Dealing With Sql Injection Attacks: Website Security With Hosting Gator

 

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Dealing With Sql Injection Attacks: Website Security With Hosting Gator

Featured articles represent cutting-edge research with significant potential to have a major impact on the field. Featured articles must be substantial original articles that combine several techniques or approaches, provide perspective on future research directions, and explain potential applications of the research.

WordPress Sql Injection Attacks: How To Protect Your Site

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By Dongzhe Lu Dongzhe Lu Scilit Preprints.org Google Scholar View Publication, Jinlong Fei Jinlong Fei Scilit Preprints.org Google Scholar View Publication * and Long Liu Long Liu Scilit Preprints.org Google Scholar View Publication

Original accepted: November 15, 2022. / Resubmissions accepted in 2023.

Sql Injection Prevention: 7 Tested Ways

For many years, injection vulnerabilities have been in the top 10 of the Open Web Application Security Project and are one of the most damaging and widely exploited types of vulnerabilities against web applications. Detecting structured query language (SQL) injection attacks remains a challenging task due to the heterogeneity of attack payloads, diversity of attack methods, and diversity of attack patterns. It has been proven that no single model can guarantee sufficient security to protect web applications, and it is very important to develop effective and accurate models to detect SQL injection attacks. In this paper, we propose synBERT, a semantic learning-based detection model that explicitly combines sentence-level semantic information from SQL statements into embedding vectors. The model learns representations that can be mapped to structures in the SQL syntax tree, as evidenced by visualization work. We collect various datasets to evaluate the classification performance of synBERT, and the results show that our approach outperforms previously proposed models. Even with a new untrained model, accuracy can reach 90% or more, indicating that the model has good generalization performance.

Injection vulnerabilities are specific vulnerabilities caused by imperfections in the developer or design process [1]. With the rapid expansion of Internet infrastructure, the increasing reliance on digital information by more and more users highlights the importance of information and data protection [2]. Data security is defined as the use of hardware or software to prevent unauthorized access, alteration, or destruction of information.

Structured Query Language (SQL) injection is a code injection attack that executes input data as code, thereby violating the principle of data-code separation [3]. An attacker can inject SQL commands into the query string of a web form submission, Uniform Resource Locator (URL), or page request and change the execution logic of SQL statements to access resources or modify data stored in the database when the web application passes. SQL statements to the back-end database for operations without strict filtering of user input parameters, as shown in Figure 1.

SQL injection attacks have become a popular tactic among cyber attackers due to their ease of execution and high threat level. Traditional rule-based detection models are not efficient and effective in detecting various attacks. At the same time, there are many free SQL injection tools on the Internet that reduce the rate of SQL injection attacks, while the shortcomings of development languages, the limited professionalism of some developers, and the lack of awareness of web security increase their likelihood. . successful attack. on the website. Therefore, it is very important to develop a reliable and accurate SQL injection attack detection model for web application security.

Pdf) Injection, Detection, Prevention Of Sql Injection Attacks

In this paper, we present synBERT based on semantic learning and deep learning to detect SQL injection attacks. For the first time, we apply the trained model to the field of vulnerability attack detection and show that it can fully learn sentence-level semantic information. It outperforms other detection algorithms in terms of classification accuracy and can differentiate between SQL injection statements and SQL statements. Contributions to this article are listed below.

We have put together a more comprehensive set of malicious data that covers the full range of SQL injection attacks. Moreover, preferred samples are selected not only from plain text but also from plain SQL statements. This method has the potential to reduce the number of false alarms.

For raw traffic, we detect injection attacks not only in the URL field, but also in the request body and request header fields. This reduces the possibility of underreporting.

We propose a new detection model, synBERT, based on semantic understanding. When compared with other detection models, this model outperforms Convolutional Neural Networks (CNN) [1], Multilayer Perceptron (MLP) [4, 5], Long Short-Term Memory (LSTM) [6, 7], etc. [8, 9]. .

Prevent Sql Injection Vulnerabilities In Php Applications And Fix Them

We use structural investigations to evaluate how well the synBERT model learns sentence-level semantic information and visualizes it with heatmaps and treemaps.

The rest of the article is structured as follows. Section 2 introduces the most commonly used SQL injection attacks and vectorization techniques, as well as the advantages and disadvantages of traditional attack detection methods. Section 3 presents our proposed model and its principles. Section 4 expands on the design and visualization principles of structural probes. Section 5 performs a comprehensive performance test of synBERT and selects a new test set for secondary evaluation. Section 6 concludes this paper and suggests future work.

Structured Query Language Injection Attack (SQLIA) is one of the most important and dangerous vulnerabilities in interactive online applications [3]. Web applications with databases that store sensitive information are one of the targets of SQLIA. According to Miter [10], SQL injection attacks are one of the oldest, most common, and most destructive types of security attacks facing web-based information systems.

The causes of SQL injection can be grouped into two categories: dynamic string construction and unsafe database configuration. The first includes error handling of escape characters, error handling of types, error handling of connection requests, error handling of errors, and error handling of multiple implementations. The latter includes a default pre-installed user that runs as a system user with root or SYSTEM or Administrator privileges and enables many system functions by default.

Sql Injection Cheat Sheet: 8 Best Practices To Prevent Sql Injection

Once summarized, classic SQL injection methods are classified into: Based on the way SQL statements are created and sent, they can be classified as cookie injection, GET injection, POST injection, HTTP (Hyper Text Transfer Protocol) header injection, and second-order injection. According to the performance effect, it can be divided into blind injection and reverse injection, mainly as follows. Boolean based blind injection, time based blind injection, error report based blind injection, compound query injection, heap injection, annotation character injection. , wide byte injection and replication style.

Additionally, SQL injection vulnerabilities are often used in newer, sophisticated attacks such as Fast-Flux SQL Injection and Composite SQL Injection (a combination of SQL injection attacks and other web application attacks). There are many examples.

SQL+ Distributed Denial of Service (DDoS). This attack is used to crash servers and consume resources so that users cannot access them. The commands that can be used in SQL injection to track DDoS attacks are encrypt, compress, link, etc.

SQL+ Domain Name System (DNS) hijacking. By using this type of attack, the attacker intends to inject SQL queries into DNS queries and capture them for distribution on the Internet.

Sql Injection Attacks And Defense: Amazon.co.uk: Clarke Salt, Justin: 9781597494243: Books

SQL+ Cross-Site Scripting (XSS). XSS is a client-side code injection attack in which an attacker can inject malicious code into an application’s input fields. After installing the XSS script, it will run and try to connect to the application database. The code to extract data from the database can be obtained using the iframe command [11].

SQL+ Insufficient authentication. If security parameters are not initialized, attackers can access sensitive content without verifying the user’s identity. Therefore, attackers use this vulnerability to inject SQL injection code.

In general, the damage caused by SQL injection includes the following: (a) An attacker can access database data without permission and steal user privacy and personal information, resulting in user information leakage. (b) Add or delete operations on database data, such as adding or deleting administrator accounts personally. (c) If the website directory has write permissions, an attacker can write a web page Trojan. An attacker can then spoof the web

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