A security analysis tool for web application reinforcement against SQL injection attacks (SQLIAs)

Objectives: SQL Injection Attack (SQLIA) is a frequent and a severe security issue in the web applications. In SQLIA, hacker can obtain the benefit of poor input validation and weak coded web application. Due to the successful execution of a SQLIA, integrity and confidentiality of data are lost which results in the degrading organization’s market value. This paper gives a valuable analysis of various types of SQLIAs, methods and mechanisms. It also explores various detection and prevention techniques. Methods/Analysis: A rigorous survey has been conducted and consequently, comparative analysis of various detection and prevention techniques is done with respect to various types of attacks. In current research various pattern matching algorithms for the detection and prevention of SQLIA are analyzed and few are tested. Findings: Comparative analysis of Boyer Moore pattern matching algorithm is done with Naive String pattern matching algorithm. The time and memory consumption taken by both the algorithms has been analyzed. The results show that Boyer Moore is more efficient to detect and prevent the SQLIAs as compared to Naive string. Novelty/Improvement: In future it aims to propose an algorithm which will enhance in terms of efficiency and resource usage. The approach needs to be implemented for every pattern matching algorithm to find the best solution regarding detection and prevention of SQLIA.

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.

With the emergence and popularity of web application, threats related to web applications has increased to large extent. Among many other web applications threats Structured Query Language Injection Attack (SQLIA) is the dominant in its use due to its ability to access the data. Many solutions are proposed in this regard that has success in specific conditions. The proposed model is based on the dynamic analyzer model. The proposed model also has certain advantages like wide applicability, fast response time, coverage to large number of techniques of SQL Injections (SQLI) and efficient in term of resource usage.

SQL injection attacks (SQLIAs) remain a prevalent threat to web applications, exploiting vulnerabilities in database interactions to compromise data security. Detecting such attacks effectively is crucial for ensuring robust application security. This study investigates the use of machine learning techniques to identify SQLIAs by analyzing patterns and features in SQL queries. A dataset comprising both legitimate and malicious SQL queries is utilized to train and evaluate various machine learning models, including decision trees, support vector machines, and neural networks. The proposed approach achieves high accuracy in distinguishing between benign and malicious queries, showcasing the potential of machine learning for proactive SQLIA detection. The findings highlight the importance of feature selection, algorithm choice, and real-time detection capabilities in mitigating the risk of SQL injection attacks. This research provides a foundation for developing intelligent, automated systems to enhance the security of database-driven applications.

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.

The ubiquity of Web applications has led to an increased focus on the development of attacks targeting these applications. One particular type of attack that has recently become prominent is the SQL injection attack. SQL injection attacks can potentially result in unauthorized access to confidential information stored in a backend database. In this paper we describe an anomaly based approach which utilizes the character distribution of certain sections of HTTP requests to detect previously unseen SQL injection attacks. Our approach requires no user interaction, and no modification of or access to, either the backend database or the source code of the web application itself. Our practical results suggest that the model proposed in this paper is superior to existing models at detecting SQL injection attacks. We also evaluate the effectiveness of our model at detecting different types of SQL injection attacks.

SQL injection (SQLi) attacks are one of the oldest and most serious security threats, consistently ranking among the top ten critical web security risks. Traditional defense mechanisms against SQL injection predominantly use blacklists to disallow common injection characters or terms. However, the major challenge for these systems is to create a comprehensive list of potential SQLi characters, terms, and multi-terms that encompass various types of SQLi attacks (time-based, error-based, etc.), taking into account various SQL datasets (such as MySQL, Oracle, and NoSQL). Recently, some research studies have concentrated on feature learning from SQL queries by applying some well-known deep architectures to detect SQLi attacks. Motivated by a similar objective, this research introduces a novel deep learning-based SQLi detection system named “Bidirectional LSTM-CNN based on Multi-View Consensus” (MVC-BiCNN). The proposed method implements a pre-processing step that generates multiple views from SQL data by semantically encoding SQL statements into their corresponding SQL tags. By utilizing two different main layers, which are bidirectional long short-term memory (LSTM) and convolutional neural network (CNN), the proposed method learns a joint latent space from multi-view representations. In the detection phase, the proposed method yields separate predictions for each representation and assesses whether the query constitutes an SQLi attack based on a consensus function’s output. Moreover, Interpretable Model-Agnostic Annotations (LIME), one of the methods of Explainable Artificial Intelligence (XAI), is employed for the purpose of interpreting the model’s results and analyzing the SQL injection (SQLi) inputs. The experimental results demonstrate that MVC-BiCNN outperforms the baseline methods, yielding 99.96% detection rate.

Web application is the base of online businesses through the Internet. The emergence of COVID 19 forced almost every job to operate online so as to bridge the distance amongst individuals. The rapid increment in the needs of web application increases security threats on information and data. According to the Open Web Application Security Project, Structured Query Language Injection Attack (SQLIA) is a top security threat for web application. SQLIA inserts malicious code to gain access or to manipulate database information by cheating the server to bypass the code to the database, thereby causing a severe impact on web application. In this paper, permutation encoding method has been proposed to prevent SQLIA, which is based on encoding all database information using the proposed method. Initially, a special character is inserted to restrict the method from reversing. Subsequently, permutation encoding method is applied. Permutation refers to the method wherein the bit location is changed within three characters and then radix encoding is applied. Permutation is based on the primitive root value. Encoding has been used to hide permutation. The proposed method is implemented and tested using PHP and MySQL databases, where the proposal result has been compared with those of other proposal methods. The results with security analysis prove that the proposal method prevents SQLIA and protects database information.

Day after day, SQL Injection (SQLI) attack is consistently proliferating across the globe. According to Open Web Application Security Project (OWASP) Top Ten Cheat Sheet-2014, SQLI is at top in the list of online attacks. The cause of spread of SQLI is thought to be Unsecure Software Engineering. The Software Development process itself appears to look at security as an add-on to be checked and deployed towards the end of the software development lifecycle which leads to vulnerabilities in web applications. This paper is an attempt to integrate security in early stages of SDLC i.e. in design phase to mitigate SQLI vulnerability. How SQLI attack happens is illustrated. Threat Modeling is performed to mitigate the SQLI vulnerability.

Web applications are becoming an important part of our daily life. So attacks against them also increases rapidly. Of these attacks, a major role is held by SQL injection attacks (SQLIA). This paper proposes a new method for preventing SQL injection attacks in JSP web applications. The basic idea is to check before execution, the intended structure of the SQL query. For this we use semantic comparison. Our focus is on stored procedure attack in which query will be formed within the database itself and so difficult to extract that query structure for validation. Also this attack is less considered in the literature.

The powerful Open Web Application Security Project (OWASP)-Zed Attack Proxy (ZAP) tool secures web applications with extensive security testing. Its main goal is to find and fix web application vulnerabilities before they can be exploited. The goal is to improve web application security using OWASP ZAP scans and inspections. Simulating SQL injection and cross-site scripting attacks using the tool reveals an application's security flaws. OWASP ZAP automates testing to protect sensitive data and web application integrity. The purpose is to protect online applications from attacks to reduce security breaches and ensure industry compliance. Modern online applications' security and dependability depend on OWASP ZAP's systematic vulnerability discovery and mitigation. By enhancing scan performance and providing actionable security information, the experimental results show that the OWASP ZAP helps safeguard online applications and reduce the danger of cyberattacks. The objective is to enhance its skills to provide comprehensive, prompt, dependable security evaluations across various situations.

SQL Injection Attack (SQLIA) has been consistently ranked among the top security threats against web applications for more than a decade. Nowadays, attackers use sophisticated tools to launch automated injection attacks. The problem of prevention and detection of SQLIA has been long attended by the research community, but hardly any solution exists for protecting multiple websites in a shared hosting environment. In this paper, we present a novel method to detect malicious queries using a twin Hidden Markov Model (HMM) ensemble and validate it with large set of benign and malicious queries collected from five sample web applications written in PHP and MySQL. Following the Multiple Classifier System (MCS) paradigm, we combine the output of individual HMMs to arrive at the final decision, which provides better accuracy and lower false alarms. The system is intended to work at the database firewall layer, therefore it can protect multiple web applications hosted on a shared server. The initial experimental results are very encouraging and indicate that the approach can effectively identify wide varieties of SQLIA with negligible impact on performance. The technique can be easily ported to other languages and database platforms without requiring major modifications.

SQL Injection Attack has been a major security threat to web applications since last 15 years. Nowadays, hackers use automated tools to discover vulnerable websites and launch mass injection attacks. Accurate run-time detection of SQL injection has been a challenge in spite of extensive research in this area. This paper presents a novel approach for real-time detection of SQL injection attacks using query transformation and document similarity measure. Acting as a database firewall, the proposed system named SQLiDDS, can protect multiple web applications using the database server. With additional inputs from human expert, SQLiDDS can also become more robust over time. Our experimental results confirm that this approach can effectively detect and prevent all types of SQL injection attacks with good accuracy yet negligible impact on system performance. The approach was tested on web applications built using PHP and MySQL, however it can be easily adopted in other platforms with minimal changes.

SQL Injection Attack (SQLIA) is one of the most severe attack that can be used against web database-driven applications. Attackers use SQLIA to obtain unauthorized access and perform unauthorized data modifications due to initial improper input validation by the web application developer. Various studies have shown that, on average, 64% of web applications worldwide are vulnerable to SQLIA due to improper input. To mitigate the devastating problem of SQLIA, this research proposes an automatic black box testing for SQL Injection Vulnerability (SQLIV). This acts to automate an SQLIV assessment in SQLIA. In addition, recent studies have shown that there is a need for improving the effectiveness of existing SQLIVS in order to reduce the cost of manual inspection of vulnerabilities and the risk of being attacked due to inaccurate false negative and false positive results. This research focuses on improving the effectiveness of SQLIVS by proposing an object-oriented approach in its development in order to help and minimize the incidence of false positive and false negative results, as well as to provide room for improving a proposed scanner by potential researchers. To test and validate the accuracy of research work, three vulnerable web applications were developed. Each possesses a different type of vulnerabilities and an experimental evaluation was used to validate the proposed scanner. In addition, an analytical evaluation is used to compare the proposed scanner with the existing academic scanners. The result of the experimental analysis shows significant improvement by achieving high accuracy compared to existing studies. Similarly, the analytical evaluations showed that the proposed scanner is capable of analyzing attacked page response using four different techniques.

Modern database systems are critical for storing sensitive information but are increasingly targeted by cyber threats, including SQL injection (SQLi) attacks. This research proposes a robust security framework leveraging Docker-based virtualisation to enhance database security and mitigate the impact of SQLi attacks. A controlled experimental methodology evaluated the framework’s effectiveness using Damn Vulnerable Web Application (DVWA) and Acunetix databases. The findings reveal that Docker significantly reduces the vulnerability to SQLi attacks by isolating database instances, thereby safeguarding user data and system integrity. While Docker introduces a significant increase in CPU utilisation during high-traffic scenarios, the trade-off ensures enhanced security and reliability for real-world applications. This study highlights Docker’s potential as a practical solution for addressing evolving database security challenges in distributed and cloud environments.