Detection of SQL injection attacks using Hidden Markov Model

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.

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 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.

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.

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 tool that is capable of reporting the transformations needed to reinforce the security of a Java-based web application and its database against SQLIAs. This tool which is based on static analysis and runtime validation uses our new technique for detection and prevention of SQLIAs. In our technique user inputs in SQL queries are removed and some information is gathered in order to make the detection easier and faster at runtime. According to these information the tool reports the transformations needed and the location of the transformations in source code and therefore after applying the transformations the result would be a reinforced web application against SQLIAs.

As web applications become increasingly popular for offering data and services among businesses and organizations they also become more susceptible to security risks. Many organizations rely today on Web Vulnerability Scanners (WVSs) to identify vulnerabilities in their web applications. However, one of the most prevalent types of web application vulnerabilities, SQL Injections (SQLi), can often go undetected by WVSs. OWASP ZAP is an open-source web vulnerability scanner that allows security professionals to develop rules to improve vulnerability detection capability. In this paper, a new method is proposed to improve the detection capability of OWASP ZAP for SQLi. We aim to accurately detect the four major types of SQLi: Error-based, Union-based, Time-based blind, and Authentication Bypass. We implemented this new method using OWASP ZAP detection policies. The method was tested against two benchmark vulnerable web applications, Mutillidae and WebGoat, and was shown to significantly improve the detection of SQL injection attacks.

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.

In recent years, many financial sectors are evolving with huge numbers of web applications, which plays a crucial role in organizations to make important decisions. Considering this, the data has to be secured in order to prevent it from any attacks which lead to a huge loss. One of the topmost attacks in the database is SQL injection attack, is injecting some malicious query into the database causing serious threats. This paper proposes an enhanced approach to dynamic query matching technique by imposing a sanitizer for quick and easy detection of attack.

In the evolving landscape of cybersecurity, timely identification of software vulnerabilities plays a vital role in safeguarding digital assets. This paper introduces a full-stack web application designed to provide a centralized platform for vulnerability assessment. Developed using React.js and Tailwind CSS on the frontend and Python-based APIs on the backend, the system integrates key security features such as file and URL scanning through VirusTotal, detection of SQL injection and XSS attacks using OWASP ZAP, CVE information retrieval via the NVD API, and static code analysis for Python, Java, and C++ using tools like Bandit, SpotBugs, and CPPcheck. Additionally, it leverages the Nikto scanner to detect web server vulnerabilities. The platform delivers real-time scan results through an intuitive interface, making it accessible to both technical and non-technical users. By combining multiple open-source tools into a single application, this project enhances the detection and mitigation of security risks in web environments. The platform includes real-time packet capturing and endpoint monitoring features, offering a network-layer perspective on potential threats. Its modular architecture and asynchronous API handling ensure fast, intelligent responses across all tools. These advancements enhance both detection accuracy and user experience. Together, they make Cyber Sentinel a complete and adaptive security solution.

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.

The rapid growth of web services has brought about rising challenges in terms of security and performance. Web servers are often attacked by many cyber threats, such as Denial-of-Service (DoS) and SQL injection among the most damaging threats. DoS attacks can block access to services by overloading server resources and SQL injection can steal confidential data and damage organizational trust. Simultaneously, efficient traffic control is essential for preserving server functionality and guaranteeing dependable service. This work proposes a system that consolidate load balancing techniques with safeguards against DoS and SQL injection threats. The system works a configurable rate-limiting module to monitor incoming request rates and enforce thresholds for DoS detection, while a Gaussian Naive Bayes model does feature extraction and classification of request inputs to identify SQL injection attacks and distinguish malicious payloads from legitimate traffic. Therefore, it uses manual selection between Round Robin and Least Connection methods to handle traffic effectively why simultaneously integrating strategies to identify and mitigate harmful requests. The experimental results showed that the system achieved 96% accuracy for SQLi detection accuracy, demonstrating robust security mechanisms and efficient load balancing performance.

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.

The purpose of this article is to propose a hybrid deep learning framework for the effective detection of SQL injection (SQLi) attacks in database-driven web applications. The proposed framework integrates supervised and unsupervised learning techniques with honeypot-assisted intelligence collection to address the limitations of existing SQLi detection systems, which are largely reactive and primarily focused on classification accuracy without adaptive intelligence mechanisms. The architecture combines a CNN–LSTM pathway for supervised pattern recognition with an autoencoder-based anomaly detection pathway trained on benign queries. Outputs from both pathways are fused through a learned fusion layer to improve robustness against both known and previously unseen attack variants. Queries classified as malicious are redirected to a low-interaction database honeypot, enabling structured capture of attack payloads and behavioural artefacts for security intelligence generation. Experimental evaluation conducted on a curated dataset of approximately 50,000 SQL queries demonstrates strong detection performance, achieving an average accuracy of 99.2 per cent, precision of 98.9 per cent, recall of 99.0 per cent, and an AUC of 0.99 across multiple runs. Although the architecture supports closed-loop retraining using honeypot-captured data, this study focuses on offline performance and initial intelligence acquisition, with adaptive retraining identified as future work. The results demonstrate that integrating supervised learning, anomaly detection, and deception mechanisms provides a robust foundation for adaptive SQLi defence systems.

Web applications have become central in the digital landscape, providing users instant access to information and allowing businesses to expand their reach. Injection attacks, such as SQL injection (SQLi), are prominent attacks on web applications, given that most web applications integrate a database system. While there have been solutions proposed in the literature for SQLi attack detection using learning-based frameworks, the problem is often formulated as a binary, single-attack vector problem without considering the prioritization and prevention component of the attack. In this work, we propose a holistic solution, SQLR34P3R, that formulates the SQLi attack as a multi-class, multi-attack vector, prioritization, and prevention problem. For attack detection and classification, we gathered 457,233 samples of benign and malicious network traffic, as well as 70,023 samples that had SQLi and benign payloads. After evaluating several machine-learning-based algorithms, the hybrid CNN-LSTM models achieve an average F1-Score of 97% in web and network traffic filtering. Furthermore, by using CVEs of SQLi vulnerabilities, SQLR34P3R incorporates a novel risk analysis approach which reduces additional effort while maintaining reasonable coverage to assist businesses in allocating resources effectively by focusing on patching vulnerabilities with high exploitability. We also present an in-the-wild evaluation of the proposed solution by integrating SQLR34P3R into the pipeline of known vulnerable web applications such as Damn Vulnerable Web Application (DVWA) and Vulnado and via network traffic captured using Wireshark from SQLi DNS exfiltration conducted with SQLMap for real-time detection. Finally, we provide a comparative analysis with state-of-the-art SQLi attack detection and risk ratings solutions.

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.