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.

Structured Query Language Injection (SQLi) is a client-side application vulnerability that allows attackers to inject malicious SQL queries with harmful intents, including stealing sensitive information, bypassing authentication, and even executing illegal operations to cause more catastrophic damage to users on the web application. According to OWASP, the top 10 harmful attacks against web applications are SQL Injection attacks. Moreover, based on data reports from the UK's National Fraud Authority, SQL Injection is responsible for 97% of data exposures. Therefore, in order to prevent the SQL Injection attack, detection SQLi system is essential. The contribution of this research is securing web applications by developing a browser extension for Google Chrome using Long Short-Term Memory (LSTM), which is a unique kind of RNN algorithm capable of learning long-term dependencies like SQL Injection attacks. The results of the model will be deployed in static analysis in a browser extension, and the LSTM algorithm will learn to identify the URL that has to be injected into Damn Vulnerable Web Application (DVWA) as a sample-tested web application. Experimental results show that the proposed SQLi detection model based on the LSTM algorithm achieves an accuracy rate of 99.97%, which means that a reliable client-side can effectively detect whether the URL being accessed contains a SQLi attack or not.

Security is undoubtedly the most serious problem for Web applications, and SQL injection (SQLi) attacks are one of the most damaging. The detection of SQL blind injection vulnerability is very important, but unfortunately, it is not fast enough. This is because time-based SQL blind injection lacks web page feedback, so the delay function can only be set artificially to judge whether the injection is successful by observing the response time of the page. However, brute force cracking and binary search methods used in injection require more web requests, resulting in a long time to obtain database information in SQL blind injection. In this paper, a gated recurrent neural network-based SQL blind injection technology is proposed to generate the predictive characters in SQL blind injection. By using the neural language model based on deep learning and character sequence prediction, the method proposed in this paper can learn the regularity of common database information, so that it can predict the next possible character according to the currently obtained database information, and sort it according to probability. In this paper, the training model is evaluated, and experiments are carried out on the shooting range to compare the method used in this paper with sqlmap (the most advanced sqli test automation tool at present). The experimental results show that the method used in this paper is more effective and significant than sqlmap in time-based SQL blind injection. It can obtain the database information of the target site through fewer requests, and run faster.

The Open Web Application Security Project (OWASP) (a non-profit foundation that works to improve computer security) considered, in 2021, injection as one of the biggest risks in web applications. SQL injection despite being a vulnerability easily avoided has a great insurgency in web applications, and its impact is quite nefarious. To identify and exploit vulnerabilities in a system, algorithms based on Swarm Intelligence (SI) can be used. This article proposes and describes a new approach that uses SI and attack vectors to identify Structured Query Language (SQL) Injection vulnerabilities. The results obtained show the efficiency of the proposed approach.

Structured Query Language (SQL) is extensively used for storing, manipulating and retrieving information in the relational database management system. Using SQL statements, attackers will try to gain unauthorized access to databases and launch attacks to modify/retrieve the stored data, such attacks are called as SQL injection attacks. Such SQL Injection (SQLi) attacks tops the list of web application security risks of all the times. Identifying and mitigating the potential SQL attack statements before their execution can prevent SQLi attacks. Various techniques are proposed in the literature to mitigate SQLi attacks. In this paper, a random decision forest approach is introduced to mitigate SQLi attacks. From the experimental results, we can infer that the proposed approach achieves a precision of 97% and an accuracy of 95%.

The advancement of technology in the creation of new tools to solve problems such as information storage generates proportionally developing methods that search for security flaws or breaches that compromise said information. The need to periodically generate security reports on database managers is given by the complexity and number of attacks that can be carried out today. This project seeks to carry out an evaluation of the security of NoSQL database managers. The work methodology is developed according to the order of the objectives, it begins by synthesizing the types of vulnerabilities, attacks and protection schemes limited to MongoDB, Redis and Apache Cassandra. Once established, a prototype of a web system that stores information with a non-relational database will be designed on which a series of attacks defined by a test plan will be applied seeking to add, consult, modify or eliminate information. Finally, a report will be presented that sets out the attacks carried out, the way in which they were applied, the results, possible countermeasures, security advantages and disadvantages for each manager and the conclusions obtained. Thus, it is possible to select which tool is more convenient to use for a person or organization in a particular case. The results showed that MongoDB is more vulnerable to NoSQL injection attacks, Redis is more vulnerable to attacks registered in the CVE and that Cassandra is more complex to use but is less vulnerable.

The heterogeneous massive logs incoming from multiple sources pose major challenges to professionals responsible for IT security and system administrator. One of the challenges is to develop a scalable heterogeneous logs database for storage and further analysis. In fact, it is difficult to decide which database is suitable for the needs, the best of a use case, execution time and storage performances. In this paper, we explore, study, and compare the performance of SQL and NoSQL databases on large heterogeneous event logs. We implement the relational database using MySQL, the column-oriented database using Impala on the top of Hadoop, and the graph database using Neo4j. We experiment the databases on a large heterogeneous logs and provide advice, the pros and cons of each SQL and NoSQL database. Our findings that Impala outperforms MySQL and Neo4j databases in terms of loading logs, execution time of simple queries, and storage of logs. However, Neo4j outperforms Impala and MySQL in the execution time of complex queries.

The power grid has high requirements for data security, and data security audit technology is facing challenges. Because the server in the power grid operating environment is considered untrustworthy and does not have the authority to obtain the secret key, the encrypted data cannot be parsed and the data processing ability of the data center is restricted. In response to the above problems, the power grid database encryption system was designed, and the access control module and the encryption module that should be written based on SQL statements were explained. The database encryption system was developed using the Java language and deployed in the cloud environment. Finally, the method was proved by experiments. feasibility.

Web application vulnerability is one of the major causes of cyber attacks. Cyber criminals exploit these vulnerabilities to inject malicious commands to the unsanitized user input in order to bypass authentication of the database through some cyber-attack techniques like cross site scripting (XSS), phishing, Structured Query Language Injection Attack (SQLIA), malware etc., Although many research works have been conducted to resolve the above mentioned attacks, only few challenges with respect to SQLIA could be resolved. Ensuring security against complete set of malicious payloads are extremely complicated and demanding. It requires appropriate classification of legitimate and injected SQL commands. The existing approaches dealt with limited set of signatures, keywords and symbols of SQL queries to identify the injected queries. This work focuses on extracting SQL injection patterns with the help of existing parsing and tagging techniques. Pattern-based tags are trained and modeled using Multi-layer Perceptron which significantly performs well in classification of queries with accuracy of 94.4% which is better than the existing approaches.

Natural Language Processing is being used in every field of human to machine interaction. Database queries although have a confined set of instructions, but still found to be complex and dedicated human resources are required to write, test, optimize and execute structured query language statements. This makes it difficult, time-consuming and many a time inaccurate too. Such difficulties can be overcome if the queries are formed dynamically with standard procedures. In this work, parsing, lexical analysis, synonym detection and formation processes of the natural language processing are being proposed to be used for dynamically generating SQL queries and optimization of them for fast processing with high accuracy. NLP parsing of the user inputted text for retrieving, creation and insertion of data are being proposed to be created dynamically from English text inputs. This will help users of the system to generate reports from the data as per the requirement without the complexities of SQL. The proposed system will not only generate queries dynamically but will also provide high accuracy and performance.

Reported vulnerabilities have grown significantly over the recent years, with SQL injection (SQLi) being one of the most prominent, especially in web applications. For these, such increase can be explained by the integration of multiple software parts (e.g., various plugins and modules), often developed by different organizations, composing thus web application variants. Machine Learning has the potential to be a great ally on finding vulnerabilities, aiding experts by reducing the search space or even by classifying programs on their own. However, previous work usually does not consider SQLi or utilizes techniques hard to scale. Moreover, there is a clear gap in vulnerability detection with machine learning for PHP, the most popular server-side language for web applications. This paper presents a Deep Learning model able to classify PHP slices as vulnerable (or not) to SQLi. As slices can belong to any variant, we propose the use of an intermediate language to represent the slices and interpret them as text, resorting to well-studied Natural Language Processing (NLP) techniques. Preliminary results of the use of the model show that it can discover SQLi, helping programmers and precluding attacks that would eventually cost a lot to repair.

Now-a-days web applications are everywhere. Usually these applications are developed by database program which are often written in popular host programming languages such as C, C++, C\#, Java, etc., with embedded Structured Query Language (SQL). These applications are used to access and process crucial data with the help of Database Management System (DBMS). Preserving the sensitive data from any kind of attacks is one of the prime factors that needs to be maintained by the web applications. The SQL injection attacks is one of the important security threat for the web applications. In this paper, we propose a code-based analysis approach to automatically detect and prevent the possible SQL Injection Attacks (SQLIA) in a query before submitting it to the underlying database. This approach analyses the user input by assigning a complex number to each input element. It has two part (i) input clustering and (ii) safe (non-malicious) input identification. We provide a details discussion of the proposal w.r.t the literature on security and execution overhead point of view.

It is critical to secure the Industrial Internet of Things (IIoT) devices because of potentially devastating consequences in case of an attack. Machine learning (ML) and big data analytics are the two powerful leverages for analyzing and securing the Internet of Things (IoT) technology. By extension, these techniques can help improve the security of the IIoT systems as well. In this paper, we first present common IIoT protocols and their associated vulnerabilities. Then, we run a cyber-vulnerability assessment and discuss the utilization of ML in countering these susceptibilities. Following that, a literature review of the available intrusion detection solutions using ML models is presented. Finally, we discuss our case study, which includes details of a real-world testbed that we have built to conduct cyber-attacks and to design an intrusion detection system (IDS). We deploy backdoor, command injection, and Structured Query Language (SQL) injection attacks against the system and demonstrate how a ML-based anomaly detection system can perform well in detecting these attacks. We have evaluated the performance through representative metrics to have a fair point of view on the effectiveness of the methods.

One of the challenges in supplying the communities with wider access to scientific databases is the need for knowledge of database languages like Structured Query Language (SQL). Although the SQL language has been published in many forms, not everybody is able to write SQL queries. Another challenge is that it might not be practical to make the public aware of the structure of databases. There is a need for novice users to query relational databases using their natural language. To solve this problem, many natural language interfaces to structured databases have been developed. The goal is to provide a more intuitive method for generating database queries and delivering responses. Through social media, which makes it possible to interact with a wide section of the population, and with the help of natural language processing, researchers at the Atmospheric Radiation Measurement (ARM) Data Center at Oak Ridge National Laboratory (ORNL) have developed a concept to enable easy search and retrieval of data from several environmental data centers for the scientific community through social media.Using a machine learning framework that maps natural language text to thousands of datasets, instruments, variables, and data streams, the prototype system would allow users to request data through Twitter and receive a link (via tweet) to applicable data results on the project's search catalog tailored to their key words. This automated identification of relevant data from various petascale archives at ORNL could increase convenience, access, and use of the project's data by the broader community. In this paper we discuss how some data-intensive projects at ORNL are using innovative ways to help in data discovery.

This paper present an approach to automate the conversion of Natural Language Query to SQL Query effectively. Structured Query Language is a powerful tool for managing data held in a relational database management system. To retrieve or manage data user have to enter the correct SQL Query. But the users who don't have any knowledge about SQL are unable to retrieve the required data. To overcome this we proposed a model in Natural Language Processing for converting the Natural Language Query to SQL query. This helps novice user to get required content without knowing any complex details about SQL. This system can also deal with complex queries. This system is designed for Training and Placement cell officers who work on student database but don't have any knowledge about SQL. In this system, user can also enter the query using speech. System will convert speech into the text format. This query will get transformed to SQL query. System will execute the query and gives output to the user.

Computer and network accountability is to make every action in computers and networks accountable. In order to achieve accountability, we need to answer the following questions: what did it happen? When did it happen? Who did it? In order to achieve accountability, the first step is to record what exactly happened. Therefore, an accountable logging is needed and implemented in computers and networks. Our previous work proposed a novel accountable logging methodology called Flow-Net. However, how to storage the huge amount of Flow-net logs into databases is not clear. In this paper, we try to answer this question.

Despite the wide of range of research and technologies that deal with the problem of routing in computer networks, there remains a gap between the level of network hardware administration and the level of business requirements and constraints. Not much has been accomplished in literature in order to have a direct enforcement of such requirements on the network. This paper presents a new solution in specifying and directly enforcing security policies to control the routing configuration in a software-defined network by using Row-Level Security checks which enable fine-grained security policies on individual rows in database tables. We show, as a first step, how a specific class of such policies, namely multilevel security policies, can be enforced on a database-defined network, which presents an abstraction of a network's configuration as a set of database tables. We show that such policies can be used to control the flow of data in the network either in an upward or downward manner.

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.

Internet users are increasing day by day. The web services and mobile web applications or desktop web application's demands are also increasing. The chances of a system being hacked are also increasing. All web applications maintain data at the backend database from which results are retrieved. As web applications can be accessed from anywhere all around the world which must be available to all the users of the web application. SQL injection attack is nowadays one of the topmost threats for security of web applications. By using SQL injection attackers can steal confidential information. In this paper, the SQL injection attack detection method by removing the parameter values of the SQL query is discussed and results are presented.

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.

Figuring innovations and development of web diminishes the exertion required for different procedures. Among them the most profited businesses are electronic frameworks, managing an account, showcasing, web based business and so on. This framework mostly includes the data trades ceaselessly starting with one host then onto the next. Amid this move there are such a variety of spots where the secrecy of the information and client gets loosed. Ordinarily the zone where there is greater likelihood of assault event is known as defenceless zones. Electronic framework association is one of such place where numerous clients performs there undertaking as indicated by the benefits allotted to them by the director. Here the aggressor makes the utilization of open ranges, for example, login or some different spots from where the noxious script is embedded into the framework. This scripts points towards trading off the security imperatives intended for the framework. Few of them identified with clients embedded scripts towards web communications are SQL infusion and cross webpage scripting (XSS). Such assaults must be distinguished and evacuated before they have an effect on the security and classification of the information. Amid the most recent couple of years different arrangements have been incorporated to the framework for making such security issues settled on time. Input approvals is one of the notable fields however experiences the issue of execution drops and constrained coordinating. Some other component, for example, disinfection and polluting will create high false report demonstrating the misclassified designs. At the center, both include string assessment and change investigation towards un-trusted hotspots for totally deciphering the effect and profundity of the assault. This work proposes an enhanced lead based assault discovery with specifically message fields for viably identifying the malevolent scripts. The work obstructs the ordinary access for malignant so- rce utilizing and hearty manage coordinating through unified vault which routinely gets refreshed. At the underlying level of assessment, the work appears to give a solid base to further research.

NoSQL databases have gained a lot of popularity over the last few years. They are now used in many new system implementations that work with vast amounts of data. This data will typically also include sensitive information that needs to be secured. NoSQL databases are also underlying a number of cloud implementations which are increasingly being used to store sensitive information by various organisations. This has made NoSQL databases a new target for hackers and other state sponsored actors. Forensic examinations of compromised systems will need to be conducted to determine what exactly transpired and who was responsible. This paper examines specifically if NoSQL databases have security features that leave relevant traces so that accurate forensic attribution can be conducted. The seeming lack of default security measures such as access control and logging has prompted this examination. A survey into the top ranked NoSQL databases was conducted to establish what authentication and authorisation features are available. Additionally the provided logging mechanisms were also examined since access control without any auditing would not aid forensic attribution tremendously. Some of the surveyed NoSQL databases do not provide adequate access control mechanisms and logging features that leave relevant traces to allow forensic attribution to be done using those. The other surveyed NoSQL databases did provide adequate mechanisms and logging traces for forensic attribution, but they are not enabled or configured by default. This means that in many cases they might not be available, leading to insufficient information to perform accurate forensic attribution even on those databases.

The Web today is a growing universe of pages and applications teeming with interactive content. The security of such applications is of the utmost importance, as exploits can have a devastating impact on personal and economic levels. The number one programming language in Web applications is PHP, powering more than 80% of the top ten million websites. Yet it was not designed with security in mind and, today, bears a patchwork of fixes and inconsistently designed functions with often unexpected and hardly predictable behavior that typically yield a large attack surface. Consequently, it is prone to different types of vulnerabilities, such as SQL Injection or Cross-Site Scripting. In this paper, we present an interprocedural analysis technique for PHP applications based on code property graphs that scales well to large amounts of code and is highly adaptable in its nature. We implement our prototype using the latest features of PHP 7, leverage an efficient graph database to store code property graphs for PHP, and subsequently identify different types of Web application vulnerabilities by means of programmable graph traversals. We show the efficacy and the scalability of our approach by reporting on an analysis of 1,854 popular open-source projects, comprising almost 80 million lines of code.

Modern databases contain an enormous amount of information stored in a structured format. This information is processed to acquire knowledge. However, the process of information extraction from a Database System is cumbersome for non-expert users as it requires an extensive knowledge of DBMS languages. Therefore, an inevitable need arises to bridge the gap between user requirements and the provision of a simple information retrieval system whereby the role of a specialized Database Administrator is annulled. In this paper, we propose a methodology for building an Intelligent Querying System (IQS) by which a user can fire queries in his own (natural) language. The system first parses the input sentences and then generates SQL queries from the natural language expressions of the input. These queries are in turn mapped with the desired information to generate the required output. Hence, it makes the information retrieval process simple, effective and reliable.

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.