Design of a System Model Based on Machine Learning Technique for SQL Injection Detection

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 applications and databases continue to face grave danger from SQL injection attacks, which can result in unauthorized access, data modification, and system compromise.This report discusses the methods attackers use to exploit SQL injection vulnerabilities and emphasizes the dangers of successful attacks, such as data leaks and system compromise.This research proposes a comprehensive system for detecting SQL injection attacks using concatenated Input Validation and Parsing Method (CIVP).The site used as experimental material is the Multimedia and Journal Services Site.Based on the results of forensic analysis on the Journal Services Site, there were several attacks in cyberspace, including using SQLMAP and Python.The system created has successfully detected SQL injection attacks.Based on the test results, it was found that the use of the method proposed in this study succeeded in making processing time 15.2% more efficient.Experiments carried out with the method proposed in this study succeeded in increasing the attack detection accuracy from 96-97% to 99.5% with a p-value of 0.008446.

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.

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.

With1 the rapid development of computer network technology, people's life and work have become more and more dependent on the Internet. Consequent network security issues have also received much attention. At present, SQL injection attacks have become the main method of hacking. SQL injection vulnerabilities seriously threaten the security of WEB application systems. This article takes the PHP language as an example, introduces the reasons for the SQL injection in detail, conducts in-depth research on the common SQL injection attack methods. Based on the practical penetration testing practice, puts forward the SQL injection detection technology and how to avoid SQL injection vulnerability when writing WEB program code. This article provides detailed technical support for testing SQL injection and provides a powerful guarantee for WEB information system in SQL injection defense.

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.

SQL Injection attacks is a common threat to web applications that utilizes poor input validation to implement attack on a target database. It is becoming a very serious problem in web application as successful leads to loss of integrity and confidentiality and this makes it a very sensitive issue of software security. This study gives a review on SQL Injection detection and prevention techniques using machine learning classifiers. Machine Learning approach has been found to be profound for SQLIA mitigation, which is implemented through defensive coding approach. An experimental analysis was performed to evaluate the performance of the learning classification algorithms to choose the best algorithm. It is imperative to note that a good number of the evaluated techniques were able to detect and prevent the SQLIA based on the KDD Test dataset. From the findings, Naive Bayes had the minimum Accuracy 80.01%, Sensitivity as well as Specificity while Decision Tree had the highest Accuracy 98.11%, Sensitivity and Specificity and therefore was chosen as the best classifier for SQLIA detection and prevention. Therefore, beyond Accuracy, other performance evaluation metrics are critical for optimal algorithm selection for predictive analytics.

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.

Software as a Service (SaaS) has been adopted in a fast pace for applications and services to run on software cloud platform. However, the success of SaaS in cloud computing cannot obscure the security challenges faced by the web applications deployed on cloud SaaS. Like other web-based systems, cloud applications are prone to most of the common web attacks. The SQL injection attack is one of the most potential threat to a SaaS application. This may result in loss of sensitive and important data (e.g., financial, personal). Through this kind of attacks, the attacker can steal critical and confidential information to a business or an organization leading to high impact on tangible (e.g., data) and intangible (e.g., reputation) assets. The purpose of this research is to investigate the potential of using machine learning techniques for SQL injection detection on the application level. The algorithms to be tested are classifiers trained on different malicious and benign payloads. They take a payload as input and decide whether the input contains a malicious code or not. The results show that these algorithms can distinguish normal payloads from malicious payloads with a detection rate higher than 98%. The paper also compares the performance of different machine learning models in detecting SQL injection attacks.

In the digital age of the 21st century, with the widespread use of Web applications, SQL injection attacks have become one of the significant challenges in the field of information security. This study proposes a method for detecting SQL injection attacks based on an improved Text-CNN model. The method starts with the preprocessing of collected SQL injection attack data, followed by the use of the convolutional layers of the Text-CNN model to extract local features, and then combines the CAM attention mechanism to achieve the task of SQL injection detection. The introduction of the CAM attention mechanism aims to merge the information from max pooling and average pooling to reduce information loss and enhance the global representation capability of the model. Experimental results show that the proposed method has improved in terms of accuracy, precision, recall rate, and F1 score, with values of 92.40%, 92.45%, 92.29%, and 0.9237, respectively. Given that traditional Text-CNN, Text-RNN, and CNN algorithms can perform well in recognizing SQL injection attacks, this provides help for subsequent applications in network security protection.

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.

Web sites are dynamic, static, and most of the time a combination of both. Web sites need protection in their database to assure security. An SQL injection attacks interactive web applications that provide database services. These applications take user inputs and use them to create an SQL query at run time. In an SQL injection attack, an attacker might insert a malicious SQL query as input to perform an unauthorized database operation. Using SQL injection attacks, an attacker can retrieve or modify confidential and sensitive information from the database. It may jeopardize the confidentiality and security of Web sites which totally depends on databases. This report presents a “code reengineering” that implicitly protects the applications which are written in PHP from SQL injection attacks. It uses an original approach that combines static as well as dynamic analysis. [2] In this report, I mentioned an automated technique for moving out SQL injection vulnerabilities from Java code by converting plain text inputs received from users into prepared statements. [3]

SQL injection is one of the important security issues in web applications because it allows an attacker to interact with the application's database. SQL injection attacks can be detected using machine learning algorithms. The effective features should be employed in the training stage to develop an optimal classifier with optimal accuracy. Identifying the most effective features is an NP-complete combinatorial optimization problem. Feature selection is the process of selecting the training dataset's smallest and most effective features. The main objective of this study is to enhance the accuracy, precision, and sensitivity of the SQLi detection method. In this study, an effective method to detect SQL injection attacks has been proposed. In the first stage, a specific training dataset consisting of 13 features was prepared. In the second stage, two different binary versions of the Gray-Wolf algorithm were developed to select the most effective features of the dataset. The created optimal datasets were used by different machine learning algorithms. Creating a new SQLi training dataset with 13 numeric features, developing two different binary versions of the gray wolf optimizer to optimally select the features of the dataset, and creating an effective and efficient classifier to detect SQLi attacks are the main contributions of this study. The results of the conducted tests indicate that the proposed SQL injection detector obtain 99.68% accuracy, 99.40% precision, and 98.72% sensitivity. The proposed method increases the efficiency of attack detection methods by selecting 20% of the most effective features.

In this work, performance of two existing SQL injection detection methods is compared and analysed together. The first existing method uses removal of SQL query attribute values method, and the other one uses database profiling technique in detecting malicious SQL query in SQL injection attacks. Both detection method algorithms are implemented and tested using SQL query log generated from DVWA and Mutillidae web applications. Analysis and evaluation from results of the experiment showed that detection method based on the considered database profiling method is found to be much flexible and more effective. Nevertheless, it was found that the former method still has some room of improvement that could be done. Therefore, improvement on the former method is proposed that increase the effectiveness of the method to distinguish non-malicious query and the malicious query during the SQL injection detection process.