SQL Injection Research Articles

A deep learning approach based on multi-view consensus 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.

Securing transportation web applications: An AI-driven approach to detect and mitigate SQL injection attacks

Securing transportation web applications: An AI-driven approach to detect and mitigate SQL injection attacks

Enhancing data protection in a web application using Django

This article explores the theme of enhancing data protection in web applications using the Django framework. It identifies the primary vulnerabilities of web applications and analyzes Django's features for defense against attacks such as SQL injections, XSS, CSRF, clickjacking, and data interception. Examining Django's built-in protection mechanisms, the article not only emphasizes their effectiveness against most common attacks but also proposes methods to improve and strengthen security measures. A detailed analysis of Django's authentication module is provided, revealing its key features in ensuring a high level of reliability in user access management. The article describes the stages of password hashing and the logic of its processing. The importance of utilizing HTTPS, SSL, and TLS protocols to enhance the security level of web applications is highlighted. Special attention is given to configuring HTTPS and using security headers, such as HTTP Strict Transport Security (HSTS), which secure connections and prevent data interception attacks. In the analysis of major attack types, including SQL injections, XSS, CSRF, focus is placed on the built-in protection functions of the web system, potential vulnerabilities, and recommendations for improving data security are presented. Additionally, protective measures against clickjacking are discussed, and recommendations for employing intermediary software to effectively counteract such attacks are provided. Emphasis is placed on the necessity of closely monitoring the possibility of page embedding and restricting this functionality to ensure the security of the web system.

A Tool Design for SQL injection vulnerability detection based on improved crawler

A Tool Design for SQL injection vulnerability detection based on improved crawler

SQL injection authentication security threat

SQL injection authentication security threat

PROGESI: a PROxy Grammar to Enhance web application firewall for SQL Injection prevention

PROGESI: a PROxy Grammar to Enhance web application firewall for SQL Injection prevention

Machine Learning Approach to Intrusion Detection: Performance Evaluation

Software-Defined Networking (SDN) simplifies network administration as well as reduces the need for manual configuration on individual devices. SDN is expected to remain a crucial technology in shaping the future of network infrastructure. However due to key vulnerabilities like Open Flow Protocol Weaknesses, Flow Table Poisoning, Weak authentication and authorization mechanisms, it is prone to intrusion attacks. In this paper, authors aim to build framework based on various machine learning models (ML) to capture anomaly packets. Four models Decision tree, Support Vector Classifier, Naïve Bayes Classifier and artificial neural networks are designed and trained to capture anomaly packets. While SVC achieved an impressive accuracy of 99%, Artificial Neural Networks (ANN) demonstrated satisfactory speed and efficiency, despite their accuracy being slightly lower than SVM’s. ANN is then deployed in SDN simulated using Mininet. The model is able to detect all DOS traffic. Other types of attacks SQL Injection, U2R Attack, Buffer Overflow Attack, Probe Attack, Brute Force Attack could not be tested as there is provision to simulated these using Mininet.

A Survey: On Detection and Prevention Techniques of SQL Injection Attacks

A Survey: On Detection and Prevention Techniques of SQL Injection Attacks

AdvSQLi: Generating Adversarial SQL Injections Against Real-World WAF-as-a-Service

AdvSQLi: Generating Adversarial SQL Injections Against Real-World WAF-as-a-Service

IMPLEMENTATION OF OAUTH 2.0 BASED ON LARAVEL FRAMEWORK IN A CASE STUDY OF CLIENT INFORMATION MANAGEMENT SYSTEM

The swift growth of the internet and its utilization by businesses for operational purposes, such as developing information systems and utilizing cloud-based data storage, has been remarkable. The discussed client data recording system is designed to facilitate the recording of wedding photography bookings, easing the workload for employees. However, the rapid expansion of the internet has also introduced security concerns, particularly regarding unauthorized access due to weak website authorization and authentication. Consequently, ensuring and effectively managing access rights to information systems becomes crucial. This study aims to implement secure website login authorization using the OAuth 2.0 method with Laravel Passport in the client data recording information system. Post-authentication, the authorization in the context of OAuth2, used within Laravel Passport, provides users with access tokens to reach the primary interface. This process involves an API that both furnishes and safeguards the intended resources. Upon authentication and receipt of a valid access token from the OAuth2 system, users can utilize the token to access the API. The research outcomes enhance the security of information system access rights, aiming to reduce unauthorized breaches in websites storing vital data, thus ensuring the safety and protection of stored client data. Testing results using SQL Injection yielded 4418 messages sent and 2209 task IDs, with a current fuzzer count of 0, signifying that the system remained secure and impervious to SQL Injection attacks.

IDPS PERFORMANCE ANALYSIS FOR MITIGATING SQL INJECTIONS AND SYN FLOOD ATTACKS

Abstract: Cyberattacks like SQL injection and syn flood attacks can threaten the information system security of an organisation or company. The Intrusion Detection and Prevention System (IDPS) is used as a solution to detect, prevent, and respond to these attacks. However, the effectiveness of IDPS in protecting information systems needs to be evaluated through performance analysis. IDPS performance analysis for mitigating SQL injection and syn flood attacks will use Suricata tools, where performance analysis will include evaluation of system accuracy and efficiency in detecting attacks, as well as the impact of the system on network or information system performance. By doing this performance analysis, it can be seen how effective the IDPS is in providing defences against such attacks. The results of the IDPS performance analysis can help an organisation or company select and implement the right IDPS according to the needs and conditions of the information system. Thus, organisations or companies can improve the security of their information systems from threatening cyber attacks. Keywords: IDPS; SQL_Injection; Suricata; Syn_Flood_Attack Abstrak: Serangan cyber seperti SQL Injection dan Syn Flood Attack dapat mengancam keamanan sistem informasi suatu organisasi atau perusahaan. Intrusion Detection and Prevention System (IDPS) digunakan sebagai solusi untuk mendeteksi, mencegah, dan merespons serangan-serangan ini. Namun, efektivitas IDPS dalam melindungi sistem informasi perlu dievaluasi melalui analisis performa. Analisis performa IDPS untuk mitigasi SQL Injection dan Syn Flood Attack ini akan menggunakan tools suricata dimana analisa performa akan meliputi evaluasi terhadap akurasi dan efisiensi sistem dalam mendeteksi serangan, serta dampak dari sistem terhadap kinerja jaringan atau sistem informasi. Dengan melakukan analisis performa ini, dapat diketahui seberapa efektif IDPS dalam memberikan perlindungan terhadap serangan-serangan tersebut. Hasil dari analisis performa IDPS dapat membantu organisasi atau perusahaan dalam memilih dan mengimplementasikan IDPS yang tepat sesuai dengan kebutuhan dan kondisi sistem informasi yang dimiliki. Dengan demikian, organisasi atau perusahaan dapat meningkatkan keamanan sistem informasi mereka dari serangan-serangan cyber yang mengancam. Kata kunci: IDPS; SQL_Injection; Suricata; Syn_Flood_Attack

Enhancing SQL Injection Attack Prevention: A Framework for Detection, Secure Development, and Intelligent Techniques

SQL injection attacks (SQLIAs) pose increasing threats as more organizations adopt vulnerable web applications and databases. By manipulating queries, SQLIAs access and destroy confidential data. This paper delivers three contributions around improving SQLIA detection research: first, a literature review assessing current detection/prevention systems to produce an SQL injection detection framework; second, specialized deep learning models optimizing session pattern analysis and feature engineering to enhance performance; third, comparing proposed models against previous defenses to surface promising research directions. Results highlight opportunities like real-time systems generalizing across attack variants through emerging techniques. Additionally, with attack complexity rising, systematized SQLIA investigation is warranted. Despite extensive study, current perspectives lack cohesive guidance informing mitigation strategies. Therefore, a framework is proposed holistically mapping knowledge gaps around contemporary SQLIAs, seminal threats in web applications, and security solutions. Furthermore, a multi-faceted framework examines research trends divided into hardening existing apps, detecting attacks on production systems, and integrating secure development practices. Literature suggests comprehensive resilience requires concurrent strength across these areas. Finally, future work remains in integrated frameworks, deep reinforcement learning adoption, automated AI auditing, and differential privacy to advance real-world SQL injection detection and prevention.

Data Leaks Using SQL Injection

The security of digital data is paramount in today's interconnected world. Among the various cyber threats, SQL injection attacks represent a significant menace to the confidentiality, integrity, and availability of sensitive information stored within databases. SQL injection is a technique employed by malicious actors to exploit vulnerabilities in web applications that interact with databases, allowing unauthorized access to or manipulation of the data. This paper presents an in-depth analysis of SQL injection attacks, their mechanisms, and the potential risks they pose to organizational data. It examines various preventive measures and best practices to mitigate the vulnerabilities that lead to SQL injection. Techniques such as input validation, parameterized queries, and the use of prepared statements are explored as effective defences against these attacks

SQL Injection Detection Using RNN Deep Learning Model

SQL injection attacks are a common type of cyber-attack that exploit vulnerabilities in web applications to access databases through malicious SQL queries. These attacks pose a serious threat to the security and integrity of web applications and their data. The existing methods for detecting SQL injection attacks are based on predefined rules that can be easily circumvented by sophisticated attackers. Therefore, there is a need for a more robust and effective method for detecting SQL injection attacks. In this research, we propose a novel method for detecting SQL injection attacks using recurrent neural networks (RNN), which are a type of deep learning model that can capture the syntax and semantic features of SQL queries. We train an RNN model on a dataset of benign and malicious SQL queries, and use it to classify queries as either benign or malicious. We evaluate our method on a benchmark dataset and compare it with the existing rule-based methods. Our experimental results show that our method achieved high accuracy and outperformed the rule-based methods for detecting SQL injection attacks. Our research contributes to the field of web application security by providing a new and effective solution for protecting web applications from SQL injection attacks using deep learning. Our method has both practical and theoretical implications, as it can be easily integrated into existing web application security frameworks to provide an additional layer of protection against SQL injection attacks, and it can also advance the understanding of how deep learning models can be applied to natural language processing tasks such as SQL query analysis.

Analisis dan Penanganan Insiden Siber SQL Injection Menggunakan Kerangka NIST SP 800-61R2 dan Algoritma Klusterisasi K-Means

Based on the OWASP Top Ten document in 2021, attacks or vulnerabilities in an application in the form of injection still rank in the top 3. SQL Injection attacks are still classified as injection vulnerabilities so they need special attention from Information & Communication Technology Managers. Badan Siber dan Sandi Negara (BSSN) has published a document related to preventing SQL Injection attacks. However, the document has not included a cyber attack analysis process that uses the K-Means clustering approach. So in this research, a collaborative method of handling cyber attacks in the form of SQL Injection is proposed using the NIST SP 800-61R2 framework as a fundamental for handling cyber attacks and K-Means clustering. Before analyzing cyber attacks, it is better to use a framework or standardization that applies globally. Based on the research conducted, the K-Means clustering algorithm can help cybersecurity analysts in the process of analyzing cyber attacks that occur. The result of this research is that the optimal value is obtained that cyber attacks in the form of SQL Injection, namely 3 clusters. The hope of the research can facilitate cybersecurity analysts in analyzing cyber attacks that are poured into reports to parties in need

Penerapan OPNsense Sebagai Sistem Keamanan Web Server Menggunakan Metode Host Instrusion Prevention System

This research was conducted to detect and prevent disturbances or intrusions that occur on web servers, because by default the security system on web servers in a network still depends on the administrator, so the security of server really depends on the alertness of an administrator in responding to disturbances that occur on the web server. This research is using experimental method. This research was carried out by implementing OPNsense as a web server security system using Host Intrusion Prevention System method. The experimental results are then documented to carry out analysis so that appropriate recommendations are produced for designing a web server security system using HIPS method. The results of this research show that OPNsense can be used as a Host Intrusion Prevention System for LAN networks to secure web servers. OPNsense can prevent Port Scanning carried out on LAN networks. SQL injection process failed because no ID parameter was found. Apart from that, information is also visible that the web server is protected by WAF/IPS. Metasploit application via eth0 does not have permission to carry out a DOS attack on network devices with the address 192.168.80.200, which is the address of the web server.

DATABASE VULNERABILITY TESTING MEMANFAATKAN SQLMAP PADA UBUNTU 22.04 ( STUDI KASUS : ANTARATECH.NET)

Vulnerability trial of database using SQLMap on Ubuntu 22.04 is an important process to identify security vulnerabilities in database systems running on the Ubuntu 22.04 platform. This study discusses the importance of vulnerability Uji coba in the information security space, with a focus on SQL-based database systems and the Ubuntu 22.04 platform. We describe a vulnerability Uji coba methodology that involves the use of SQLMap, a penetration tool specifically designed to identify SQL injection vulnerabilities. This research covers the basic concepts of SQL injection, the steps of implementing SQLMap on the Ubuntu 22.04 platform, and how to interpret the test results. We also discuss the benefits of using SQLMap, including an in-depth understanding of database vulnerabilities that can help organizations improve the security of their systems. The result of this research is a practical guide to performing database vulnerability testing with SQLMap on the Ubuntu 22.04 platform, thus helping security professionals and system administrators to identify and address potential vulnerabilities that could compromise data integrity and confidentiality. This research makes an important contribution in the effort to maintain the security of database systems in the challenging digital era.

Designing an internet of things laboratory to improve student understanding of secure IoT systems

In response to an alarming shortage of workers in cybersecurity and a growing skills gap, the U.S. Department of Defense is taking steps to build cybersecurity capacity through workforce training and education. In this paper, we present an approach to address this shortage and skills gap through the development of cybersecurity education courseware for internet of things (IoT) applications.To attract students and workers into the field of cybersecurity, it is important to design courseware that is exciting and tied to real-world problems. We describe our design for an embedded systems course taught at the graduate level for engineering and computer science students. The innovation in our approach is to select the fast-growing domain of healthcare and feature different IoT sensors that are seeing increased usage. These include barcode scanners, cameras, fingerprint sensors, and pulse sensors. These devices cover important functions such as patient identification, monitoring, and creating electronic health records. We use a password protected MySQL database as a model for electronic health records. We also demonstrate potential vulnerabilities of these databases to SQL injection attacks.We administered these labs and collected survey data from the students. We found a significant increase in student understanding of cybersecurity issues. The mean confidence level of the students in cybersecurity issues increased from 2.5 to 4.1 on a 5-point scale after taking this course, which represents a 65% increase. The instructional lab material has been uploaded to the web portal https://clark.center designated by the National Security Agency for dissemination. Our approach, design, and experimental validation methodology will be useful for educators, researchers, students, and organizations interested in re-skilling their workforce.

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,

SQLI Attack: An Approach using ML and Hybrid Techniques

Abstract: Web-based systems are significantly at risk from SQL Injection Attacks (SQLIA), particularly in industries that handle sensitive data, like finance and healthcare. During these attacks, hostile actors insert false SQL queries into the database server of a web application in an effort to steal sensitive data. The use of classifiers and techniques like end-to-end deep learning and expanding the Aho-Corasick algorithm to detect SQLIA attacks have been covered in the literature. These researches have shed light on identifying and minimizing SQLIA, but the problem still exists. To detect and prevent SQLIA, a thorough methodology is suggested that combines static and dynamic studies with machine learning and hybrid tactics. The study compares several machine learning algorithms with hybrid techniques, showcasing the hybrid strategy's higher performance in both training and test sets. This method, which provides a practical means to secure web applications, is put out as a potential remedy for the persistent problem of SQLIA. In result, SQL Injection Attacks continue to be a danger, requiring effective ways for detection and prevention. It is highlighted as a promising solution the potential of machine learning and hybrid solutions, notably the hybrid approach. The study emphasizes how crucial it is to follow recommended procedures for protecting online applications from SQLIA attacks.