker is able to insert a series of SQL statements into a 'query' by manipulating user input data into a web-based application, an attacker can take advantages of web application programming security flaws and pass unexpected malicious SQL statements through a web application for execution by the back-end database. This paper proposes a novel specification-based methodology for the prevention of SQL injection Attacks. The two most important advantages of the new approach against existing analogous mechanisms are that, first, it prevents all forms of SQL injection attacks; second, Current technique does not allow the user to access database directly from the database server. Our proposed framework for building secure and anti-theft web applications is consisting of four stages. In each stage we analyze the inputted data taken from the user and make a decision, whether that is suspected or not.

Method to Detect SQL Injection Attacks for Complex Network Environment

SQL injection has become a serious security risk among all the attacks against Web application. The SQL injection attack allows an attacker to access the underlying database unrestrictedly, and furthermore, retrieves the confidential information of the corporation and the network user. We found that most of the existing researches are able to detect most of the attacks, but they do not consider the complexity involved in using the defense system and the eventual cost of modification of the original program. For this reason, we conducts an in-depth research on SQL injection and defense: requires no modification of the web application code,and can be adapted to different usage scenarios,involving also different operating systems and server applications,and can be able to detect all the known injection points for the test application

Rule based Detection of SQL Injection Attack

This paper presents an effective detection method RDUD for SQL injection attack. RDUD is an enhanced version of DUD (1). The method comprises a supervised machine learning approach using a Support Vector Machine(SVM) to learn and to classify a query at runtime. Two web profiles - (i) legitimate web profile and (ii) attack web profile are generated for each of the web-application software which consists of a set of production rules extracted from the dynamic SQL queries. Both the web profiles are generated during training phase. At runtime a dynamic SQL query is matched with each of the web profile and accordingly it classify based on the matching distance. RDUD is independent of the developers initialization of syntax rules, valid trusted string database, static or pre-generated program code checking, etc. Also the method is significant in view of its simplicity, efficient and its high detection rate in comparison to the earlier method (1).

SQL injection attack Detection using SVM

Web application has various input functions which are susceptible to SQL-Injection attack. SQL-Injection occurs by injecting suspicious code or data fragments in a web application.Personal information disclosure ,loss of authenticity, data theft and site fishing falls under this attack category. It is impossible to check original data code and suspicious data code using available algorithms and approaches because of inefficient and proper training techniques of dataset or design aspects. In this paper we will use SVM (Support Vector Machine) for classification and prediction of SQL-Injection attack. In our propose algorithm, SQL-Injection attack detection accuracy is (96.47% and which is the highest among the existing SQL-Injection detection Techniques.

An Efficient Technique for Detection and Prevention of SQL Injection Attack using ASCII Based String Matching

With the rise of the Internet, web applications, such as online banking and web-based email, have become integral to many people's daily lives. Web applications have brought with them new classes of computer security vulnerabilities, such as SQL injection. It is a class of input validation based vulnerabilities. Typical uses of SQL injection leak confidential information from a database, by-pass authentication logic, or add unauthorized accounts to a database. This security prevents the unauthorized access to your database and also it prevents your data from being altered or deleted by users without the appropriate permissions. Malicious Text Detector, Constraint Validation, Query length validation and Text based Key Generator are the four types of filtration technique used to detect and prevent the SQL Injection Attacks from accessing the database

IdMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining

This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paper.

A novel method for SQL injection attack detection based on removing SQL query attribute values

SQL injection or SQL insertion attack is a code injection technique that exploits a security vulnerability occurring in the database layer of an application and a service. This is most often found within web pages with dynamic content. This paper proposes a very simple and effective detection method for SQL injection attacks. The method removes the value of an SQL query attribute of web pages when parameters are submitted and then compares it with a predetermined one. This method uses combined static and dynamic analysis. The experiments show that the proposed method is very effective and simple than any other methods.

Retrofitting Existing Web Applications with Effective Dynamic Protection Against SQL Injection Attacks

This article presents an approach for retrofitting existing Web applications with run-time protection against known, as well as unseen, SQL injection attacks (SQLIAs) without the involvement of application developers. The precision of the approach is also enhanced with a method for reducing the rate of false positives in the SQLIA detection logic, via runtime discovery of the developers’ intention for individual SQL statements made by Web applications. The proposed approach is implemented in the form of protection mechanisms for J2EE, ASP.NET, and ASP applications. Named SQLPrevent, these mechanisms intercept HTTP requests and SQL statements, mark and track parameter values originating from HTTP requests, and perform SQLIA detection and prevention on the intercepted SQL statements. The AMNESIA testbed is extended to contain false-positive testing traces, and is used to evaluate SQLPrevent. In our experiments, SQLPrevent produced no false positives or false negatives, and imposed a maximum 3.6% performance overhead with 30 milliseconds response time for the tested applications.