SQL Injection Vulnerabilities Research Articles

Cyber Astra (Cyber Security Threats Detection)

Cyber security is a critical issue in today's interconnected digital environment where organizations face increasing threats. This content demonstrates integrated threat detection system, user-friendly network application designed to enhance the security of the organization. The system includes advanced tools and features such as Dark Web Infiltration Finder, Breach Tracker, Email Leak Finder, Nmap Scanner for IP Address and Network Scanning, SQL Injection Detector, Clickjacking Detector and Specially Designed Search Engine for Penetration Testers Use Google Dork for discovery. Main Features: 1. Dark Web Leak Finder: - Track organizational leaks on the Dark Web. - Provide instant updates on compromised credentials or sensitive information. Track crime tracker: - See crime history on maps and your organization. - Increase protection by identifying potential patterns and trends in cyber threats. It also works as an email leak finder: - Search public and private sites to determine the status of corporate email leaks. -Example has been updated to take steps to prevent tampering of email addresses and unauthorized access. 2. Nmap Scanner: - Fully scan IP addresses and networks to identify vulnerabilities. -it tells us which port is open or closed, and also tells us which service is running on which port. - Use to strengthen and monitor network security. 3. SQL injection detection: - Check URLs for potential SQL injection vulnerabilities. - Provide warnings and recommendations to reduce risks associated with SQL injection attacks. 4. Clickjacking Detector: - detects and blocks clickjacking attempts in web applications. - Improved user interface to check for clickjacking vulnerabilities on the client or user's website. 5.Search engine for Penetration Testers: - Includes advanced Google Dork technology for targeted reconnaissance. - Provide auditors with a useful search engine to identify vulnerabilities.

Web Based Intrusion Detection System for SQLIA

SQL Injection Attack (SQLIA) refers to an injection attack wherein an attacker can execute malicious SQL statements that control a web application’s database server. By leveraging 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. The main idea of our work is to allow developers the freedom to write and execute code without having to worry about these attacks. In this paper we propose a Web Based Intrusion Detection System for SQLIA to extract a SQL query connecting to database from a PHP file. The structure of the query under observation will be converted to XML file and compared against the legitimate queries stored in the XML file using association rule mining thus minimizing attacks. WEBIDS is expected to reduce the time and manual effort as it only focuses on fragments that are vulnerable for attacks. Key Words: XML Rule Mining, PHP, SQL injection,

A Static Detection Method for SQL Injection Vulnerability Based on Program Transformation

Static analysis is popular for detecting SQL injection vulnerabilities. However, due to the lack of accurate modeling of object-oriented database extensions, current methods fail to accurately detect SQL injection vulnerabilities in applications that use object-oriented database extensions. We propose a program transformation-based SQL injection vulnerability detection method to address this issue. This method consists of two stages: program transformation and vulnerability detection. In the first stage, object-oriented database extensions are automatically transformed into semantically equivalent procedural database extensions through the identification of key statements, call relation verification, and program transformation. In the second stage, application programs are automatically scanned using a combination of control flow graph construction and taint analysis techniques to detect SQL injection vulnerabilities. Based on the proposed method, we have implemented the OODBE-SCAN prototype system and performed experimental analysis on eight modern PHP applications. We compare OODBE-SCAN with two related static analysis tools, RIPS and Seay. The results show that OODBE-SCAN can detect more real-world vulnerabilities and has higher accuracy than existing methods.

Simulating all archetypes of SQL injection vulnerability exploitation using reinforcement learning agents

Vulnerabilities such as SQL injection represent a serious challenge to security. While tools with a pre-defined logic are commonly used in the field of penetration testing, the continually evolving nature of the security challenge calls for models able to learn autonomously from experience. In this paper we build on previous results on the development of reinforcement learning models devised to exploit specific forms of SQL injection, and we design agents that are able to tackle a varied range of SQL injection vulnerabilities, virtually comprising all the archetypes normally considered by experts. We show that our agents, trained on a synthetic environment, perform a transfer of learning among the different SQL injections challenges; in particular, they learn to use their queries to efficiently gain knowledge about multiple vulnerabilities at once. We also introduce a novel and more versatile way to interpret server messages that reduces reliance on expert inputs. Our simulations show the feasibility of our approach which easily deals with a number of homogeneous challenges, as well as some of its limitations when presented with problems having higher degrees of uncertainty.

DETECTION OF SQL INJECTION VULNERABILITY IN CODEIGNITER FRAMEWORK USING STATIC ANALYSIS

SQL Injection attacks are still one type of attack that often occurs in web-based applications. The causes and ways to prevent SQL Injection have been widely explained in various sources. Unfortunately, until now, SQL Injection vulnerabilities are still often found in multiple applications. Web-based application frameworks that already have functions to protect against attacks are often not used optimally. This is inseparable from the role of programmers, who often forget the rules for writing program code to prevent SQL Injection attacks. We conducted this research to detect SQL Injection vulnerabilities in source code using a case study of the PHP CodeIgniter framework. We compared this research with static analysis tools like RIPS, Synopsys Coverity, and Sonarqube. The tool we have developed can detect SQL Injection vulnerabilities that cannot be detected by the two tools with an accuracy of 88.8%. The results of our research can provide suggestions for programmers so that they can improve the code they write.

Vulnerability Detection With K-Nearest Neighbor and Naïve Bayes Method using Machine Learning

In this day and age, the use of the Internet has increased. SQL injection is a serious security threat on the Internet for various dynamic websites. As the use of the Internet for various online services increases, so make the security threats that exist on the Web. SQL injection attacks are one of the most serious security vulnerabilities on the Web. Most of these vulnerabilities are caused by a lack of input validation and the use of SQL parameters. SQLMap is an application from the Kali Linux operating system that is useful for injecting data on a website by using the features available in this application. In this paper, author conducts a security assessment to detect attacks on a website, more precisely to detect SQL Injection attacks, using the K-Nearest Neighbor method and naïve bayes. The results obtained are that the website being tested has SQL Injection vulnerabilities, and the K-Nearest Neighbor method is the best method for this case because it has an accuracy of 94.2%. In comparison, the Naïve Bayes method has an accuracy of 80%.

Analysis of Methods for Detecting Vulnerabilities of Web Resources to SQL Injections

This article examines the issue of SQL injection vulnerabilities in web applications and the use of automated scanners to detect these vulnerabilities. It begins with a description of SQL injections and their consequences, as well as manual testing for vulnerabilities. The article goes on to analyze various automated vulnerability scanners, including Acunetix, Burp Suite, Nessus, OpenVAS, SQLMap, OWASP ZAP, and Nikto. Each scanner has its advantages and disadvantages, as well as the level of detail and functionality. The article concludes with conclusions that emphasize the importance of understanding SQL injection risks and using the right tools to detect them. It is emphasized that automated scanners are not a one-size-fits-all solution and must be accompanied by manual verification and analysis. The article points to the need for constant updates of scanners and a combination of automated and manual methods to ensure the highest level of security. It provides readers with a useful overview of the various aspects and aspects of using automated SQL injection vulnerability scanners in web applications.

Count vectorizer model based web application vulnerability detection using artificial intelligence approach

A web application is a dynamic, intricate, and interactive program that provides end-users with information and services such as utility payments, online communication, e-learning, socializing, shopping, online banking, and income tax filing etc. Web applications have become a major target for attackers due to their accessibility, availability, and ubiquity. Web application vulnerabilities are hazardous for some reasons. Attackers can harm an organizations image and status. The implementation flaws in web application allow the invader to infuse user-input that violates the syntax-based assembly of the query or infuse malicious code etc. Among various types of injection flaws, SQL injection (SQLI) is more prominent than (XML) both are considered as common application-layer web attack, which allows the attacker to bypass the security mechanisms therefore; these two are ranked as the most common vulnerabilities. Hence, a methodology for detecting evaluating both SQLI & XML vulnerabilities in web applications are considered for research. This research work addresses the above mentioned flaws and proposed an Ensemble Method to classify the Structure Query Language injection vulnerabilities, we selected a benchmark dataset with 33,758 rows containing; various types of SQL and XML injection attacks. Raw data is preprocessed to remove artifacts, and then feature engineering is performed using Natural Language Processing techniques to clean the data and extract 6 types of features such as TF-IDF, Word-to-Vector, SkipGram, Count Vectorizer, Glove and Continuous Bag of words. Imbalance data is handled using sampling techniques, best features are selected using 4 types of validation techniques Significant Test, PCA, Variance Threshold and Sbest. Prepared data is provided to Ensemble Model having two stages; Stage-2 accepts URL from the user and detects presence of susceptibility in the sub domains and domains. Stage-1 having 9 different types of machine learning models Multinomial, Gaussian, Bernoulli Naive Bayes, Logistic Regression, Decision Tree, Random Forest, AdaBoost, SVC with, poly, rbf and linear kernel, these models are trained on additional vectors such as google news and glove to detect the new query either SQL or XML for presences or absence of vulnerability, using this proposed ensemble approach obtained the accuracy of 99%.

Review Paper on a Study on SQL Attacks and Defense

In today's era, every person is utilizing websites and so many different web applications for online administrations, for example booking of railway tickets, movie ticketing, shopping, communication, and so forth. These websites consist sensitive and confidential information. With the linearity of web applications in the last decade, the unconstructive crash of security has also matured either. SQL injection attack is one such attack where the anonymous user can append SQL code to the input query. This research paper starts with developing criteria for a systematic literature review based on research questions, quality assessment, and data samples. The paper presents various SQL injection techniques with their intended attacks. Further studies explore different techniques to prevent attacks. Existing vulnerabilities of Web systems threaten the regular work of information systems. The most common Web system vulnerability is SQL injection. There are known approaches to protect Web applications against SQL injection attacks in the article. To improve the Web software security is developed defense mechanism that protects Web resources from SQL injection performing. To implement this software it is used PHP, JavaScript, and formal language theory known as regular expressions. As a result, it is received a software tool that allows protecting Web software from SQL injection vulnerability. The developed software tool allows users to protect their Web applications from an attack using SQL.

Combinatorial methods for dynamic gray‐box SQL injection testing

SummaryThis work presents an extended and enhanced gray‐box combinatorial security testing methodology for SQL injection vulnerabilities in web applications. We propose multiple new attack grammars modelling SQLi attacks against MySQL‐compatible databases, each one targeting a different injection context. Additionally, these grammars are also dynamically refined at the beginning of each attack against an endpoint of a web application, as a further optimization of the used attack model by taking into account the specifics of the generated query of that endpoint. Our goal is to enhance existing combinatorial approaches for detecting SQL injection vulnerabilities. The newly developed methodology is implemented in a prototype security testing tool called SQLInjector+, which is an extension of an earlier prototype developed by us in prior work. This improved tool can attack (i.e. test) any web application that uses a MySQL‐compatible database management system. We evaluate our revised approach and improved prototype tool in a case study comprising of different kinds of web applications to which SQLi is a potential security threat. The case study contains the well‐known verification framework WAVSEP among other five real‐world web applications and one web application firewall. Our generated attack vectors, constructed via combinatorial methods applied to our improved and dynamically optimized attack grammars, are capable of injecting every known vulnerable endpoint in WAVSEP and also of finding new vulnerable parameters in some of the real‐world applications investigated in this paper. Our approach performs equally well or better when compared with existing state‐of‐art of SQL injection security testing tools (sqlmap, w3af, wapiti and fuzzdb) across all tested web applications in the case study.

SQL Injection

SQL Injection (SQLi) could also be a kind of an injection attack that makes it possible to execute malicious SQL statements. These statements control a database server behind an internet application. Attackers can use SQL Injection vulnerabilities to bypass application security measures. They can go around authentication and authorization of an internet page or web application and retrieve the content of the entire SQL database. They can also use SQL Injection to feature, modify, and delete records within the database.

A Detailed Survey on Various Aspects of SQL Injection in Web Applications: Vulnerabilities, Innovative Attacks, and Remedies

In today’s world, Web applications play a very important role in individual life as well as in any country’s development. Web applications have gone through a very rapid growth in the recent years and their adoption is moving faster than that was expected few years ago. Now-a-days, billions of transactions are done online with the aid of different Web applications. Though these applications are used by hundreds of people, in many cases the security level is weak, which makes them vulnerable to get compromised. In most of the scenarios, a user has to be identified before any communication is established with the backend database. An arbitrary user should not be allowed access to the system without proof of valid credentials. However, a crafted injection gives access to unauthorized users. This is mostly accomplished via SQL Injection input. In spite of the development of different approaches to prevent SQL injection, it still remains an alarming threat to Web applications. In this paper, we present a detailed survey on various types of SQL Injection vulnerabilities, attacks, and their prevention techniques. Alongside presenting our findings from the study, we also note down future expectations and possible development of countermeasures against SQL Injection attacks.

An Efficient Model to Detect and Prevent SQL Injection Attack

SQL injection attack (SQLIA) is considered one of most threats used to attack web applications. Therefore, attackers used SQL injection vulnerability to gain ultimate access to databases that belong to applications and expose their sensitive information. Thus, attackers use SQL injections vulnerability to manipulate data also it could be used to take full control of the target machine. Accordingly, several methods were proposed in the literature to address this vulnerability widely because of its importance and high impact on the security of web applications. Thus, we propose a model to detect and prevent SQL injection attacks, which uses runtime validation to detect the occurrence of such attacks, our proposed model is adaptable to any existing system, with no need to modify the client or server and either no need to know web application source code. Furthermore, the modification independence is done by adding additional middleware between client and server. Thus, any check process is done on this middleware, and it is represented as a proxy that can do sanitize the inputs for detecting and preventing SQLIA. Furthermore, our proposed model accuracy reaches 86:6% for detecting and preventing SQLIA.

An Efficient Model to Detect and Prevent SQL Injection Attack

SQL injection attack (SQLIA) is considered one of most threats used to attack web applications. Therefore, attackers used SQL injection vulnerability to gain ultimate access to databases that belong to applications and expose their sensitive information. Thus, attackers use SQL injections vulnerability to manipulate data also it could be used to take full control of the target machine. Accordingly, several methods were proposed in the literature to address this vulnerability widely because of its importance and high impact on the security of web applications. Thus, we propose a model to detect and prevent SQL injection attacks, which uses runtime validation to detect the occurrence of such attacks, our proposed model is adaptable to any existing system, with no need to modify the client or server and either no need to know web application source code. Furthermore, the modification independence is done by adding additional middleware between client and server. Thus, any check process is done on this middleware, and it is represented as a proxy that can do sanitize the inputs for detecting and preventing SQLIA. Furthermore, our proposed model accuracy reaches 86:6% for detecting and preventing SQLIA.

ANALISIS KEAMANAN WEBSITE SMAN 1 SUMBAWA MENGGUNAKAN METODE VULNERABILITY ASESEMENT

SMAN 1 Sumbawa is a school that provides information to students through a website-based information system to facilitate school administration services. Considering that the Website can be accessed widely, it is necessary to pay attention to the security of the Website. One of them is by using the Vulnerability Assessment method. The Vulnerability Assessment method is a method for conducting vulnerability testing on a website or application that has the potential to enter an attack which consists of several stages such as Network Discovering, Vulnerability Scanning, and Result Analysis. This stage aims to identify security holes on the SMAN 1 Sumbawa website. The tests that have been carried out have identified four levels of vulnerability, namely high, medium, low, and informational on the SMAN 1 Sumbawa website. The hight vulnerability level obtained is SQL Injection. The SQL Injection vulnerability makes it easy for attackers to access the entire database. The results of the tests that have been carried out show that the SMAN 1 Sumbawa Website has many vulnerabilities or Vulnerability that the SMAN 1 Sumbawa Website is still in an unsafe state.

SQL Injection Vulnerability Detection Using Deep Learning: A Feature-based Approach

SQL injection (SQLi), a well-known exploitation technique, is a serious risk factor for database-driven web applications that are used to manage the core business functions of organizations. SQLi enables an unauthorized user to get access to sensitive information of the database, and subsequently, to the application’s administrative privileges. Therefore, the detection of SQLi is crucial for businesses to prevent financial losses. There are different rules and learning-based solutions to help with detection, and pattern recognition through support vector machines (SVMs) and random forest (RF) have recently become popular in detecting SQLi. However, these classifiers ensure 97.33% accuracy with our dataset. In this paper, we propose a deep learning-based solution for detecting SQLi in web applications. The solution employs both correlation and chi-squared methods to rank the features from the dataset. Feed-forward network approach has been applied not only in feature selection but also in the detection process. Our solution provides 98.04% accuracy over 1,850+ recorded datasets, where it proves its superior efficiency among other existing machine learning solutions.

Analysis of Website Security of SMKN 1 Pangandaran Against SQL Injection Attack Using OWASP Method

Every technological development is usually accompanied by an increase in security on a digital platform that is widely used by a large audience. However, with the rapid development of information technology, some of the security gaps found can be used as loopholes to commit crimes where these actions can harm others. These actions are often carried out by irresponsible people to benefit from the actions taken. Some of the gaps that are often found on digital platforms, especially on websites are SQL Injection where from BSSN data from January to April 2019, 73% of the vulnerability reports received are SQL Injection vulnerabilities. In addition, SQL Injection is also the number one threat to the security of a website application, where this SQL Injection attack is a code injection attack technique that is carried out by exploiting the security gaps that exist in the database layer of a website. In this study, taking the theme of SQL Injection which aims to conduct security analysis from the website of the school agency SMKN 1 Pangandaran, using OWASP which is used to carry out the analysis process. The result of testing the system on the website is where the attacker can perform injection using SQL payload to enter the database. Where the results of system analysis and testing will be recommended to close gaps on existing websites.

Simulating SQL injection vulnerability exploitation using Q-learning reinforcement learning agents

In this paper, we propose a formalization of the process of exploitation of SQL injection vulnerabilities. We consider a simplification of the dynamics of SQL injection attacks by casting this problem as a security capture-the-flag challenge. We model it as a Markov decision process, and we implement it as a reinforcement learning problem. We then deploy reinforcement learning agents tasked with learning an effective policy to perform SQL injection; we design our training in such a way that the agent learns not just a specific strategy to solve an individual challenge but a more generic policy that may be applied to perform SQL injection attacks against any system instantiated randomly by our problem generator. We analyze the results in terms of the quality of the learned policy and in terms of convergence time as a function of the complexity of the challenge and the learning agent’s complexity. Our work fits in the wider research on the development of intelligent agents for autonomous penetration testing and white-hat hacking, and our results aim to contribute to understanding the potential and the limits of reinforcement learning in a security environment.

Fault-based testing for discovering SQL injection vulnerabilities in web applications

In this paper we proposed a model to investigate the behaviour of websites when dealing with invalid inputs. Many vulnerabilities rise from invalid inputs. An invalid input is considered as a form of a successful attack if it is processed by the website code or back-end database. Based on this assumption, we proposed a list of indicators that tested and processed invalid inputs. A tool is developed to implement this model. We tested the model through evaluating several websites selected randomly. Our tool has no special credentials or access to any of the tested websites. We found many SQL injection vulnerabilities based on our proposed model. Upon the manual investigation of the web pages that showed such vulnerabilities, we found few instances of false positives. We believe that this can provide a systematic and automated approach to test websites for vulnerabilities related to improper input validation.

Fault-based testing for discovering SQL injection vulnerabilities in web applications

In this paper we proposed a model to investigate the behaviour of websites when dealing with invalid inputs. Many vulnerabilities rise from invalid inputs. An invalid input is considered as a form ...