IRJET- Detection of SQL Injection using Machine Learning : A Survey

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 11 | Nov 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 239
Detection of SQL Injection using Machine Learning: A Survey
TareekPattewar
1
,HiteshPatil
2
,HarshadaPatil
3
,NehaPatil
4
,MuskanTaneja
5
,TusharWadile
6
1Assistant Professor, Dept. of Information Technology, R. C. Patel Institute of Technology, Maharashtra, India
2,3,4,5,6Student, Dept. of Information Technology, R. C. Patel Institute of Technology, Maharashtra, India
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Abstract - In today’s world, SQL Injection is a serious security threat over the internet for the various dynamic web
applications residing over the internet. The web-page that accept critical information from users store this information in the
form of online database. Web database is important because it’s one of the major ways businesses can access information that
isn’t generated by itself. Using SQL Injection, attackers could even gain unrestricted access to an entire database. The threats
include attacks such as Cross Side scripting (CSS), Denial of Service Attack (DoS) and Structured Query Language (SQL)
injection attack. SQL injection attack fall under top ten vulnerabilities. There are various machine learning algorithms used
for detection of SQL injection threats such as Naive Bias, Gradient Boosting and also various data encryption algorithm such as
MD5, AES and combination of both not only detect but also analyzethreats.
KeyWords: SQLInjection,CrossSideScripting,Denialof ServiceAttack,NaïveBias,GradientBoosting
1. INTRODUCTION
SQL injection is an attack technique that exploits a security vulnerability occurring in the database layer of an application.
Hackers use injections to obtain unauthorized access to the underlying data, structure, and DBMS. By an SQL injection
attacker can embed a malicious code in a poorly-designed application andthen passed to the back end database.Themalicious
data then produces database query results or actions that should never have been executed. By using an SQL Injection
vulnerability, given the right circumstances, an attacker can use it to bypass a web application’s authentication and
authorization mechanisms and retrieve the contents of an entire database.
SQL Injection can also be used to add, modify and delete records in a database, affecting data integrity. To such an extent, SQL
Injection can provide an attacker with unauthorized access to sensitive data.SQL injection is a code injection technique, used
to attack data-driven applications, in which malicious SQL statements are inserted into an entry field for execution (e.g. to
dumpthe database contents to the attacker). SQL injection must exploitasecurityvulnerabilityinanapplication’ssoftware, for
example, when user input is either incorrectly filtered for string literal escape characters embedded in SQL statements or
user input is not strongly typed and unexpectedlyexecuted.SQLinjectionismostlyknownas anattackvectorforwebsitesbut
canbeusedtoattackany typeof SQL database. SQL injectionattacks allowattackers to spoof identity, tamper with existing data,
cause repudiation issues such as voiding transactions or changing balances, allow the complete disclosure of all dataonthe
system,destroythedataormakeitotherwise unavailable,andbecomeadministratorsofthedatabase server.
A SQL injection attack consists of insertion or ”injection” of a SQL query via the input data from the client to the application.
A successful SQL injection exploit can read sensitive data from the database, modify database data (Insert/Update/Delete),
execute administration operations on the database (such as shutdowntheDBMS),recoverthecontentofagivenfile presenton
theDBMSfilesystemandinsomecasesissue commands to theoperatingsystem.SQLinjectionattacks area typeofinjectionattack,
inwhichSQLcommands areinjectedintodata-planeinputinordertoeffectthe executionof predefined SQLcommands.
Fig-1: SQL Injection [1]

An SQL injection is a computer attack in which malicious code is embedded in a poorly-designed application and then
passed to the backend database. The malicious data then producesdatabasequery results oractionsthatshouldneverhavebeen
executed.ASQL injection(SQLi)isatypeofsecurityexploitinwhichthe attacker adds Structured Query Language (SQL) code to a
Web form input box in order to gain access to unauthorized resources or make changes to sensitive data.
1.1 How and Why Is an SQL InjectionAttack Performed :
TomakeanSQLInjectionattack,anattackermustfirst find vulnerable user inputs within the web page or web application.A
webpageorwebapplicationthathasanSQL Injection vulnerability uses such userinput directlyin an SQL query. The attacker can
create input content. Such content is often called a malicious payload and is the key part of the attack.Aftertheattackersends
thiscontent,maliciousSQL commands are executed in the database.
SQL is a query language that was designed tomanage datastoredinrelationaldatabases.Youcanuseittoaccess, modify, and
delete data. Many web applications and websites store all the data in SQL databases. In some cases, you can also use SQL
commands to run operating system commands. Therefore, a successful SQL Injection attack can have very serious
consequences.
 Attackers can use SQL Injections to find the credentials of other users in the database. They can then
impersonate these users. The impersonated user may be a database administrator with all databaseprivileges.
 SQL lets you select and output data from the database. AnSQLInjectionvulnerability could allowtheattackerto
gaincompleteaccesstoall data in a databaseserver.
 SQLalsoletsyoualterdatainadatabaseandadd new data. For example, in a financial application, an attacker could
use SQL Injection to alter balances,voidtransactions,ortransfermoneyto their account.
 You can use SQL to delete records from a database, even drop tables. Even if the administrator makes database
backups, deletion ofdata couldaffectapplicationavailability until the database is restored. Also, backups may not
cover the most recentdata.
 In some database servers, you can access the operatingsystemusingthedatabaseserver.This maybeintentional
oraccidental.Insuchcase,an attackercoulduseanSQLInjectionastheinitial vector and then attack the internal
network behind a firewall.
1.2 SQLInjectionAttackProcess:
SQLIA is a hacking technique which the attacker adds SQL statements through a web application’s input fields or hidden
parameterstoaccesstoresources.Lackofinput validation in web applications causes hacker to be successful. For the following
examples we will assume that a web application receivesa HTTPrequestfroma clientas input and generates a SQL statementas
outputfor theback enddatabaseserver.Forexampleanadministratorwillbe authenticated after typing: employee id=112 and
password=admin. Figure1 describes a login by a malicious user exploiting SQL Injection vulnerability. Basically it is
structured in three phases:
1. anattackersendsthemaliciousHTTPrequestto the web application
2. creates the SQLstatement
3. submits the SQL statement to the back end database
Fig-2: SQL Injection Process [2]

The above SQL statement is always true because of the Boolean tautology we appended (OR 1=1) so, we will accesstothe
webapplicationasanadministratorwithout knowing the rightpassword.
1.3 Types of SQL Injection (SQLi):
SQL Injection can be used in a range of ways to cause serious problems.By levering SQLInjection,anattacker could bypass
authentication, access, modify and delete data within a database. In some cases, SQL Injection can even be used to execute
commands on the operating system, potentially allowing an attacker to escalate to more damaging attacks inside of a
network that sits behind a firewall. SQL Injection can be classified into three major categories – In-band SQLi,InferentialSQLi
andOut-of-band SQLi [7].
1. In-bandSQLi(ClassicSQLi):In-bandSQL Injectionisthemostcommonandeasy-to- exploitofSQLInjectionattacks.
In-bandSQL Injectionoccurswhenanattackerisableto use the same communication channel to both launch
the attack and gather results. The two most common types of in- band SQL Injection are Error-based SQLi
and Union-basedSQLi.
2. Error-based SQLi : Error-based SQLi is an in- band SQL Injection technique that relieson errormessagesthrown
by the databaseserver to obtain information about the structure of the database. In somecases, error-based
SQL injection alone is enough for an attacker to enumerate an entire database. While errors areveryuseful
duringthedevelopmentphase ofawebapplication,theyshouldbedisabledon a live site, or logged to a file with
restricted access instead.
3. Union-based SQLi : Union-based SQLi is an in-band SQL injection technique that leverages the UNION SQL
operatorto combine theresultsoftwoormoreSELECTstatements into a single result which is then returned as
part of the HTTP response.
4. Inferential SQLi (Blind SQLi):Inferential SQL Injection, unlike in-band SQLi, may take longer for an attacker to
exploit,however,itis just as dangerous as any other form of SQL Injection.InaninferentialSQLiattack,nodata
isactuallytransferredviathewebapplication and the attacker would not be able to see the result ofanattack
in-band (whichis why such attacks are commonly referred to as “blind SQL Injection attacks”). Instead, an
attacker is able to reconstruct the database structure by sending payloads, observing the web application’s
response and the resulting behaviorofthedatabaseserver.Thetwotypes of inferential SQL Injection are Blind-
boolean- basedSQLiandBlind-time-basedSQLi.
5. Boolean-based (content-based) Blind SQLi: Boolean-based SQL Injection is an inferential SQL Injection technique
that relies on sending an SQL query to the database which forces the application to return a different result
depending on whether the query returns a TRUE or FALSE result.Depending on the result, the content
within the HTTP response will change, or remain the same. This allows an attacker to infer if the payload
used returned true or false, even though no data from the database is returned. This attack is typicallyslow
(especiallyonlargedatabases) sinceanattackerwouldneedtoenumeratea database, character bycharacter.
6. Time-based Blind SQLi : Time-based SQL Injection is an inferential SQL Injection technique that relies on
sendinganSQL query to the database which forces the database to wait for a specified amount of time (in
seconds) before responding. The response timewillindicatetotheattackerwhetherthe result of the query is
TRUE or FALSE. Depending on the result, an HTTP response will be returned with a delay, or returned
immediately.Thisallowsanattackertoinferif thepayloadusedreturned trueorfalse, even thoughnodatafrom
thedatabaseisreturned. This attack is typically slow (especially on largedatabases) sinceanattackerwould
need to enumerate a database character by character.
7. Out-of-band SQLi: Out-of-band SQL Injection is not very common, mostly because it depends on features
being enabled on the database server being used by the web application. Out-of-band SQL Injection occurs
when an attacker is unable to use the same channel to launch the attack and gather results. Out-of-band
techniques, offer an attacker an alternative to inferential timebased techniques, especially if the server
responses are not very stable (making an inferential time-based attack unreliable).Out-of-band SQLi
techniques would rely on the database server’s ability to make DNS or HTTP requests to deliver data to an
attacker. Such is the case with Microsoft SQL Server’s xpdirtree command, which can be used to make DNS
requests to a server an attacker controls; as well as Oracle Database’s UTLHTTP package,which canbeusedto
sendHTTPrequestsfromSQL and PL/SQL to a server an attacker controls[2].

1.5 How SQL Injection Works:
Software developers create SQL queries to perform database functions within their applications. Each query has an
argument that ensures only desired records are returned when a user runs the query.In a SQL injection, attackers exploit this
argument by injecting malicious code into the input form. The first step of a SQL injection attack is to research how the targeted
database functions. This is done by submitting a variety of random values into the argument field to observe how the server
responds. The secondstepisusingthisinformationtocraftaninputvalue that the server will interpret and execute as a SQL
command.
For example, a database may store information about customers that have made a purchase with customer ID numbers.
InsteadofsearchingforaspecificcustomerID,an attackermay insert theargument “CustomerID = 1000 OR 1=1”intotheinputfield.
Since the statement 1=1 is always true, the SQL query would execute to return all available customer IDs and any
corresponding data.In addition to returning unauthorized information, SQL arguments could be written that delete an entire
database,bypasstheneed forcredentials,removerecords oraddunwanteddata.
2. LITERATURE SURVEY
1. D. RichardHippdesignedSQLiteinthespring of2000whileworkingforGeneralDynamics on contract with the United States
Navy. Hipp was designing software used for a damage- control system aboard guided missile destroyers, which
originallyusedHP-UXwith anIBMInformixdatabaseback-end.SQLite began as a Tclextension[5].
2. The design goals of SQLite were to allow the program to be operated without installing a database management
system or requiring a database administrator. Hipp based the syntax and semantics on those of PostgreSQL 6.5. In
August 2000, version 1.0 of SQLite was released, with storage based on gdbm (GNU Database Manager). SQLite 2.0
replaced gdbm with a custom B-tree implementation, adding transaction capability. SQLite 3.0, partially funded by
America Online, added internationalization, manifest typing, and other major improvements [2].
3. Huang and colleagues propose WAVES, a blackbox technique for testing web applications for SQL injection
vulnerabilities. The tool identify all points a web application that can be used to injectSQLIAs.Itbuildsattacksthattarget
these points and monitors the application how response to the attacks by utilize machine learning[4].
4. Atefeh Tajpour , Mohammad Sharifi et al., had found various tools related to SQL injection attack. They studied SQL
injection which is a type of attack where the attacker adds Structured Query Language code to a web form input box to
gain access or make changes to data. SQL injectionvulnerabilityallowsanattackertoflow commands directly to a web
application’s underlying database and destroy functionality or confidentiality. Researchers have proposed different
tools to detect and prevent this vulnerability. In this paper we present all SQL injection attack types and also current
tools whichcandetectorpreventtheseattacks.Finally we evaluate thesetools[4].
5. Dynamic Candidate Evaluations Approach, Bisht et al. propose CANDID. It is a Dynamic Candidate Evaluations method for
automatic prevention of SQL Injection attacks. This framework dynamically extracts the query structures from every
SQL query location which are intended by thedeveloper(programmer).Hence,itsolvesthe issue of manually modifying
the application to create theprepared statements [6].
6. Puspendra Kumar, R.K. Pateriya et al., found different SQL detection technique. SQLInjection poses a serious security
issueovertheInternetor over web application. In SQL injection attacks, hackerscantakeadvantageofpoorlycodedWeb
application software to introduce malicious code into the organization’s systems and network.The vulnerability exists
when a Web application do not properly filter or validate the entered databy a user on a Web page. Large Web
applications have hundreds of places where users can input data, each of which can provide a SQL injection
opportunity. Attacker can steal confidential data of the organization with these attacks resulting loss of market value of
the organization. They presents an effective survey of SQL Injection attack,detectionandpreventiontechniques[6].
7. Livshits et al. use static analysis techniques to detect vulnerabilities in software. Java Static Tainting uses
information flow techniques to detect when tainted input has been used to make a SQLIA. The primary limitation of
this approach is that it can detect only known patterns of SQLIAs and it can generate a relatively high amount of
false positives because it uses a conservative analysis.Static analysis, also called static code analysis, is a method of
computer program debugging that is done by examining the code without executing the program [2].
8. Xiang Fu et al. proposed the design of a static analysis framework, called SAFELI for identifying SQLIA
vulnerabilities at compile time. SAFELIstatically monitorthe MSIL (Microsoft Symbolic intermediate language) byte
code of an ASP.NET Web application, using symbolic execution. SAFELI can analyze the source code and will be able
to identify delicate vulnerabilities thatcannot bediscoveredby black-box vulnerability scanners.The main drawback

of this technique is that this approach can discover the SQL injection attacks onlyonMicrosoftbasedproduct[8].
9. Nguyen-Tuong et al. proposed a PHP interpreter to track precise per character taint information. A context sensitive
analysis is used to detect and reject queries if certain types of SQL tokens has been constructed by illegitimate input.
Limitation of these two approaches is that they require rewriting code [5].
10. Buehrer et al. used similar approach of comparing SQL Injection Detection Using Machine Learning 12 the actually
generated queries with the one that should have been generated (programmer intended). The only difference in this
approach is that it achieves the results by using ParseTrees.
3. METHODOLOGY
3.1 ALGORITHM:
SQL Injection can be considered as one of the most serious attacks, as it influences the database and can make serious
damage to your data and the whole system. For sure it can have more serious consequences than a Javascript Injection or
HTML Injection, as both of them are performed on the client-side. For comparison, with this attack, you can have accessto
the whole database. It should be mentioned, that to test against this attack, you should have quite good knowledge of SQL
programming language and in general, you should know how databases queries are working. Also while performing this
injectionattackyoushouldbemorecareful and observant, as any inaccuracy can be left as SQL vulnerabilities.
3.1.1 Machine LearningAlgorithm
The use of machine learning algorithms to detect and prevent various cyber security threats is being debated largely.
While the power of using supervised and unsupervised learning techniques to detect security threats cannot be questioned,
the computing resources and time required to execute such complex algorithms remains a major concern for the ever
advancing cyber security community. Tremendous research work has been done on using various machine learning
algorithms to detect SQL Injection attacks. There is no single perfect algorithm or technique in machine learning that can
be applied to a particular problem. A problem needs to be tested against various algorithms falling under classification or
regression techniques, and the results need to be compared, before finalizing a particular approach, for maximum accuracy.
SQL Injection detection using Na¨ıve Bayes algorithm has been implemented in previous researches. It use an approach
called GradientBoostingalgorithmto detect andpreventSQL Injection attacks. It also implemented the Na¨ıve Bayes algorithm
and compared the results against Gradient Boosting for this particular problem. It begin with an introduction to SQL
Injection attacks and the need and motivation to build a better SQL Injection detection system. It then understand the SQL
Injection attacks All the significant implementations so far provides enough literature review to learn from and improve
on the problem.
1. Na¨ıve Bayes 2. Gradient Boosting.
1. Naïve Bayes : Na¨ıve Bayes algorithm has already been implemented for detecting SQL Injections . Na¨ıve Bayes is a
classification model in supervised learning that is based on Bayes Theorem. The essence to Na¨ıve Bayes is that it
assumes that the presence of a feature in a data model is unrelated to the presence of other features. In short it
assumes that all the features in a data are conditionally independent of each other, hence it gets its name ‘Na¨ıve
Bayes’.
2. BaggingandBoosting:
Bagging - Bagging is an ensemble learning approach that predicts a value of data by using multiple supervised
learning models and then combining the results of all these individual learning models by a chosen technique. The
technique used for combining these results could be any including by weighting the results, taking their average,
voting for the maximum result, etc. Bagging is also called as Bootstrap Aggregation. Bagging can help with reducing
variance errors by using multiplesupervisedlearningmodelsand then combining their result. An example of bagging
technique is Random Forest Algorithm. In this approach, multipledecisiontreesarecreatedonrandomsubsetsof training
data and results are collected from each decision tree. A final result is then selected from these results by taking an
average of all the results from individual supervised learningmodels.
Boosting - Boosting on the other hand is an Ensemble learning approach that also uses multiple supervised learning
models in combination to provide better predictive results. The difference is the way in which boosting uses these
multiple models. Instead of using them in parallel, in boosting the multiple models are used sequentially. In this
technique each predictor model learns and tries to minimize the errors from the previous predictor model. Boosting
algorithms can reduce the bias errors introduced due to small size of datasets. An example of boosting algorithm is

Gradient Boosting.
Gradient Boosting - Gradient Boosting approach is used to classify and detect SQL Injection attacks. One of the
important reasons of choosing this approach is because not enough data is available to train the machine learning
models. Na¨ıve Bayes technique has been implemented to detect SQL Injection attacks because it can be trained even
onsmalldatasets. However, that can lead to high bias errors as it is possible that data will not be classified correctly all
the time.The hope is that using Gradient Boosting approach results in better accuracy while classifyingtheSQLInjection
queriesandoverallprovidesbetterresultsandhigherratioof detecting an SQL Injection attack.
3.1.2 EncryptionAlgorithm:
MD5 Algorithm:
MD5 algorithm was developed by Professor Ronald L. Rivest in 1991. According to RFC 1321, “MD5 message- digest
algorithm takes as input a message of arbitrary length and produces as output a 128-bit quot; fingerprintquot; or quot;
message digestquot; of the input. TheMD5algorithmisintendedtousefordigitalsignature applications,wherealargefilemust
be quot; compressed quot; in a secure manner before being encrypted with a private (secret) key under a public-key
cryptosystemsuch asRSA.” Theinput to theMD5algorithmisfirstdividedinto blocks of 512 bits each. And to record the length
of the original message, 64 bits are padded at the end of the message. Andsomebitsarepadded tothemessageif the length
of the message is less than 512 bits. After this each block is divided into 16 words of 32 bits which are denoted as M0. . .
..M15.MD5algorithmusesabuffer,tableandfour auxiliary functions for producing an output.
AES Algorithm:
Algorithm AES (Advanced Encryption Standard) is a symmetric encryption algorithm and was developed by two Belgian
cryptographer Joan Daemen and Vincent Rijmen.It wasdesignedtobeefficientinbothhardwareandsoftware, and supports a
block length of 128 bits and key lengths of 128,192,and256bits.Thisencryptionalgorithmisusedby
U.S. for securing the sensitive and unclassified material, so it issecureenoughwithhighsecurities.Thethreeblockciphers: AES-
128,AES-192and AES-256.Eachcipherencryptsand decryptsdatainblocksof128bitsusingcryptographickeysof 128-,192-
and 256-bits, respectively. And AES performs all its computations on bytes rather than bits. Hence, AES treats the input
plaintext of 128 bits block as 16bytes.
3.1.3 Dataset
Gathering a dataset for this problem was challenging as no datasets with public access to actual SQL Injection attacks that
were launchedareavailable. The recommendation is to always send SQL through Command structures, such as Prepared
Statements or Stored Procedures, rather than building up the SQL directly, which is open for SQL injection. This dataset
containsaroundSQLInjectionsofallthethree types, that are, Union Based, Error Based and Blind SQL Injections.
3.2 SQLInjectionDetectionTool:
As SQL injection attacks exploit vulnerable Web application and databasecode, the only way to prevent them is to resolve your
code39;svulnerabilities.Any place that code dynamically generates a SQL query usingdatafromanexternalsourceshouldbe
closely checked. On larger projects, you should look at using automatic source code scanning tools and Web vulnerability

scanners.
A good Web vulnerabilityscanner will spot common technical vulnerabilities, such as SQL injection flaws, cross-site scripting
vulnerabilities, parameter tampering, hidden field manipulation, backdoors, debug options and buffer overflows. If you use
any third-partyapplicationsthatutiliseadatabaseback- end, it39;s vital that that you follow any vendor updates regarding
vulnerabilities and patches to ensure the new code isn39;t introducing vulnerabilities into your own system. Even if your
database administrators and application developers arefollowing best practice,Iwould still recommend the deployment of an
application-layer firewall or Web application firewall (WAF). WAFs can provide protection beyond that of traditional
network firewalls and intrusion detection/prevention systems. Many, like those produced by Imperva Inc. and Barracuda
Networks Inc., can help prevent attacks suchasSQLinjection,cross-sitescriptingandothers that target flaws in application
logic or technical vulnerabilities insoftware.
Best-of-breed WAFs can recognise evasion techniques exploited by attackers using SQL injection, suchasobfuscatingtheattack
byencodingportionsof the injected command. Your chosen application-layer firewall should also allow you to create filters to
intercept, analyze or modify traffic specific to your network.
Filters make it easier to adapt the firewall to protect assets or monitor traffic specific to your network. Even better ifithas
the capability to quot;learnquot; what is and what isn39;t normal traffic for your specific network and adapt its behavior
accordingly. When irregularities are detected, the WAF can shut down potentialattacks while they39;re happening. Also as
SQL injection often takes place via the URL query string, you should regularly review your Web server39;s logs to look for
anomalousqueriesthatmay be injectionattempts.
3.3 SystemArchitecture:
The raw dataset consist of SQL queries. These queries are used for authentication bypass. There are various types of SQL
queries merged together to feed up the machine learning algorithm. Feature Extraction is done after merging SQL queries.
Feature Extraction involves reducing the numberof resources required todescribe a largeset ofdat. Labelling consist ofgroup
of samples that have been tagged with one or more labels. Classification is done with various algorithms of machine learning. It
classifies thedatasetasitis malicious or not.
Fig-3: System Architecture
4. CONCLUSION
SQL injection attack is a very serious problem of web applications. Finding the efficient solution of this problem is essential.
Researchershavedevelopedmanytechniques to detect and prevent this vulnerability. There is no appropriate solution that
can prevent all types of SQL injection attacks. SQL Injection attacks remain to be one of top concerns for cyber security
researchers.Signaturebased SQL Injection detection methods are no longer reliable as attackers are using new types of SQL
Injections each time. ThereisaneedforSQLInjectiondetectionmechanismsthat are capable of identifying new, never before
seen attacks. Applying machine learning to the field of cyber-security is being considered by many researchers. Two
machine learning classification algorithms are implemented on the problem, which are, Na¨ıve Bayes Classifier and Gradient
Boosting Classifier. Na¨ıve Bayes classifier machine learning modelprovidesresultswithanaccuracyof92.8%.Ensemble learning
methods are said to provide results with better accuracy as they implement multiple simple classifiers to improve error
and accuracy. Hence Gradient Boosting Classifier from ensemble learning is selected to be implementedontheSQLInjection
classificationproblem.

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