Biblio

A huge amount of stored and transferred data is expanding rapidly. Therefore, managing and securing the big volume of diverse applications should have a high priority. However, Structured Query Language Injection Attack (SQLIA) is one of the most common dangerous threats in the world. Therefore, a large number of approaches and models have been presented to mitigate, detect or prevent SQL injection attack but it is still alive. Most of old and current models are created based on static, dynamic, hybrid or machine learning techniques. However, SQL injection attack still represents the highest risk in the trend of web application security risks based on several recent studies in 2021. In this paper, we present a review of the latest research dealing with SQL injection attack and its types, and demonstrating several types of most recent and current techniques, models and approaches which are used in mitigating, detecting or preventing this type of dangerous attack. Then, we explain the weaknesses and highlight the critical points missing in these techniques. As a result, we still need more efforts to make a real, novel and comprehensive solution to be able to cover all kinds of malicious SQL commands. At the end, we provide significant guidelines to follow in order to mitigate such kind of attack, and we strongly believe that these tips will help developers, decision makers, researchers and even governments to innovate solutions in the future research to stop SQLIA.

With the rapid development of Internet technology, the era of big data is coming. SQL injection attack is the most common and the most dangerous threat to database. This paper studies the working mode and workflow of the GreenSQL database firewall. Based on the analysis of the characteristics and patterns of SQL injection attack command, the input model of GreenSQL learning is optimized by constructing the patterned input and optimized whitelist. The research method can improve the learning efficiency of GreenSQL and intercept samples in IPS mode, so as to effectively maintain the security of background database.

Cloud computing has a major role in the development of commercial systems. It enables companies like Microsoft, Amazon, IBM and Google to deliver their services on a large scale to its users. A cloud service provider manages cloud computing based services and applications. For any organization a cloud service provider (CSP) is an entity which works within it. So it suffers from vulnerabilities associated with organization, including internal and external attacks. So its challenge to organization to secure a cloud service provider while providing quality of service. Attribute based encryption can be used to provide data security with Key policy attribute based encryption (KP-ABE) or ciphertext policy attribute based encryption (CP-ABE). But these schemes has lack of scalability and flexibility. Hierarchical CP-ABE scheme is proposed here to provide fine grained access control. Data security is achieved using encryption, authentication and authorization mechanisms. Attribute key generation is proposed for implementing authorization of users. The proposed system is prevented by SQL Injection attack.

In today's world web applications have become an instant means for information broadcasting. At present, man has become so dependent on web applications that everything done through electronic means like e-banking, e-shopping, online payment of bills etc. Due to an unauthorized admittance might threat customer's or user's confidentiality, integrity and authority. SQL injection considered as most Spartan dangerous coercions to the databases of web applications. current scenario databases are highly susceptible to SQL Injection[4] . SQL Injection is one of the most popular and dangerous hacking or cracking technique . In this work authors projected a novel approach to mitigate SQL Injection Attacks in a database. We have illustrated a technique or method prevent SQLIA by incorporating a hybrid encryption in form of Advanced Encryption Standard (AES) and Elliptical Curve Cryptography (ECC) [5]. In this research paper integrated approach of encryption method is followed to prevent the databases of the web applications against SQL Injection Attack. Incidentally if an invader gains access to the database, then it can cause severe damage and ends up with retrieves data or information. So to prevent these type of attacks a combined approach is projected , Advanced Encryption Standard (AES) at login phase to prevent the unauthorized access to databases and on the other hand Elliptical Curve Cryptography (ECC) to encode the database so that without the key no one can access the database information [3]. This research paper illustrates the technique to prevent SQL Injection Attack.

This SQL injection attack is one of the common means for hackers to attack database. With the development of B/S mode application development, more and more programmers use this mode to write applications. However, due to the uneven level and experience of programmers, a considerable number of programmers do not judge the legitimacy of user input data when writing code, which makes the application security risks. Users can submit a database query code and get some data they want to know according to the results of the program. SQL injection attack belongs to one of the means of database security attack. It can be effectively protected by database security protection technology. This paper introduces the principle of SQL injection, the main form of SQL injection attack, the types of injection attack, and how to prevent SQL injection. Discussed and illustrated with examples.

SQL injection attacks are a kind of the greatest security risks on Web applications. Much research has been done to detect SQL injection attacks by rule matching and syntax tree. However, due to the complexity and variety of SQL injection vulnerabilities, these approaches fail to detect unknown and variable SQL injection attacks. In this paper, we propose a model, ATTAR, to detect SQL injection attacks using grammar pattern recognition and access behavior mining. The most important idea of our model is to extract and analyze features of SQL injection attacks in Web access logs. To achieve this goal, we first extract and customize Web access log fields from Web applications. Then we design a grammar pattern recognizer and an access behavior miner to obtain the grammatical and behavioral features of SQL injection attacks, respectively. Finally, based on two feature sets, machine learning algorithms, e.g., Naive Bayesian, SVM, ID3, Random Forest, and K-means, are used to train and detect our model. We evaluated our model on these two feature sets, and the results show that the proposed model can effectively detect SQL injection attacks with lower false negative rate and false positive rate. In addition, comparing the accuracy of our model based on different algorithms, ID3 and Random Forest have a better ability to detect various kinds of SQL injection attacks.

Since radio frequency identification (RFID) technology has been used in various scenarios such as supply chain, access control system and credit card, tremendous efforts have been made to improve the authentication between tags and readers to prevent potential attacks. Though effective in certain circumstances, these existing methods usually require a server to maintain a database of identity related information for every tag, which makes the system vulnerable to the SQL injection attack and not suitable for distributed environment. To address these problems, we now propose a novel blockchain-based mutual authentication security protocol. In this new scheme, there is no need for the trusted third parties to provide security and privacy for the system. Authentication is executed as an unmodifiable transaction based on blockchain rather than database, which applies to distributed RFID systems with high security demand and relatively low real-time requirement. Analysis shows that our protocol is logically correct and can prevent multiple attacks.

SQL injection is well known a method of executing SQL queries and retrieving sensitive information from a website connected database. This process poses a threat to those applications which are poorly coded in the today's world. SQL is considered as one of the top 10 vulnerabilities even in 2018. To keep a track of the vulnerabilities that each of the websites are facing, we employ a tool called Acunetix which allows us to find the vulnerabilities of a specific website. This tool also suggests measures on how to ensure preventive measures. Using this implementation, we discover vulnerabilities in an actual website. Such a real-world implementation would be useful for instructional use in a foundational cybersecurity course.

Emerging computing relies heavily on secure backend storage for the massive size of big data originating from the Internet of Things (IoT) smart devices to the Cloud-hosted web applications. Structured Query Language (SQL) Injection Attack (SQLIA) remains an intruder's exploit of choice to pilfer confidential data from the back-end database with damaging ramifications. The existing approaches were all before the new emerging computing in the context of the Internet big data mining and as such will lack the ability to cope with new signatures concealed in a large volume of web requests over time. Also, these existing approaches were strings lookup approaches aimed at on-premise application domain boundary, not applicable to roaming Cloud-hosted services' edge Software-Defined Network (SDN) to application endpoints with large web request hits. Using a Machine Learning (ML) approach provides scalable big data mining for SQLIA detection and prevention. Unfortunately, the absence of corpus to train a classifier is an issue well known in SQLIA research in applying Artificial Intelligence (AI) techniques. This paper presents an application context pattern-driven corpus to train a supervised learning model. The model is trained with ML algorithms of Two-Class Support Vector Machine (TC SVM) and Two-Class Logistic Regression (TC LR) implemented on Microsoft Azure Machine Learning (MAML) studio to mitigate SQLIA. This scheme presented here, then forms the subject of the empirical evaluation in Receiver Operating Characteristic (ROC) curve.

SQL injection attack (SQLIA) pose a serious security threat to the database driven web applications. This kind of attack gives attackers easily access to the application's underlying database and to the potentially sensitive information these databases contain. A hacker through specifically designed input, can access content of the database that cannot otherwise be able to do so. This is usually done by altering SQL statements that are used within web applications. Due to importance of security of web applications, researchers have studied SQLIA detection and prevention extensively and have developed various methods. In this research, after reviewing the existing research in this field, we present a new hybrid method to reduce the vulnerability of the web applications. Our method is specifically designed to detect and prevent SQLIA. Our proposed method is consists of three phases namely, the database design, implementation, and at the common gateway interface (CGI). Details of our approach along with its pros and cons are discussed in detail.

We present D-ForenRIA, a distributed forensic tool to automatically reconstruct user-sessions in Rich Internet Applications (RIAs), using solely the full HTTP traces of the sessions as input. D-ForenRIA recovers automatically each browser state, reconstructs the DOMs and re-creates screenshots of what was displayed to the user. The tool also recovers every action taken by the user on each state, including the user-input data. Our application domain is security forensics, where sometimes months-old sessions must be quickly reconstructed for immediate inspection. We will demonstrate our tool on a series of RIAs, including a vulnerable banking application created by IBM Security for testing purposes. In that case study, the attacker visits the vulnerable web site, and exploits several vulnerabilities (SQL-injections, XSS...) to gain access to private information and to perform unauthorized transactions. D-ForenRIA can reconstruct the session, including screenshots of all pages seen by the hacker, DOM of each page and the steps taken for unauthorized login and the inputs hacker exploited for the SQL-injection attack. D-ForenRIA is made efficient by applying advanced reconstruction techniques and by using several browsers concurrently to speed up the reconstruction process. Although we developed D-ForenRIA in the context of security forensics, the tool can also be useful in other contexts such as aided RIAs debugging and automated RIAs scanning.

After more than a decade of research, web application security continues to be a challenge and the backend database the most appetizing target. The paper proposes preventing injection attacks against the database management system (DBMS) behind web applications by embedding protections in the DBMS itself. The motivation is twofold. First, the approach of embedding protections in operating systems and applications running on top of them has been effective to protect this software. Second, there is a semantic mismatch between how SQL queries are believed to be executed by the DBMS and how they are actually executed, leading to subtle vulnerabilities in prevention mechanisms. The approach – SEPTIC – was implemented in MySQL and evaluated experimentally with web applications written in PHP and Java/Spring. In the evaluation SEPTIC has shown neither false negatives nor false positives, on the contrary of alternative approaches, causing also a low performance overhead in the order of 2.2%.

In this paper, we have mentioned a method to find the performance of projectwhich detects various web - attacks. The project is capable to identifying and preventing attacks like SQL Injection, Cross – Site Scripting, URL rewriting, Web server 400 error code etc. The performance of system is detected using the system attributes that are mentioned in this paper. This is also used to determine efficiency of the system.

As web applications is becoming more prominent due to the ubiquity of web services, web applications have become main targets for attackers. In order to steal or leak sensitive user data managed by web applications, attackers exploit a wide range of input validation vulnerabilities such as SQL injection, path traversal (or directory traversal), cross-site scripting (XSS), etc. This paper propose a technique that can verify input values of Java-based web applications using static bytecode instrumentation and runtime input validation. The technique searches for target methods or object constructors in compiled Java class files, and statically inserts bytecode modules. At runtime, the instrumented bytecode modules validate input values of the targets, and take countermeasure against malicious inputs. The proposed technique can mitigate the input validation vulnerabilities in Java-based web applications without source codes. To evaluate the effectiveness of the proposed technique, experiments are carried out with an insecure web application maintained by OWASP WebGoat Project. The experimental results show that the proposed technique successfully mitigates input validation vulnerabilities such as SQL injection and path traversal.

Security testing is a pivotal activity in engineering secure software. It consists of two phases: generating attack inputs to test the system, and assessing whether test executions expose any vulnerabilities. The latter phase is known as the security oracle problem. In this work, we present SOFIA, a Security Oracle for SQL-Injection Vulnerabilities. SOFIA is programming-language and source-code independent, and can be used with various attack generation tools. Moreover, because it does not rely on known attacks for learning, SOFIA is meant to also detect types of SQLi attacks that might be unknown at learning time. The oracle challenge is recast as a one-class classification problem where we learn to characterise legitimate SQL statements to accurately distinguish them from SQLi attack statements. We have carried out an experimental validation on six applications, among which two are large and widely-used. SOFIA was used to detect real SQLi vulnerabilities with inputs generated by three attack generation tools. The obtained results show that SOFIA is computationally fast and achieves a recall rate of 100% (i.e., missing no attacks) with a low false positive rate (0.6%).

Web application security has become crucially vital these days. Earlier "default allow" model was used to secure web applications but it was unable to secure web applications against plethora of attacks [1]. In contrast, more restricted security to the web applications is provided by default deny model which at first, builds a model for the particular application and then permits merely those requests that conform to that model while ignoring everything else. Besides this, a novel and effective methodology is followed that allows to analyze the validity of application requests and further results in the generation of semi structured XML cases for the web applications. Furthermore, mature and resilient XML cases are generated by employing learning techniques. This system will further be gauged by examining that XML file containing cases are in correct accordance with the XML format or not. Moreover, the distinction between malicious and non-malicious traffic is carried out carefully. Results have proved its efficacy of rule generation employing access traffic log of cross site scripting (XSS), SQL injection, HTTP Request Splitting, HTTP response splitting and Buffer overflow attacks.

Web applications are used on a large scale worldwide, which handles sensitive personal data of users. With web application that maintains data ranging from as simple as telephone number to as important as bank account information, security is a prime point of concern. With hackers aimed to breakthrough this security using various attacks, we are focusing on SQL injection attacks and XSS attacks. SQL injection attack is very common attack that manipulates the data passing through web application to the database servers through web servers in such a way that it alters or reveals database contents. While Cross Site Scripting (XSS) attacks focuses more on view of the web application and tries to trick users that leads to security breach. We are considering three tier web applications with static and dynamic behavior, for security. Static and dynamic mapping model is created to detect anomalies in the class of SQL Injection and XSS attacks.

Since the past 20 years the uses of web in daily life is increasing and becoming trend now. As the use of the web is increasing, the use of web application is also increasing. Apparently most of the web application exists up to today have some vulnerability that could be exploited by unauthorized person. Some of well-known web application vulnerabilities are Structured Query Language (SQL) Injection, Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF). By compromising with these web application vulnerabilities, the system cracker can gain information about the user and lead to the reputation of the respective organization. Usually the developers of web applications did not realize that their web applications have vulnerabilities. They only realize them when there is an attack or manipulation of their code by someone. This is normal as in a web application, there are thousands of lines of code, therefore it is not easy to detect if there are some loopholes. Nowadays as the hacking tools and hacking tutorials are easier to get, lots of new hackers are born. Even though SQL injection is very easy to protect against, there are still large numbers of the system on the internet are vulnerable to this type of attack because there will be a few subtle condition that can go undetected. Therefore, in this paper we propose a detection model for detecting and recognizing the web vulnerability which is; SQL Injection based on the defined and identified criteria. In addition, the proposed detection model will be able to generate a report regarding the vulnerability level of the web application. As the consequence, the proposed detection model should be able to decrease the possibility of the SQL Injection attack that can be launch onto the web application.